Stroke-volume-allocation model enabling wearable sensors for vascular age and cardiovascular disease assessment ..read more

PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors Sinha and colleagues present PERCEPTION, a precision oncology computational pipeline that can predict the response and resistance of patients by analyzing single-cell transcriptomic data from their tumor samples ..read more

BioBBC: a multi-feature model that enhances the detection of biomedical entities ..read more

Towards accurate indel calling for oncopanel sequencing through an international pipeline competition at precisionFDA ..read more

Teacher-student collaborated multiple instance learning for pan-cancer expression prediction from histopathology slides PDL1 expression is a common biomarker for immunotherapy response in cancer, and it is usually quantified using immunohistochemistry. Here, the authors develop a weakly supervised learning approach combining multiple instance learning and a teacher-student framework to predict ..read more

Application of machine learning for identification of heterotic groups in sunflower through combined approach of phenotyping, genotyping and protein profiling ..read more

A single-cell atlas enables mapping of homeostatic cellular shifts in the adult human breast Single-cell RNA sequencing analysis of over 800,000 human adult breast cells from 55 female donors identifies 41 cell subtypes and highlights age- and parity-dependent effects. Samples from healthy women with germline mutations in showed signs of T cell exhaustion ..read more

PLMSearch: Protein language model powers accurate and fast sequence search for remote homology Homologous protein search is one of the most commonly used methods for protein analysis. Here, authors propose PLMSearch, a search method that takes only sequences as input and can search millions of protein pairs in seconds while maintaining sensitivity comparable to SOTA structure search methods ..read more

Procrustes is a machine-learning approach that removes cross-platform batch effects from clinical RNA sequencing data Procrustes is a machine-learning algorithm that can overcome batch effects across RNA-seq data obtained by different sample preparation methods, like exome capture-based or poly-A RNA-seq protocols ..read more

Transcriptome-based deep learning analysis identifies drug candidates targeting protein synthesis and autophagy for the treatment of muscle wasting disorder ..read more