Genetics

Relationship Between Gene Expression and Drug Response in Triple-Negative Breast Cancer: Leveraging Single-Cell RNA Sequencing and Machine Learning to Identify Biomarker Profiles

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by limited therapeutic options and poor prognosis. To address these challenges, we combined single-cell RNA sequencing (scRNA-seq) data with advanced machine learning techniques to find biomarkers that predict treatment response. Using tumor and blood samples from TNBC patients treated with either paclitaxel alone or in co...

REMAG: recovery of eukaryotic genomes from metagenomic data using contrastive learning

Metagenome-assembled genomes (MAGs) are central to exploring microbial communities. Yet, despite the relevance of protists and fungi to diverse ecosystems, eukaryotic MAG recovery lags behind that of prokaryotes. A major bottleneck is that most state-of-the-art binning pipelines exclusively rely on prokaryotic single-copy core gene reference databases and are optimized for smaller genomes. To addr...

Contact-Guided 3D Genome Structure Generation of E. coli via Diffusion Transformers

In this study, we present a conditional diffusion-transformer framework for generating ensembles of three-dimensional Escherichia coli genome conforma...

FusionRegister: Every Infrared and Visible Image Fusion Deserves Registration

Spatial registration across different visual modalities is a critical but formidable step in multi-modality image fusion for real-world perception. Al...

Layer-wise Instance Binding for Regional and Occlusion Control in Text-to-Image Diffusion Transformers

Region-instructed layout control in text-to-image generation is highly practical, yet existing methods suffer from limitations: (i) training-based app...

Cancer genomic profiling predicts pathogenicity of BRCA1 and BRCA2 variants

Accurate classification of BRCA1 and BRCA2 variants is essential for cancer risk assessment and therapy selection, yet over one-third remain variants ...

Popformer: Learning general signatures of positive selection with a self-supervised transformer

Understanding natural selection can help shed light on the genetics underpinning adaptive evolution. The widespread availability of large-scale human ...

Discovery of a phenazine thiol conjugase from sparse data using genome-informed machine learning

Machine learning has enabled powerful biological discoveries using models trained on large datasets. However, for many important biological questions,...

Circular RNA identification using a genomic language model and a small number of authenticated examples

Genomic language models (gLMs) hold great promise for deciphering biological sequences, yet their effectiveness is hindered by the limited number of e...

Phenotypic reversion and target prioritization for cellular inflammation via representation learning with foundation models

The identification of genetic perturbations that can reverse disease-associated cellular phenotypes toward a healthy state is a central challenge in e...

dAMN: a genome scale neural-mechanistic hybrid model to predict bacterial growth dynamics

This study presents dAMN, a hybrid neural-mechanistic model that integrates neural networks with genome-scale dynamic flux balance analysis (dFBA) to ...

What Do Biological Foundation Models Compute? Sparse Autoencoders from Feature Recovery to Mechanistic Interpretability

Foundation models trained on protein and DNA sequences are increasingly deployed for variant interpretation, drug design, and gene regulation predicti...

Optimal spatial release strategies for confined gene drives and Wolbachia

Gene drives are genetic elements that can rapidly spread through populations, offering potential solutions for controlling disease vectors and pests. ...

Machine Learning for analysis of Multiple Sclerosis cross-tissue bulk and single-cell transcriptomics data

Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system whose molecular mechanisms remain incompletely understood. In th...

Automated machine learning of echocardiographic strain enables identification of early myocardial changes in pre-symptomatic TTR carriers

Objectives: To identify unique echocardiographic signatures associated with TTR+ carrier status preceding onset of cardiac amyloidosis. Background: Ca...

Genome-wide classification of tumor-derived reads from bulk long-read sequencing

DNA extracted from tissue samples typically derive from of a complex mixture of cell types. Without single cell analysis, it has been generally imposs...

Leveraging publicly available datasets and machine learning approaches for predicting the health benefits of fermented foods

Fermented foods are an ancient, near universal component of human dietary culture and are increasingly recognized for their health benefits. Bioactive...

A Machine Learning Framework for Serogroup Classification of pathogenic species of Leptospira Based on rfb Locus Profiles

Leptospira is a highly diverse genus traditionally classified by serological assays into more than 30 serogroups and over 300 serovars. However, this ...

Tabular foundation model predicts alternative lengthening of telomeres (ALT) and identifies SMARCAL1 as a target in ALT-driven cancers

Alternative lengthening of telomeres (ALT) is a telomerase-independent pathway used by aggressive cancers to maintain their replicative immortality. B...

Massive-scale single-nucleus multi-omics identifies novel rare noncoding drivers of Parkinson's disease

Most genetic variants contributing to complex diseases reside in the noncoding genome. While common variants uncovered by genome-wide association stud...