AIMC Topic: Epistasis, Genetic

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Gene-gene interaction detection with deep learning.

Communications biology
The extent to which genetic interactions affect observed phenotypes is generally unknown because current interaction detection approaches only consider simple interactions between top SNPs of genes. We introduce an open-source framework for increasin...

Nested epistasis enhancer networks for robust genome regulation.

Science (New York, N.Y.)
Mammalian genomes have multiple enhancers spanning an ultralong distance (>megabases) to modulate important genes, but it is unclear how these enhancers coordinate to achieve this task. We combine multiplexed CRISPRi screening with machine learning t...

Spatial rank-based multifactor dimensionality reduction to detect gene-gene interactions for multivariate phenotypes.

BMC bioinformatics
BACKGROUND: Identifying interaction effects between genes is one of the main tasks of genome-wide association studies aiming to shed light on the biological mechanisms underlying complex diseases. Multifactor dimensionality reduction (MDR) is a popul...

Using machine learning and big data to explore the drug resistance landscape in HIV.

PLoS computational biology
Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive...

Rosetta:MSF:NN: Boosting performance of multi-state computational protein design with a neural network.

PloS one
Rational protein design aims at the targeted modification of existing proteins. To reach this goal, software suites like Rosetta propose sequences to introduce the desired properties. Challenging design problems necessitate the representation of a pr...

CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph.

BMC bioinformatics
BACKGROUND: Elucidation of interactive relation between chemicals and genes is of key relevance not only for discovering new drug leads in drug development but also for repositioning existing drugs to novel therapeutic targets. Recently, biological n...

Exploring gene-gene interaction in family-based data with an unsupervised machine learning method: EPISFA.

Genetic epidemiology
Gene-gene interaction (G × G) is thought to fill the gap between the estimated heritability of complex diseases and the limited genetic proportion explained by identified single-nucleotide polymorphisms. The current tools for exploring G × G were oft...

Molecular docking and machine learning analysis of Abemaciclib in colon cancer.

BMC molecular and cell biology
BACKGROUND: The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is personalised opens the doors to the design ...

Predicting breast cancer risk using interacting genetic and demographic factors and machine learning.

Scientific reports
Breast cancer (BC) is a multifactorial disease and the most common cancer in women worldwide. We describe a machine learning approach to identify a combination of interacting genetic variants (SNPs) and demographic risk factors for BC, especially fac...

Hierarchy and levels: analysing networks to study mechanisms in molecular biology.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Network representations are flat while mechanisms are organized into a hierarchy of levels, suggesting that the two are fundamentally opposed. I challenge this opposition by focusing on two aspects of the ways in which large-scale networks constructe...