首页|Decoding Missense Variants by Incorporating Phase Separation via Machine Learning
Decoding Missense Variants by Incorporating Phase Separation via Machine Learning
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – According to news reporting based on a preprint abstract, our journalists obtained thefollowing quote sourced from bi orxiv.org:“Computational models have made significant progress in predicting the effect of protein variants.“However, deciphering numerous variants of unknown significance (VUS) located wi thin intrinsicallydisordered regions (IDRs) remains challenging. To address thi s issue, we introduced liquid-liquid phaseseparation (LLPS), which is tightly l inked to IDRs, into the analysis of missense variants. LLPS is vital formultipl e physiological processes. By leveraging missense variants that alter phase sepa ration propensity,we developed an innovative machine-learning approach called P SMutPred to predict the impact of missensemutations on phase separation. PSMutP red shows robust performance in predicting missense variants thataffect natural phase separation.
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