Information Processing at the Genomics Level

  • Alvin T. Kho
  • Hongye Liu


A central objective in biology is to identify and characterize the mechanistic underpinnings (e.g., gene, protein interactions) of a biological phenomenon (e.g., a phenotype). Today, it is technologically feasible and commonplace to measure a great number of biomolecular features in a biological system at once, and to systematically investigate relationships between the former and the latter phenotype or phenomenological feature of interest across multiple spatial and temporal scales. The canonical starting point for such an investigation is typically a real number valued data matrix of N genomic features × M sample features, where N and M are integers, and N is often orders of magnitude greater than M. In this chapter we describe and rationalize the broad concepts and general principles underlying the analytic steps that start from this data matrix and lead to the identification of coherent mathematical patterns in the data that represent potential and testable mechanistic associations. A key challenge in this analysis is how one deals with false positives that largely arise from the high dimensionality of the data. False positives are mathematical patterns that are not coherent (from a technical or statistical standpoint) or coherent patterns that do not correspond to a true mechanistic association (from a biological standpoint).


Data Matrix Tail Length Central Dogma Expression Quantitative Trait Locus Coherent Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



analysis of variance


deoxyribonucleic acid


expressed sequence tag


false discovery rate


Granger causality


gene ontology


genome-wide association scan


principle component analysis


gage repeatability reproducibility


ribonucleic acid


receiver operating characteristic


serial analysis of gene expression


single-nucleotide polymorphism


self-organizing map


expression quantitative trait loci


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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.Boston Childrenʼs HospitalBostonUSA
  2. 2.Informatics ProgramHarvard Medical School/Boston Childrenʼs HospitalBostonUSA

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