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MOLECULAR NETWORKS. “The concept of molecular networks extends beyond gene regulatory networks. In fact, much of the early research in systems biology focused on flux balance analysis (FBA), which is a genome-wide analysis of metabolic regulation.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

DATA RICHNESS. “The transformation of biology by genomics from a relatively data-poor into a data-intensive field has motivated the development of novel computational, machine-learning and other quantitative methods for genomic analysis that attracted a large number of engineers, physicists, and mathematicians into biology.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

DEEP MINING. “The resulting map is mined for relationships between training dataset enrichments in specific units and can be interpreted further by laying additional data on the map not used during the training. These maps typically reveal very distinct colocalization patterns between particular datasets in specific cell-types.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

GLOBAL ASSESSMENTS. “A hallmark of this functional data is that they represent global measurements of thousands of molecular features where no one feature has an a priori higher importance than others.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

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

HARD PREDICTIONS. “We are still unable to assess the contribution of these changes in functional elements to what are often complex phenotypes that arise from these combinatorial interactions between multiple variants occurring jointly at genes, let alone their interactions with the environment.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

The moon up cose and personal. https://youtu.be/nr5Pj6GQL2o View in LinkedIn