Imperatively Hidden Object

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Imperatively Hidden Objects

The Pivotal Role, which Imperatively Hidden Elements (IHE) Play in Correct Feature Selection (FS) to Properly Train Supervised Machine Learning (ML) Algorithms to Better Predict, Model and Understand Lipid-Aging and Many Other Phenomena.

Long Title:Is proper, correct and exhaustive feature selection for training machine learning algorithms already possible even before all imperatively hidden objects/factors/dimensions, which are required for correctly conceptualizing aging and many other complex phenomena, are fully discovered?
Short Title:Is proper feature selection possible before all imperatively hidden objects, which are required for conceptualizing aging adequately, are fully discovered?
Topic:About imperatively hidden objects and the need for new concept discoveries to select all necessary features required to fully understand aging, immigration and other phenomena.
Author:Thomas Hahn & Daniel Wuttke

Abstract

How can the inherent challenges posed by hidden objects be adequately addressed and eventually overcome?

What are the challenges in predicting relevant features? - Logic or obscure? - If logic, are scientists already aware of it? - But if they were, they would let a computer select the features in a much less biased and much more systematic way than people are capable of. But since I have not seen anybody doing that, I believe, we could drastically accelerate our discovery process by making researchers aware of the advantages to hand over feature selection from observation bias people to much less bias and much more systematic artificial intelligence (AI).

Introduction

Humans are very biased in choosing their method of conducting experimental measurements or make observations without being aware of it. What percentage of the entire electromagnetic wave spectrum can we perceive? No more than 5% for sure. But the changes, of which we must be aware, before we can understand aging, are most likely much more distinct outside our narrow sensory window because our sensory limitations did not affect the evolution of aging in any way.

For example, humans can only hear part of the sound an elephant makes because humans cannot hear such low frequencies as the elephant can. This tends to prevent the full understanding of the elephant’s communication options. Humans cannot distinguish such low sound frequencies from background noise, i.e. environment, because they cannot perceive the low elephant sound frequencies from being different from the background environment. But without considering those imperatively hidden factors we cannot fully understand elephant communication. Therefore, humans tend to miss cellular processes, which can only be distinguished from background noise outside the electromagnetic wavelength interval, for which humans have evolved sensory organs, i.e. eyes, ears and skin. The mechanism by which the tongue and nose operate is of an entirely different dimension because they cannot sense any wavelength.

For example, before magnets were discovered, they remained for us an imperatively hidden object because we could not even suspect them in any way. But still, just because we lack any senses for perceiving any kind of magnetism does not stop it from affecting our lives. Only after we discovered the consequences of the forces, which the magnetic field has on some metals, prompted us to search outside the limited window, within which we can sense differences in wave length. Magnetic fields could affect life in many positive ways because they are used to treat major depressive disorder and cause involuntary muscle contraction. But has anybody even thought of measuring the magnetic field of a cell or brain, which I expect to be strong enough for us to measure with sensitive devices? Since any electric current causes a perpendicular radiating magnetic field, it can be hypothesized that the weak magnetic field is pulse-like and depends on the temporal pattern by which neurons fire action potentials. The changes in the magnetic field of a cell is expected to be enriched for the cellular component membrane because they have proton pumps and maintain an electric gradient to produce ATP. But what if changes in this magnetic field are causing us to age? Then we could stop the aging process by any intervention, which sets our cellular magnetic field pattern back to its youthful benchmark.

I suspect that the reason for our only rudimentary understanding of the aging process is caused by us missing such kind of imperatively hidden objects, which are required for making the essential key observations without which aging cannot be fully explained. I view a magnetic field as a concept, which exists, regardless weather we are aware of it. There may be many more other hidden concepts, which we must develop correctly, before we can reverse aging.

Analogies to aid in the understanding of the concept of Imperatively Hidden Objects (IHO)

Let’s say that an immortal interstellar alien highly intelligent out-of-space critter has landed on Earth. Let’s imagine that he can only perceive wave lengths within the limits of the magnetic field. Then we humans would not even notice this out of space interstellar visitor because he/she remains an imperatively hidden object (IHO) that we cannot even suspect. Let’s say this interstellar species has not evolved a body or anything to which our senses are sensitive. Let’s say that this life can be fully defined by irregularities within the magnetic field. But this interstellar critter can perceive us humans because our magnetic field disrupt the homogeneity of the background environment and must therefore be something other than background noise. Let’s say that this immortal interstellar critter can perceive and process all the magnetic fields on Earth. Could he maybe develop the concept of siblings or parents on its own? Is the magnetic field of relatives more similar to each other than expected by chance? It is very likely because humans vary a lot in their neuronal wiring architecture. Hence, each human could be defined by the pattern of his/her action potentials. This inevitably causes a very weak unique perpendicularly acting electromagnetic field that cannot be detected by our instruments. Therefore, instead of humans, we should use the giant squid as model organism to understand the relationships between life, aging and changes in magnetic field because it has the thickest neuron. Therefore, it must fire stronger action potentials than our human neurons. This will inevitably cause a stronger perpendicularly acting electromagnetic field, which may be strong enough to be detected by our instruments.

Let’s say that this interstellar critter wants to use machine learning to predict the risk of any particular university student in the USA for having to return home after graduation because they lost their immigration status and could not find a job, which would have made them eligible for one year OPT (Optional Practical Training). Let’s say that this interstellar critter has no concept of aging and that his most important goal is to develop a classifier by developing a new machine learning algorithm, which can predict in advance the risk that any particular student is facing to no longer been allowed to reside in the United States. Let’s say that accomplishing this objective has the same meaning and importance to this critter as for us the cure of aging and elimination of death.

What should he do? He cannot talk. No human even suspects him. He could start using supervised machine learning by observing thousands of students to find out what those students share, who are forced to leave, or what they lack compared to citizens, who are always welcome here.

I hypothesize that no matter how clever and sensitive to irregular interruption of the homogenous electromagnetic field, which is the only dimension, in which he can sense the presence of humans and any other form of life, he has no chance to understand the risk factors for being forced to leave America after graduation, because they are an imperatively hidden concepts (IHC) to this critter, because he cannot even suspect them in any way. However, without developing the right concepts in advance, this critter can never the discover risk factors for having to leave the USA after graduation.

The same applies to aging. We are still missing essential concepts without which we cannot fully understand it. But even if somebody by chance could detect the magnetic irregularities caused by this foreign interstellar critter, he/she could never suspect that it is highly intelligent.

This means that even if we measured a cell across the entire wavelength spectrum and could clearly detect its presence we would never suspect it to have any kind of intelligence because we would consider the anomalies in the magnetic field as background noise. Our visiting interstellar critter has a similar problem. He cannot develop the essential concepts without which he could never develop a machine learning algorithm to predict all the correct risk factors, which impair the chances for somebody to be allowed to keep residing in the US while not full time enrolled. As long as this critter has no concept of “country”, e.g. the USA, he has absolutely no chance to discover nationalities because even if he could figure out the nationality of everyone, it would make no sense to him. But words like “American” “German”, “French” or “Indian” cannot make any sense to this critter as long as the concept of “country” remains an imperatively hidden object for him. How can somebody be considered “German” or “American” as long as the concept of Germany or USA are still lacking? One can only be German if Germany exists. Without at least suspecting the concept of a country, e.g. Germany, there is absolutely no way to discover the required concept of citizenship. But without determining the feature “citizenship” no machine learning algorithm could learn to make correct predictions. .The same applies to aging. We are still lacking so many essential concepts without which aging can never be understood

For example, as long as the concept of a ribosome is lacking, we have no way of understanding the changes in the relative abundance ratio of mRNA and proteins. We may have some initially success with building a model to predict protein abundance and concentration because it is about 70% similar to the transcriptome. However, according to Janssens et al (2015) [1], this similarity declines with age and is a driver of replicative aging in yeast.

But no matter how many training samples we use to train our predictor, it must fail, unless we have developed a mental concept of a ribosome. I believe we face a similar predicament with understanding the causes and regulation of epigenetic changes over time with advancing age, despite being able to measuring them so clearly that we can use them to determine the biological age. But unfortunately, as long as we lack any concept, by which epigenetic changes could be connected to other cellular processes, we cannot understand how epigenetic changes are regulated.

Before we could correctly conceptualize the role and scope of the ribosome we had no way to explain the mechanisms by which mRNA and protein abundance are linked. But even after we conceptualized the role of the ribosome correctly any machine learning algorithm to predict protein concentration would inevitably fail as long as we lack the correct concept of the poly-AAA-tail. Similarly, there are still lots of imperatively hidden concepts, factors, dimensions or objects, which we cannot suspect because we cannot perceive them, which prevent us from fully understanding aging. However, the fact that our current observations fail to fully explain aging, indicate the presence of imperatively hidden factors of which we can see the consequences without being able to detect them. But since every consequence must have a cause, any unexplained consequence indicates the presence of imperatively hidden imperceptible factors (IHF) without which we cannot succeed to improve our feature selection.

As I have explained in my immigration example, only when selecting the correct feature, e.g. citizenship, the risk for being asked to leave America by the federal government can be fully understood and hence, can be predicted much better. Could I convince anybody of the high likelihood of the presence of imperatively hidden factors, which we cannot perceive yet as being distinctly different from their environment?

Conclusions and proposed responses/adaptations of our study design - What is the rate-limiting bottleneck, which limits our research progression and why?

The current bottleneck in defeating aging is not addressed by further improving our machine learning algorithms and increasing the training samples, but instead, we must focus on improving proper feature selection first. My main contribution towards defeating aging is to predict features, measurement types and intervals between measurements, which could show the actions of aging much clearer than the features, which we have currently selected to stop aging and defeat death. Now it is up to wet-lab scientists to test my hypotheses. But even if all of them can be ruled out, the possibilities, by which the mechanism of aging could function, would be reduced. This would leave us with fewer hypotheses left to test. Since the options we have for fully understanding the aging process are large - but yet finite - any crazy appearing – no matter high unlikely seeming - hypothesis, which can be ruled out, brings us a tiny step closer to immortality.

The reason why I claim that correct feature selection, but not the gradually improving performance of our machine learning algorithms, is the current bottleneck, which is holding us back from improving our understanding of the aging process, is that our machine learning algorithms have been improving gradually over time, but our feature selection methods have not.

The fact that I cannot find any data for measuring the yeast transcriptome in five-minute intervals for more than 3 out of the average 25 replications, which is considered the average wild type (WT) yeast replicative lifespan, indicates that nobody has seriously suspected that we could at least observe the effects of the aging mechanism by selecting new periodic features, such as period length, temporal phase shift or amplitude, which only make sense if we replace our linear with a periodic concept of life. However, this requires us to change our concepts about life to be driven by linearly acting trends to cyclical periodically acting trends in order to expand our feature selection options to periodic quantities, such as period length, temporal phase shift, amplitude or oscillation pattern, which would have been impossible to imagine when holding on to the old linear concept. In this case – although we could clearly measure the period length - we could not detect it as a feature affected by aging until we explicitly define, select and measure this new feature, e.g. the period length, temporal phase shift, amplitude or oscillation pattern.

Please let me know if this writing makes sense to you, because so far, almost nobody, except for me, seems to worry about this problem. Thanks a lot for your time to read and think through this. I welcome your feedback because my conclusions are logical but surprising to me because nobody else appears to have been aware of this because our study designs don’t reflect this insight yet.

Here is a positive response to my posting at Research Gate, which gave me the confidence to share this.

In general, we describe a machine learning system as the sequence of three fundamental stages: preprocessing, processing and post-processing. The preprocessing has been concentrated, fundamentally, on the selection of attributes (editing), on the selection of objects (condensation) or on the mixture of both, but always starting from a previous database. The processing has followed strategies guided by symbolic learning, regression, connectionism, evolutionary-genetic algorithms, probabilities or analogy. And more recently, by the classifiers combination schemes and deep learning. While post-processing has focused on improving the quality of the prediction and/or trying to explain it. However, despite of many efforts made in each of these stages, over more than 60 years, don't exist the master algorithm capable of solving all the learning problems. This means that machine learning systems have improved over time, but our selection of features has not. So where is the error?

Hence, I believe that yes, your writing can make a lot of sense!!!

The imperatively hidden features could be the key to make essential observations that allow us to understand processes or phenomena that we have not yet been able to explain. If we start from the fact that the success of our experimental design is subject not only to the objectives we pursue, but also to the nature of the data we have and to their capacity to explain (model) the phenomenon or process itself . Then, our lack of capacity to understand a phenomenon becomes the limiting factor when it comes to explaining it, or what is the same, it prevents us from describing it according to its features and, therefore, to model it. Hence, the importance of knowing those essential concepts that allow us to understand what is happening. To subsequently, be able to make an adequate selection of features that leads to the development of that algorithm capable of modeling the process. This means that, even when we have a large number of characteristics on a process or phenomenon and the combination thereof, if we do not have those features that truly describe it and still remain imperatively hidden, then it will not be possible to understand it.

I work in the field of Computational Biology, precisely developing algorithms for the prediction of protein structures. And after hundreds and hundreds of algorithms and approximations described by the literature, the prediction does not exceed 30% of accuracy. This could be due to our inability to adequately model the proteins folding process. To our inability to discover what are the concepts, factors or sub-process that we cannot perceive and that prevent us from fully understanding the process from a holistic point of view. It is true that if we assume that each consequence must have a cause, then any unexplained consequence indicates the presence of imperatively hidden factors without which we cannot improve our selection of traits. This, warns us that no matter how much we focus on improving our machine learning algorithms and increase the training samples, if not we focus on an appropriate selection of traits.

I worry about that there could be many still hidden dimensions, which are very similar to the magnetic field that we cannot yet anticipate. But we must first associate information from these kind of magnetic-field-resembling still imperatively hidden dimensions with aging before we can understand aging.

Since we humans have observational tunnel vision, which is mostly limited to the dimensions of our sensations, which must use artificial intelligence because for it all the different dimensions and the features, which define them, are more equal. Only if we can make people understand this, we will have a chance to collectively survive. I need help to get this published because only then experts will take it seriously. For that, I must provide proof-of-principle that we still very naive and observation bias humans would have missed important relevant information if we would not have let artificial intelligence (AI) define possibly aging-relevant features for us in a much more systematic and less bias manner. For us bias humans to create a much less bias AI, we must be able to look at life from many different ridiculous-seeming perspectives because that is what we expect our aging-features-selecting AI to accomplish for us. I am really good at that but the problem is that nobody seems to have time to me to listen. But if I write it, almost nobody has time to read my writings either. We need to create AI to systematically search for relations between our observational measurements, which we humans cannot suspect.

Here is another writing of mine, in which I have described a partial solution, even before I had defined the problem. We must reword it in such a way that people can understand it much easier. We must show one example, in which it has worked, as proof-of-principal that it will work in similar ways if we succeed in expanding its scope . The most important thing is that I don't feel alone because otherwise I may start believing that I must be wrong since nobody else seems to be thinking my way. Below is my partial remedy.

Continuously Ongoing Emergency Random Evolution mimicking procedure "Unpredictable Survival"

rding the number and order of the Transcription Factor Bimding Site (TFBS) for the same Transcription Factor (TF) in the promoter of one gene it could still predict time series plots well enough to raise its prediction power far above the current threshold.

Although this old model is still imperfect it has value to get it as soon as possible instead of waiting until our crawler has found all input parameters (features) to assign a value to all possible dimension of the TFBS domain. This would actually speak in favor of allowing our prediction crawler to randomly vary any specific dimension of any domain suited for training supervised machine learning because the fewer the number of dimensions making up any domain the fewer and smaller information input domain are required for building a model based on randomly considering and randomly grouped information domains. Currently, most of us are not aware of the artificial imperative limitations resulting from letting humans have the monopoly on deciding, which dimensions can be grouped together to form a meaningful instance for input or output to train a supervised model. It is likely that smaller domains consisting of fewer dimensions or larger domain combining more dimensions could be more. But - although there are so many humans on this planet - our thinking, understanding, conceptualizing, imagining and applying our intuitive preferences for intuitively tending to include very specific dimensions into an indivisible input or output instance without even worrying about possible alternatives, is still too similar. The way in which our senses, perceptions, imaginations, concepts and partial understanding of any phenomenon intuitively selects the dimensions to a larger domain, which most of us would never even consider to predict in parts or as a very small dimension of a much larger super-domain, is only one out of very many possible option for combining any number of specific dimensions into a domain from which any number of input or output instances can be formed. One could imagine a domain as a row like a gene, which can have any number of column i.e. its dimensions, which must be considered like a single instance in their combination because it is lacking the option to consider only a few of its columns or combining some of with columns from an entirely different and unrelated table. A good example are time series plots. Human tend to be bias and prefer to define the gene expression time series curves by mRNA measured at each time point. This sounds so obvious but is this the best way for conceptualizing the temporal expression signature for each gene? I felt my colorful time series plots have much more meaning and can carry much more informational value as well as a more meaningful concept for imaging, comparing and analyzing gene specific temporal signatures. But although they look very pretty and are a good way to get a first impression about the similarities between two curves, they are not well suited to find out whether the plots for the genes, which belong to the same GO term, are indeed more correlated to each other than to the rest. Since I felt that a vector can never be the same as a curve I tried many ways to account for the slopes connecting each time point. But since I could think of so many different ways to achieve this, I could not decide on any way that I consider as the best possible option, I am still not sure how to convert time series plots into numerical dimensions, which possess the very obvious advantage to allow for easy comparing, ranking and quantifying. I am not sure how to account for differences between plots from the Y axis. Maybe we should add another dimension to our concepts of our understanding of a time series curve. If we added to its time points also the total area under the curve to each plot, maybe we could quantify them in a much better and more intuitive way. But how much numerical value should we give each time point and the area under the curve. I am stuck with this problem ever since I tried to quantify time series plots. But imagine how many more option you'd had if you were not a human because then you would not limit your dimensions for defining your domain to only those you can easily imagine. A computer can randomly extract and try out any combination, subset or superset of dimensions without tending to be limited to those dimensions that can easily be conceptualized as a picture. Extreme gradient boosting (xgboost), which never gets tired to randomly define an indivisible domain by any combination of dimensions, might have much more luck.


The writing above about Imperative Hidden Elements (IHE) is the conclusion of my dissertation so far. But, if I want this writing to be the conclusion of my work, then it must get published. Now the hard part is to find somebody, who can understand its significance. If people would understand it, they'd change their research approach. But when I post it online, the administrators mistakenly assume it is spam and delete it. So if I can get help to get connected to a journal, which agrees to publish a revised version of it, then I am practically done.

I must be done by end of March at the very latest, because if I cannot graduate this spring, then I can never graduate. If I can graduate, then I can get a work permit. But if I cannot find a job within 90 days, then my work permit will be taken away again. But I can only get a job, if I can show at least one first-author bioinformatics publication in a peer-reviewed respected scientific bioinformatics journal with high impact factor. But when I look at all the other publications on the net, they have done much more work than I can do until the end of March, especially without money and GA funding.

I am supposed to try to give numerical examples of my writing, but I am not yet good enough in R to do it. Graphing in Python, which Daniel is teaching me, is also hard. I was able to plot the trajectories for 40 highly correlated mitochondrial large ribosomal subunit genes, but I had to type each single gene by hand. I cannot manually enter all genes, which are grouped together into approximately 5,800 GO terms ranging from one to up to over 2,000 member genes, into a .csv file to specify my choices. It must be a query, which allows me to visually look and click on all the GO terms, of which I want to plot the time series trajectories.

Then I must try different methods to cluster them and decide on a method that best resembles the similarity ranking of our experimentally confirmed observations i.e. that gets to the same results as we see in reality regardless of reason. Then I can claim that, since my clustering correctly predicts already known reality, then it may also correctly infer the functions of those genes, for which it is still unknown. This takes lots of trial and error. There is no right or wrong answer. If I can find a way, then we get to name all the genes, which function we could infer correctly. I rewrote this part of my dissertation but it is still hard to understand.

Outlier analysis might be easier. I read some papers, which claim that when a single gene is not co-expressed similar to all the other GO-term gene members, it must be mistakenly assigned to that particular GO-term and recommend reassigning it to another one. This happens frequently because GO (Gene Ontology) terms change at least once per year. This would also give us something to publish. I must show that something in my dissertation is novel because otherwise it won’t count as a dissertation. This means that I must read all the math papers, which were recommended to me on Research Gate, to learn about the many different options by which time series curves could be clustered. Since most of the articles refer to specific R packages, we must translate them into Python to see which results look most promising. We might even have to split up the genes in periodic and non-periodic genes because the Meta-Cycle R package we used forced a period length and a phase shift on all the genes even on constantly expressed housekeeping genes and on genes, which were never expressed at all. This must inevitably lead to wrong conclusions. I wish all those clustering articles had functional code with them, so we could reproduce and compare, instead of having to reinvent them. I am not good enough in R yet to be able to learn a new R package on my own although there are many cool sounding Bioconductor packages out there.

The main problem is that it’s not even clear to me yet, in which direction we must proceed, which is frustrating because most of the time I keep getting results, which I never expected. Now since my life depends on completing this dissertation this spring, I feel too tense to peacefully sleep, because not only do I need to somehow find a way to finish my dissertation, but also to make up for all I missed since I had no access to a Linux computer until last month.

I applied for more than 1,000 jobs, but got rejected everywhere, because the minimum what employers expect is to be very good in Linux, Python, R, Machine Learning, C, C++, Java, Java Script, Perl, Bash, MATLAB, RNA Seq., Cancer and advanced statistics. I am not good yet in any of them. I only have sort of a working knowledge of the first three. The school has not given me any of the skills, in which I am expected to have experience when entering the job market with a PhD in bioinformatics. It seems that everyone is supposed to learn the skills, which they really need to get a job, totally on their own. Then the only accomplishment, which I feel I have been able to achieve so far, is to come up with a legitimate excuse for why machine learning cannot make perfect predictions as long as not all the necessary input features have been selected properly.

I intuitively felt for a long time that I have been set on an academic suicide mission since I started but now I am finally able to logically explain the reasons for it. For a long time it was not clear to me why I kept feeling this way. So if this gets published, then everybody else on the same kind of academic suicide mission, for which no solution must exist and which therefore can never be achieved with the resources we have, no longer needs to feel bad about having failed to accomplish his/her university objectives.

Why should the concept of Imperatively Hidden Elements (IHE) become common knowledge?

Or was it clear to everyone, except for me, that nobody even expected a 100% clear cut and reproducible solution, and hence had no reason to feel bad about, e.g. failing to discover at least a tiny step, which would bring us closer to immortality? Did everyone, except for me, knew all along that our scientific progress-rate limiting bottlenecking factor is proper feature selection but not the lack of advances in improving our machine learning algorithms? I assumed that this could not be the case because most publications present improvements in machine learning algorithms, but neglect to even mention that lack of progress in better feature selection is preventing us from making any significant progress but not poorly performing machine learning algorithms. I thought that if I did not know that until my defense, then most others, especially those who don’t know much about machine learning yet, i.e. most of our wet-lab biologists, who need to design their experiments accordingly, are not aware of this problem either. Then I thought I must tell them or else they keep mistakenly misinterpreting unpredictable outcomes as unavoidable noise instead of correctly realizing still imperatively hidden elements (IHE) affect our predicted outcome in manner, which we cannot yet explain, unless we start observing the last temporal visible object/observation/event, after which we can no longer correctly predict the chain of subsequent events or observations, from many different perspectives and angles in order to really scrutinize it legitimacy, because most likely, it is composed of more than the visible object, of which we are already aware, because an undetermined number of distinctly different, but yet still imperatively hidden; thus inevitably for us still imperceptible, but nevertheless fully legitimate and truly existing yet invisible objects look too similar to our only visible object, which tricks us into the illusion of erroneously believing that it’s only a single object. But the truth is that it is actually 2 or more objects, between which we cannot yet distinguish. Nevertheless, the number of possible outcomes depends on the number of truly existing objects, regardless whether or not we can distinguish them from their background.

For example, until this morning, I was not aware of the difference between Python and Cython. Cython was for me in imperatively hidden object, which to me looked exactly like Python, because Cython is almost identical to Python, except for, that its variables must be of a specified type, e.g. string, vector, list, numeric, data frame, etc., which is not the case in Python. This led to my observation that some Python packages run fine on Windows whereas other never worked. Since the PyAffy.py library must have been written in Cython, it tricked me into the illusion that the only way to use a Python package, which fails to run under Windows, is to use Linux. It took us 3 months to set up a Linux laptop I could see. Now I discovered that we could have copied the Cython code and save it as a Python file by omitting specifying the variable type and run it just like a Python file.

If we had used machine learning to discover the reason for some Python packages, specifically the PyAffy-package not running under Windows, could machine learning figure out that the feature “variable type selection”, which is a Boolean of present or absent, can be used to distinguish between Python and Cython? If we just gave the programming code of Python libraries and the outcome of running and not running as training data for supervised machine learning, could it figure out without any prior knowledge about Cython, that a programming language other than Python is causing problems under Windows? Can machine learning learn programming? Can it learn to conceptually understand the differences between variable types, classes, functions, objects, loops, methods, etc.?

I feel that our schooling has not adequately prepared us to properly deal with imperatively hidden elements, because most students appear to be totally left in the dark about this dangerously unnecessarily very time-consuming temporary progress-preventing nettlesome barrier, which can halt progress for many years, because nobody ever shared with them (i.e. our future wet-lab scientists, who must choose wisely, which data they collect for the computational analysts, to reach valid conclusions) the concept that the risk for misinterpreting poor outcome predictability as unavoidable background noise instead of getting alerted that a still imperatively hidden element (IHE), which we cannot perceive yet, creates a much bigger and more dangerous obstacle, than any visible element, which has at least one feature that causes it to stand out from what we often mistakenly refer to as background noise in the system, until this imperatively hidden element (IHE) has been fully uncovered and understood.

Now my biggest question is who can help me to get this published, especially as long as no numbers and mathematical calculations are involved.

Therefore, we must generate a numerical example as proof-of-principle that there can be instances, where my description fully applies, because I am afraid that a bioinformatics dissertation lacking any numerical calculations may not b considered as a dissertation despite its potentially much further reaching impact if people seriously trying to understand its meanings and apply it according to its implications.

For example, only 500 years ago, people dreamed of immortality like we are dreaming of it today. Unfortunately, they had no concept of a cell yet. But exactly this lack of concept about the cell being the atomic indivisible and smallest element of life, made everything inside the cell imperatively hidden elements (IHEs), which could not be uncovered before we succeeded in conceptualizing a cell as the smallest independently functioning unit of life. But how many of such kind of fundamental cell concepts discoveries are we still away from correctly understanding and reversing aging?

The reason, which gave me the confidence to seriously claim that all I have stated above must be truer than what I view as the mainstream perception about the mode of action, which is driving our scientific discovery process and its direction, is a detailed clear and easy to understand reply by a bioinformatics director on Research Gate, where I had posted the attached question for verification, because at first I could not believe that my logical conclusion from my own writing is a much better way to reflect reality because the evolution of life has not been limited to our narrow perceptual spectrum, its few features and dimensions, which many of us subconsciously seem to consider the boundaries of the world. No phenomenon will ever stop being a fully legitimate phenomenon just because we are not aware of it! But who can tell now, what we are not aware of yet until we are aware of it?

Many bacteria, ants and spiders are so blind that they cannot even sense any light. But who, while still mentally sane would ever dare to claim that their lives are not affected by visible light? If nothing else, life makes a difference in how well their predators can see and find their blind pray.

Who can know in advance today how many imperatively hidden elements (IHE) (e.g. objects (IHO), or factors (IHF), or concepts (IHC), or relationships (IHR), or variables (IHV), or reasons (IHR), or interactions (IHI), or dependencies (IHd), or dimensions (IHD), or ID-etc., which are clearly defined by their inherent innate features), still await our timely discovery?

However, we can only significantly accelerate the rate, by which we will uncover the still remaining IHEs, if we stop denying or ignoring the no longer IHC of IHE, but instead, embrace it as an unexpected shortcut to immortality. A widely shared better understanding and first-hand personal experiences of the inherently synergistically acting dynamics of selecting the most promising methods for the soonest uncovering for most of the IHE needs to become the implicitly universally shared accepted scientific foundation because it maximizes our chances for individual survival. Since we are all defined by our experiences and the way we react to them we all have a dynamically changing unique and irreplaceable self-identity. If we lose it by losing our lives our experiences are lost forever because they can never be retrieved again. From the individual perspective of any deceased person the situation after death = that before death. This inevitably causes the long-term total loss of the value of life as long as it remains finite. From the perspective of any time point after death the lifespan of the deceased becomes irrelevant because – like before birth – self-identity and self-perception are lacking at any time before birth or after death. Therefore, life can only make sense if it never ends or else it will be in vain from the very beginning. The fact that most humans refuse to admit this fact does not change it in any way no matter how much we hate and deny it. Since every one of us can only choose between living an eventually worthless life, because over time everyone will inevitably forgotten, or immortality as the only other possible alternative, since it is the only way to retain and expand the subjective meaning of life over time permanently by retaining all otherwise irreproducible personality-forming past memories and experiences based on which everyone’s unique self-identity and self-perceptions keeps gradually changing over time with every new experience, impression, idea, concept or any other change in perception. This makes everyone’s self-perception and self-identity as indefinitely valuable because it cannot be restored after death. Similar to evolution, everyone’s self-identity constitutes the best adaptation to the challenges and opportunities experienced between birth and presence. Therefore, like in evolution, there must exist a set of environmental conditions, which could potentially make everyone, no matter how severely disabled, sick, dependent and maladapted even the most sorriest individual may appear to the current set of environmental condition, there is most likely a situation – no matter how unlikely it may seem – to which even the currently most struggling person can respond better than anyone else. This gives everyone – including any animal – which perceives itself as being something other than its environment the inherently intrinsic role of a tool, which works very well, i.e. better than any other tool for some tasks, while being completely useless for others. However, the decisive factor for the survival of the entire group of very diverse and heterogeneous unique instances of never-resting or ever static; hence, irreplaceable and irreproducible self-identities and subjective self-perceptions, who can perceive their environment as something other than themselves, depends on having only a single population member, who can deal with any particular problem or challenge, when nobody else can. Even animals like dogs, horses, cats, snakes, donkeys, monkeys, hawks, doves, marsupials, dolphins, whales, reptiles, birds, rodents, foxes, rabbits, elephants, etc. then contribute to the survival of their owners during emotional, medical or confrontational crises. This even gives such kind of animal a status of a group survival tool. Nobody can predict in advance future changes and hence nobody can predict which survival tools will be most beneficial for all of us tomorrow.

Almost all of our sun’s radiation, which supports all life on Earth because it can get converted into lipids, carbohydrates and proteins, goes unused every single second. It is sufficient to provide enough energy to power the life of billions of planets like Earths simultaneously. This renders any fear about overpopulation absurd. Regardless, whether we succeed in giving our lives permanent meanings by reversing all adverse aspects of aging, the birth rate keeps exceeding its supposedly counterbalancing death rate by several percent. This will inevitably cause our mother-planet to become too small for sustaining all instances of life, which deserve every imaginable support to stay alive as long as they feel not to be the same as their surroundings because this allows them to perceive and respond. Killing them would be the same as taking away an irreplaceable uniquely responding option, which deserves its own opportunity to adapt to its experiences by actively striving to find the best subjectively best situation on its own. Every form of consciousness, which can distinguish itself as not being the same as its surroundings is like an imperatively visible element (IVE), which would be demoted to an IHE, if killed. It would lose out on opportunities to improve its self-perception. It would lose its irreplaceable unique self-identity and perception; hence, causing its memories to become inevitably lost forever. It may take long until people start defining the value of life based on the same criteria by which we distinguish between IHEs and IVEs. As long as any form of life is forced to go backwards from an IVE to an IHE, it is losing its most valuable feature, i.e. irreplaceable self-identity and self-perception. Humans may never know which animals would be deprived of their most valuable features as soon as they are getting prevented from making a difference between themselves and their surroundings.

But this is all just intellectual theory because as soon as we start reaching for eternal life, we must define what it is to minimize the risk that it will be taken away from anyone, who deserves to keep it. That is why I applied the same criteria I use to distinguish between IHE and IVE. The new rule of thumb for deserving to live is: any critter, who subjectively perceives itself as not exactly the same as its surrounding background noise, i.e. who possesses at least one feature by which it differs from its surroundings, should be allowed to keep living in its environment, because it has passed the critical threshold, above which a uniquely irreplaceable instance of constantly adapting self-identity and self-perception must be assumed. Depriving any critter from its opportunities and benefits to remain active within its environment would be morally wrong, because if we did this to humans, it would be considered murder. But then, any animals navigating in similar levels of consciousness would lose the same as their aging human counterparts and would suffer the same kind of loss, which humans would feel when gradually losing their ability to subjectively perceive themselves as something other than their surroundings. All this hypothetical writing above, which may never reach any practical significance, because homo Sapiens tends to care less for other species is only necessary, because of the huge overlap between the declining self-perception and self-identity capabilities of dying humans, especially when dying of Alzheimer, dementia or any other age-related disease interfering with memory formation and retrieval, without which no unique self-identity and self-perception can be maintained any longer. Once lost, it can never be restored again. This is the sad about humans gradually losing the very feature, which has given them the inherently innate features of an IVE, all their lives. Unfortunately, after having lost their subjective perception of their most defining IVE feature, i.e. to perceive themselves as something other than their surrounding background living environment, they can no longer maintain any form of self-identity and self-perception, not even a completely static one, because it imperatively depends on memory formation, retrieval, responses and adaptations. This demotes their mental and cognitive status from an IVE to and IHE; hence, causing them to disappear from their own subjective perception. This implies that their subjective perception can no longer be brought back to what it was between birth and loss of the capabilities associated with any IVE.

Note that no IVE will ever stay the same as time goes on. It is in constant flax of gradually transitioning with every new impression and every change it notices. No IVE resembling critter can ever return to exactly any of its It past self-perception and self-identity. This makes it imperatively irreproducible and thus, guarantees its indefinitely high value, which cannot be compensated for in any way by any currency.

Seriously considering immortality requires us to break away from many of our long overdue obsolete concepts. Although we are not even close to accomplishing immortality yet by rejuvenating and then remain forever young, healthy, energetic, curious, adventurous, flexible, adaptive, ambitious, innovative, creative, passionate and full of love for all aspects of life, we would benefit a lot from replacing our old mortal with immortal concepts of life because this is a precondition for succeeding in transitioning faster from mortal to immortal beings. For our generation, this will make the difference between life and death.

Once thing, which I am glad not to be all alone, which must change immediately, is that we, i.e. the WHO, the NIH, the FDA, our medical coding, das Deutsche Gesundheitsamts and every healthcare provider worldwide must stop stubbornly refusing to declare aging as a disease, because it’s the worst disease ever! Aging is the master disease of all diseases because it’s causing us to gradually become more and more susceptible of developing them and inevitably subsequently die from any combination of them.

If our medical providers, who we patients must trust our lives even though most of them don’t seem to feel neither the need nor moral obligation to save our lives if we’d be fortunate to live beyond 100 years, who else can we trust? Who’ll take the initiative to protect our lives when we have aged too much for actively protecting ourselves? Who honestly deserves from being demoted from IVE to an IHE simply because it happened to so many of his/her fellows in the same age range? I’d rather commit suicide in the USA if ordered by US Immigration to return to Germany because there I have no doctor, who I can trust would fight for my life as if it was his/her own. That is what I am expecting of my doctors.

I am an enthusiastic member of the German Party, which promotes health and longevity research. I perceive myself as a very strong and convincing communicator. Yet I failed in convincing a medical doctor to support our party by signing our partition with her full name and address and thus to become one out of the about 3,000 voters, who are registered in the state of Berlin, to express their support to the state election officials to allow our still very young lifespan and health-span extension party to participate in the state election for representation in the Berlin Senate. We had a heated discussion for two hours while riding the subway from Potsdam to Erkner. By the time our subway commuter train, i.e. the Berlin S-Bahn, had reached its final destination she apologized for refusing to sign our petition to be allowed to participate in our first statewide election because she felt that it is wrong to keep old people alive beyond their natural lifetime. Who then would fight for my life if I am no longer strong enough to fend for myself? Unfortunately, these are the kind of doctors, on whom my life would depend, if US Immigration forces me to leave America because my disabilities prevent me from finding a job within only 90 days. If America punishes me for not finding a very competitive bioinformatics job on time it feels like murder because in Germany I cannot get any medical treatment since the drugs I need to function are not available there. Returning home would feel like digging my own grave because I’d be fully aware to surely die there after only forty more years at the very best. Since this would mean an imperative death verdict for me, I’d rather die while still in the States as a means of last resort to escape the depressing uphill battle against the continuously ongoing mental, emotional, cognitive, physiological and metabolic decline without any true allies all alone because in Germany there is not a single medical provider, who I could trust my life because they believe that everyone deserves to die when having reached very old age.

Consistent with aforementioned immortality concepts, which give us the choice to select only between immortality and suicide over time (i.e. aging induced overall decline), I’d know deep in my heart that the date, at which US Immigration is revoking my OPT (Optional Practical Training) work visa, affects my life like a death sentence because from the perspective of June 18th, 2099, i.e. on my would be 125th birthday, it makes no more difference whether Thomas Hahn, maybe then referred to as Dr. Thomas Hahn committed suicide sometimes in the summer of 2018 when his OPT work permit was voided by US Immigration because – despite trying his very best nobody wanted to hire him for any paid bioinformatics job. This caused him to exceed his maximally permitted 90-day unemployment limit.

This unintended incompliance with US Immigration laws, which is totally outside the control of any OPT work permit holder, for which everyone gets punished regardless of reasons and efforts to try to comply, would take away all my reasons for wanting to stay alive because it would trap me in a medical condition from which I could never ever recover again. Its feels already bad enough that almost every of the more than 1,000 potential employers, with whom I applied for jobs since August 2017 and today, cannot see any of the many benefits (i.e. IHOs) of hiring me compared of the other - generally more than 100 applicants - with whom I must compute for every single post-doc position, for which I have applied so far.

Only one single employer appears to believe that my resume, skills and experiences are worth a second look and let me advance to the final interview round scheduled for January 12th as a camp-counselor for a bioinformatics science discovery camp for high school kits in Seattle. This was the only phone conversation with any potential employer, which I had so far, where I felt that HR was seriously interested in what I could offer as a camp counselor and teacher when working with me instead of against me. Based on our last phone conversation I am feeling confident to get hired for this job but it’s only for two weeks getting paid $27/hour. Unfortunately, when my 2 weeks of real employment, which US Immigration laws allow me to have, is over I am facing again exactly the same kind of debilitating problem, i.e. a very high risk for getting expelled from America for good for an incompliance I could not prevent.

Having no job is already bad enough. Unfortunately, as a punishment for being punished by all employers, with whom I applied, for not even having the chance of ever becoming the most qualified applicant because my unfortunate mutations, which virtually prevent me from functioning as efficiently and productively as the other more than 100 wild type (WT) phenotypes, who are much better adapted to meet the job expectations of an admittedly very fast paste rapidly changing competitive cut-throat working environment, which I will never even have a remote chance of ever getting close to their speed of job performance, no matter how hard I’ll try and how much I can succeed in compensating for the shortcomings of my visual impairment by trying alternative options to accomplish the same as the WTs, against whom I can never objectively directly compete unless when getting lucky and figuring out an alternative way of accomplishing the same objectives or even exceed their job-performance if I don’t need to depend on the same methods when being forced to compete with genetically highly superior WT genotypes. Unfortunately, I could not convince my potential employers from the unexpected and invisible benefits from hiring me because I cannot hold against them that my strengths are at very high risk to keep remaining an IHE for HR, the hiring managers, supervisors and bosses, for longer than the short time window I might be given to convince them that although my mutations prevent me from succeeding in completing as much work as my genetically superior WT job-seeker-competitors, if I get unnecessarily restricted to their work methods. However, if given enough time, resources and support, I could have a realistic chance to figure out innovative ways to exceed anyone in on-the-job performances. Despite being an objectively genetically poorly adapted inferior mutant, I’d like to have the opportunity to focus on tasks, which nobody else is expected to ever accomplish, and which therefore are never called for in any advertised job description, especially in strategic planning, concept development and discoveries, convincing and persuasive writing. These kinds of much more creative, innovative, strategic and conceptual tasks allow me to substitute my lack of reading speed, which is only 1/10th of what it should be, with my at least two times faster typing speed. This means my reading skills are no better than those of a second-grade elementary students.


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