PertSpectra: Interpretable Matrix Factorization for Predicting Functional Impact of Genetic Perturbation Experiments.

Seowon Chang¹, Anna Shcherbina², Tal Ashuach³, Shahin Mohammadi⁴, Stephanie See⁴, Ninad Ranadive⁴, Emily Fox⁴, Navpreet Ranu⁴

¹Center for Computational and Molecular Biology, Brown University, Providence, Rhode Island, 02912, USA, seowon_chang@brown.edu.

²Insitro, South San Francisco, California, 94080, USA, annashch@insitro.com.

³Insitro, South San Francisco, California, 94080, USA, tal.ashuach@insitro.com.

⁴Insitro, South San Francisco, California, 94080, USA.

PubMed DOI

Abstract

In drug discovery, measuring the effects of genetic perturbations is a powerful tool for studying unknown disease mechanisms, but biological interpretation of these effects, especially with the advent of screens involving combinatorial perturbations, remains challenging. To address limitations in current methodology we introduce PertSpectra, a guided triple matrix factorization that incorporates p…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.