Genetics

Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

Spatial transcriptomics technologies enable high-throughput quantification of gene expression at specific locations across tissue sections, facilitating insights into the spatial organization of biological processes. However, high costs associated with these technologies have motivated the development of deep learning methods to predict spatial gene expression from inexpensive hematoxylin and eosi...

Control of a Bi-Stable Genetic System via Parallelized Reinforcement Learning

Achieving real-time control of genetic systems is critical for improving the reliability, efficiency, and reproducibility of biological research and engineering. Yet the intrinsic stochasticity of these systems makes this goal difficult. Prior efforts have faced three recurring challenges: (a) predictive models of gene expression dynamics are often inaccurate or unavailable, (b) nonlinear dynamics...

Intra-genomic genes-to-genes correlation enables bacterial genome representation

The bacterial pan-genome consists of core genes shared by all members of a taxonomy and accessory genes found in only a subset. The correlation among ...

A Transparent and Generalizable Deep Learning Framework for Genomic Ancestry Prediction

Accurately capturing genetic ancestry is critical for ensuring reproducibility and fairness in genomic studies and downstream health research. This st...

Accurate and robust classification of Mycobacterium bovis-infected cattle using peripheral blood RNA-seq data

The zoonotic bacterium, Mycobacterium bovis, causes bovine tuberculosis (bTB) and is closely related to Mycobacterium tuberculosis, the primary cause ...

scXpand: Pan-cancer detection of T-cell clonal expansion from single-cell RNA sequencing without paired single-cell TCR sequencing

Advances in single-cell sequencing have enabled detailed characterization of T-cell clonal dynamics in cancer. However, analyses aiming to link transc...

Alzheimer’s subtypes A supervised, unsupervised, multimodal, multilayered embedded recursive (SUMMER) AI study

Since Alzheimer’s disease (AD) is a heterogeneous disease, different subtypes may have distinct biological, genetic, and clinical characteristics, req...

CoBRA: Compound Binding Site Prediction using RNA Language Model

Ribonucleic acid (RNA) molecules perform a variety of functions within cells and thus implicated in various human diseases such as cancer. The fact th...

multiDEGGs: a multi-omic differential network analysis package for biomarker discovery and predictive modeling

Modern clinical trials increasingly leverage high-throughput omic data for patient stratification and biomarker discovery. While traditional different...

Fourier transform infrared spectroscopy enables rapid species discrimination across Malassezia and strain-level typing in M. pachydermatis

Malassezia pachydermatis is a zoophilic yeast found on the skin and in the outer ear canal of many mammals. It normally maintains a commensal lifestyl...

Comparative Machine Learning Analysis of Saliva and Plaque Microbiomes in Children with Type 1 Diabetes

Type 1 diabetes (T1D) is associated with microbial dysbiosis. While most research has focused on the gut microbiome, limited data address the role of ...

Chromatin and gene-regulatory dynamics of human pulmogenesis by single cell multiomic sequencing

Human lung development is governed by complex gene regulatory networks that orchestrate cellular differentiation and organogenesis. We present a singl...

Machine learning and multi-omic analysis reveal contrasting recombination landscape of A and C subgenomes of winter oilseed rape

Meiotic recombination is essential for generating genetic diversity, driving plant evolution, and enabling crop improvement, yet its uneven distributi...

MetaChrome: An Open-Source, User-Friendly Tool for Automated Metaphase Chromosome Analysis

DNA Fluorescence In Situ Hybridization (FISH) is an essential technique to study chromosome biology and genetics, enabling precise visualization of sp...

Machine Learning Models Based on Histological Images from Healthy Donors Identify ImageQTLs and Predict Chronological Age

Histological images offer a wealth of data. Mining these data holds significant potential for enhancing disease diagnosis and prognosis, though challe...

Modulation of specific interactions within a viral fusion protein predicted from machine learning blocks membrane fusion

Enveloped viruses must enter host cells to initiate infections through a fusion process, during which the fusion proteins undergo significant and comp...

Lung Adenocarcinoma Just Desserts: An Expanding Pie of Activating Oncogenes or a Layer Cake of Integrated Alterations

The molecular landscape of lung adenocarcinoma (LUAD) is often summarized as a “pie chart” of driver oncogenes, suggesting identification and targetin...

Novel Machine Learning-based Approach to Identify Viral Biomarkers of Human Respiratory Emissions from Oral and Nasal Metagenomes

Humans spend approximately 90% of their lives in built environments, making virus transmission indoors a key determinant of health. Environmental samp...

Benchmarking large language models for genomic knowledge with GeneTuring

Large language models (LLMs) show promise in biomedical research, but their effectiveness for genomic inquiry remains unclear. We developed GeneTuring...

Data-efficient protein mutational effect prediction with weak supervision by molecular simulation and protein language models

Machine learning-based protein mutational effect prediction is widely used in protein engineering and pathogenicity prediction, but training data scar...