Prediction of Genetic Interactions Using Machine Learning and Network Properties.

TitlePrediction of Genetic Interactions Using Machine Learning and Network Properties.
Publication TypeJournal Article
Year of Publication2015
AuthorsMadhukar NS, Elemento O, Pandey G
JournalFront Bioeng Biotechnol
Date Published2015

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.

Alternate JournalFront Bioeng Biotechnol
PubMed ID26579514
PubMed Central IDPMC4620407
Grant ListR01 CA194547 / CA / NCI NIH HHS / United States
T32 GM083937 / GM / NIGMS NIH HHS / United States
R01 GM114434 / GM / NIGMS NIH HHS / United States
U54 OD020353 / OD / NIH HHS / United States
U01 HL107388 / HL / NHLBI NIH HHS / United States