The front page of AI-driven biology
Thursday, October 15, 2026Thu, Oct 15
Updated 7:01 PM PT
Illumina announced the launch of BioInsight, a unified software suite that combines its multiomics analytics, artificial intelligence tools, and biological datasets.
Machine learning classifiers trained on early embryonic stages revealed that tumors widely reactivate gene expression programs from preimplantation development, with opposing effects on survival depending on the stage mimicked.
HitSV combines repeat-aware breakpoint detection with haplotype-resolved local DNA assembly to identify structural genomic variants across multiple sequencing platforms.
Researchers introduced DNA-LLM2Vec, a staged conversion framework that equips directional, generative DNA language models with two-way sequence context.
An artificial intelligence system quantified human brown adipose tissue activity across clinical scans, revealing strong associations between active brown fat, improved metabolic profiles, and reduced rates of psychiatric illness.
A computational drug-repurposing pipeline pairs multi-tissue RNA sequencing from treated rodents with a harmonized library of over 900 human disease expression profiles.
SpatialZoomer uses spectral graph theory to analyze spatial transcriptomics data simultaneously at individual cell resolution and across broad tissue domains.
Researchers built two neural network tools, xNNDriver and xAEDriver, to prioritize cancer driver genes by connecting mutations to gene essentiality profiles from the Cancer Dependency Map.
Researchers mapped the binding profiles of 67 RNA-binding proteins in human cells using a ligation-free profiling method termed ecCLIP.
A computational framework named SPACER extracts gene-level recruitment rules directly from spatial transcriptomics data to explain how tissues attract specific cell types.
A computational tool called scMOCHA recovers mitochondrial DNA variants and heteroplasmy directly from standard single-cell RNA sequencing datasets.
EpiDiff uses machine learning on DNA methylation data to produce a continuous numerical score of cellular differentiation across single-cell, sorted, and bulk tissue samples.
FastaLake uses AlphaNovo de novo peptide predictions to retrieve complete microbial proteins from large catalogs, generating sample-specific search spaces that match or outperform traditional metagenomic databases.
Researchers deployed autonomous instances of Claude Code to search 1.9 billion protein clusters for reverse transcriptases, identifying a new class of giant-virus enzymes dubbed array-associated RTs.
A machine learning tool called unduliner uses long-read sequencing data to connect specific somatic mutations with local changes in DNA methylation patterns.
Seal, a new transfer-learning framework, predicts spatiotemporal gene regulation and variant impacts across 26 brain regions, 30 cell types, and seven human developmental stages.
Researchers developed Droplet-MT, a microfluidic method that profiles the transcriptome and DNA methylation together across roughly 10,000 single cells per run.
Researchers combined single-nucleus RNA sequencing across 413,697 nuclei with spatial transcriptomics in 56 lung sections to trace the progression of lung adenocarcinoma from early lesions to invasive cancer.
Researchers built REFinder, a pipeline that uses a DNA language model called REBEAN to label raw sequencing reads by enzyme class and assemble only sequences with putative catalytic signatures.
Researchers developed VINTER, a generative vision-language model trained on 1.45 million histology-caption pairs that jointly processes tissue morphology, local gene counts, and spatial neighborhoods.
The Advanced Research Projects Agency for Health awarded Bexorg up to $28.3 million, reported by Endpoints News as $28 million, over a three-phase, 36-month contract.
Researchers released a standalone desktop application for intelligent Differential Mobility Spectrometry, a deep learning tool that automates parameter tuning for distinguishing nearly identical lipid molecules.
Comparative analysis of four field strains of Colletotrichum graminicola against a reference assembly revealed extensive chromosomal rearrangements, duplications, and non-conserved minichromosomes.
Training spatial transcriptomic foundation models alongside matched tissue images does not consistently improve their ability to transfer cell annotations across donors, according to benchmarks across three models.
Machine-learning analysis of temporal changes across the cell-surface proteome identified the enzyme ALPP as an adaptive target that pancreatic tumors produce in response to KRAS inhibition.
Researchers have developed mmFEA, a computational framework that estimates relative metabolic reaction rates by combining gene expression or protein abundance with metabolite measurements through Michaelis-Menten enzyme kinetics.
Researchers applied ChromBPNet, a deep learning model trained on single-cell chromatin accessibility profiles, to evaluate how noncoding type 1 diabetes variants alter gene regulation in pancreatic islets.
A computational method named SpaReg aligns serial tissue slices to build three-dimensional tissue maps across histology, spatial proteomics, and spatial transcriptomics while correcting for physical tears and distortions.
TxCyto uses deep learning to estimate the signaling activity of 645 cytokines, which are immune signaling proteins, directly from standard RNA sequencing data rather than direct laboratory measurement.
Researchers have developed spEnhance, a computational framework that sharpens low-resolution spatial sequencing data by combining tissue images with single-cell reference maps and gene co-expression patterns.