Prediction of Genetic Interactions Using Machine Learning and Network Properties - Madhukar, Neel S.; Elemento, Olivier; Pandey, Gaurav (Frontiers in Bioengineering and Biotechnology, 2015)
A genetic interaction 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 genetic interaction - 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 genetic interactions 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 genetic interactions is possible in single cell organisms such as yeast, the systematic discovery of genetic interactions is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict genetic interactions, 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 genetic interactions, both under general (healthy/standard laboratory) conditions and under specific contexts, like diseases.