Article

Refining interaction search through signed iterative Random Forests

2018-11-11

Abstract excerpt

Advances in supervised learning have enabled accurate prediction in biological systems governed by complex interactions among biomolecules. However, state-of-the-art predictive algorithms are typically “black-boxes,” learning statistical interactions that are difficult to translate into testable hypotheses. The iterative Random Forest (iRF) algorithm took a step towards bridging this gap by providing a computation...

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Literature Corpus work: 8a19196f-289b-55d7-9440-655a4ce8cb83
DOI: 10.1101/467498

Open publication

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