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Front Bioeng Biotechnol. 2015 Oct 26;3:172. doi: 10.3389/fbioe.2015.00172. eCollection 2015.

Prediction of Genetic Interactions Using Machine Learning and Network Properties.

Author information

1
Department of Physiology and Biophysics, Meyer Cancer Center, Institute for Precision Medicine and Institute for Computational Biomedicine, Weill Cornell Medical College , New York, NY , USA ; Tri-Institutional Training Program in Computational Biology and Medicine , New York, NY , USA.
2
Department of Genetics and Genomic Sciences and Graduate School of Biomedical Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai , New York, NY , USA.

Abstract

A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI - synthetic sickness or synthetic lethality - involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases.

KEYWORDS:

cancer; drug discovery; genetic interactions; machine learning; network analysis; prediction

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