ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features.

Motivation: The Estimation of Model Accuracy problem is a cornerstone problem in the field of Bioinformatics. As of CASP14, there are 79 global QA methods, and a minority of 39 residue-level QA methods with very few of them working on protein complexes. Here, we introduce ZoomQA, a novel, single-model method for assessing the accuracy of a tertiary protein structure/complex prediction at residue level, which have many applications such as drug discovery.

Recent Progress of Machine Learning in Gene Therapy.

With new developments in biomedical technology, it is now a viable therapeutic treatment to alter genes with techniques like CRISPR. At the same time, it is increasingly cheaper to perform whole genome sequencing, resulting in rapid advancement in gene therapy and editing in precision medicine. Understanding the current industry and academic applications of gene therapy provides an important backdrop to future scientific developments.