@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix : <http://denigma.org/resource/> .
:OpenBiomind a :Software_Toolkit ;
:definition "A toolkit for analysis of gene expression, SNP and other biological datasets using advanced machine learning techniques" ;
:website <http://code.google.com/p/openbiomind/> .
# 1. Did you pay the whole amount (1600$ to Molcan)? I was left with the understanding that only a part had been paid and that Molcan was waiting for the other part. Perhaps I have missed something after.
# 1'. I didn't see the proof and will check my emails. Did you send it to me recently (in 2014)? or was it before?
:Molcan a :Company .
:Reimbursing a owl:Class .
# -- reimbursing the first part to Daniel, the one you paid
# -- Molcan -- SLS is paying the complement
# -- china //proof Ed will check
:Bioinformatics_Project a :Project ;
:uses :Denigma, :Connectivity_Map .
# genetic-expression-database (map). Anton and I would like to go on that path. More precisely I would like
:Connectivity_Map a :Online_Resource .
:Potential_Gerontodrug_List a :List .
:Potential_Gerontogene_List a :List .
# Common signature (Meta analysis)
:Yeast_Ontology a :Ontology ;
:link <http://denigma.org/data/Yeast/ontology> .
:Mice_Ontology a :Ontology ;
:link <http://denigma.org/data/Mice/ontology> .
:Drug_Ontology a :Ontology ;
:link <http://denigma.org/data/Drug/ontology> .
:Resource_Description_Framework a :Framework .
:Turtle a :Markup_Language .
:Subject :Predicate :Object .
:Aging :is_a :Disease .
:Webintelligence a :Web_Resource ;
:website <http://webintelligence.eu/> .
 a :Query ;
PREFIX bds: <http://www.bigdata.com/rdf/search#>
SELECT ?property ?object
<http://denigma.org/resource/Aging> ?property ?object
:Semanticweb a :Course ;
:website <https://openhpi.de/course/semanticweb> .
# What drugs impact gerontogenes
Biological informatics (abbreviated bioinformatics) is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to study and process biological data. Bioinformatics is the research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioural or health data, including those tools required to acquire, store, organize, archive, analyse, and visualize such data [Huerta+Al:2000].
It is impossible to do much with this gigantic volume of data unless one uses computer tools to decipher and to find meaningful patterns in the data. Bioinformatics essentially is using computers to analyse a wide variety of biological problems. Bioinformatics is the transformation of data into knowledge and understanding in the area of biology. Bioinformatics started as a small and obscure discipline. Today it is a huge field that is making a digital revolution in biology.
Bioinformatics and computational biology have similar aims and approaches, but they differ in scope where bioinformatics is mostly more general and computational biology problems are often more specific in their focus. Bioinformatics organizes and analysis basic biological data, while computational biology builds theoretical models of biological systems. To be more precise, bioinformatics usually deals with genomics and other omics while computational biology is totally focused on building accurate mathematical simulations.
The aims of bioinformatics are three-fold:
Today bioinformatics aims to conduct global analysis of all the available data with the aim of uncovering common principles that apply across many systems and highlight novel features. The computational goals of bioinformatics are:
The central paradigm of molecular biology is:
DNA -> RNA -> Protein -> Phenotype (Symptoms)
The corresponding central paradigm of bioinformatics is:
-> Molecular structure
-> Biochemical function
-> Phenotype (Symptoms)
Bioinformatics is a fast-growing interdisciplinary field. As a result of the rising research effort in bioinformatics, the global bioinformatics market was valued at 4,110.6 million USD in 2014 and is expected to reach 12,542.4 million USD in 2020 [Hare:2014].
Genomic sequencing capabilities have increased exponentially [Lander+Al:2001] [Venter+Al:2001] [Kircher+Kelso:2010], outstripping advances in computing power [Kahn:2011] [Gross:2011] [Huttenhower+Hofmann:2010] [Schatz+Al:2010] [Cloud:2012]. The rate of available genomic data is increasing approximately tenfold every year, a rate much faster than Moore's Law for computational processing power [Kahn:2011].
Human genomes differ on average by only 0.1% [Venter+Al:2001]. One thousand human genomes contain less than twice the unique information of one genome. Thus, although individual genomes are not very compressible, collections of related genomes are extremely compressible [Christley+Al:2009] [Brandon+Al:2009] [Maekinen+Al:2009] [Kozanitis+Al:2010].
Compressive algorithms for genomics have the advantage of becoming proportionally faster with the size of the available data [Loh+Al:2012]. As computing moves toward distributed and multiprocessor architectures, this ability must be considered for new algorithms to be run in parallel.
Bioinformatics is very useful as it forms a basic for other disciplines at the intersection of biology and computer science, as it deals with the raw data and provides algorithms to operate on it, and gathers meaningful insights into biological phenomena. It is, for instance, utilized heavily in functional genomics to infer the functions of biological entities encoded in the genome.