SMACC

Budding yeast datasets were used to develop high-performance Multinomial Regression (MN) models capable of predicting the impact of single, double and triple genetic disruptions on viability. These models are interpretable and give realistic non-binary predictions and can predict negative genetic interactions (GIs) in triple-gene knockouts. They are based on a limited set of gene features and their predictions are influenced by the probability of target gene participating in molecular complexes or pathways. Furthermore, the MN models have utility in other organisms such as fission yeast, fruit flies and humans, with the single gene fitness MN model being able to distinguish essential genes necessary for cell-autonomous viability from those required for multicellular survival. Finally, our models exceed the performance of previous models, without sacrificing interpretability.本研究使用出芽酵母数据集开发了高性能多项回归（Multinomial Regression, MN）模型，该模型可预测单、双、三重重遗传扰动对细胞存活率的影响。此类模型具备可解释性，可输出符合实际的非二分类预测结果，还能预测三基因敲除实验中的负向遗传相互作用（GIs）。该类模型基于有限的基因特征集，其预测结果会受到靶基因参与分子复合物或通路的概率影响。此外，该多项回归（MN）模型还可应用于裂殖酵母、果蝇、人类等其他物种，其中单基因适合度MN模型能够区分维持细胞自主存活率必需的基因，与支持多细胞生存的必需基因。最后，本研究提出的模型在未牺牲可解释性的前提下，性能优于既往同类模型。

搜集汇总

背景与挑战

背景概述

SMACC是一个高度整合与注释的小型分子抗病毒化合物数据库，包含针对新兴病毒和螺旋酶的数万条化学基因组学数据。它旨在为病毒学家和药物化学家开发广谱抗病毒药物及螺旋酶抑制剂提供参考资源。