New algorithm improves gene expression marker identification across diverse biological systems

Researchers have developed a new computational approach that enables more accurate selection of genes that characterize ...
PNAS

Reversible molecular simulation for training classical and machine-learning force fields

The success of a molecular dynamics simulation depends on the accuracy of the force field used to define the atomic interactions. It is challenging to train both classical and modern machine-learning ...
PNAS

Bayesian phylodynamic inference of population dynamics with dormancy

Dormancy is a widespread bet-hedging strategy across taxa, enabling organisms to survive natural and anthropogenic disturbances. It fundamentally alters eco-evolutionary processes, including ...
IEEE

Split Learning Optimized For The Medical Field: Reducing Communication Overhead

Abstract: Split Learning (SL) is a distributed privacy-preserving learning methodology designed to address the challenges associated with the deployment of large-scale deep neural networks on medical ...
Frontiers

A layout framework for genome-wide multiple sequence alignment graphs

Sequence alignments are often used to analyze genomic data. However, such alignments are often only calculated and compared on small sequence intervals for analysis purposes. When comparing longer ...
IEEE

An Event-Driven Approach to Genotype Imputation on a Custom RISC-V Cluster

Abstract: This article proposes an event-driven solution to genotype imputation, a technique used to statistically infer missing genetic markers in DNA. The work implements the widely accepted Li and ...
C&EN

Real-Time Liquid Rate and Water Cut Prediction From the Electrical Submersible Pump Sensors Data Using Machine-Learning Algorithms

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. This study presents a novel data-driven approach for calculating multiphase flow rates ...
Frontiers

Identification of the diagnostic genes and immune cell infiltration characteristics of gastric cancer using bioinformatics analysis and machine learning

Background: Finding reliable diagnostic markers for gastric cancer (GC) is important. This work uses machine learning (ML) to identify GC diagnostic genes and investigate their connection with immune ...