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SELF-ORGANIZING MAPS. “The maps consist of thousands of units (or "neurons") that are arranged in a two dimensional grid. In order to avoid boundary effects, the maps are often laid on the surface of a toroid that can be unwrapped for visualization. Each unit of the map has an associated vector that is originally initialized randomly.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

NEW FIELDS. “The concurrent development of microarrays as the first platform for large-scale gene expression measurements led to the birth of the new field of functional genomics, which quickly expanded to include other biomolecules, namely proteomics and metabolomics for the measurements of protein levels and metabolic intermediates respectively.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

MACHINE LEARNING TOOL. “A Self-Organizing Map (SOM) is another unsupervised machine learning clustering technique that has been used in two recent publications to analyze a large number of ChIP-seq (and DNase-seq) datasets using maps with potentially at least a thousand such micro-states.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

MICRO TO META. “We would like to interrogate the genome with a much larger number of potential micro-states and then apply some form of dimension reduction to identify related micro-states that form larger coherent groups of "meta-states".” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

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EXTREME NODE. “The combinatorial nature of gene regulation points to another extreme, where we are interested in identifying relatively small cohorts of genomic regions that show similar coordinated changes of chromatin marks and transcription factor binding across many data sets and multiple cell types.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

TWO SYSTEMS. “The systems biology of gene expression is frequently understood as a problem of gene regulatory network inference, where gene networks capture how the expression profile of individual genes interacts with each other.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

MAJOR BENCHMARKS. “When considering the interplay between systems biology and genomics, two major paradigms stand out: one is the use of gene expression measurements to obtain the structure of the system and infer Gene Regulatory Networks, while the other is the leveraging of system properties to interpret observed gene expression patterns using pathway enrichment methods.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

PERSONALIZED MEDICINE. “The rapid availability of ubiquitous sequencing holds great promise for medicine to the extent that genomics empowers the analysis of patient genomes to guide personalized treatment. While we can now sequence an individual's genome and transcriptomes, it remains extremely difficult to use that data to inform treatment.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

NEW TOOLS. “There are now a multitude of implementations based on this concept that introduce additional functional data such as protein interaction data, gene regulatory networks, pathway topology information, metabolic changes or expression kinetics. These methods have been applied not only to understand gene expression changes but also in Genome-Wide Association Studies (GWAS), comparative genomics and gene prioritization.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn