Genetics

CEP-IP: An explainable framework for cell subpopulation identification in single-cell transcriptomics.

BACKGROUND AND OBJECTIVE: Single-cell RNA sequencing (scRNA-seq) frameworks lack explainable approaches for identifying cell subpopulations harboring strong pairwise monotonic gene-module relationships between a gene of interest (GOI) and its co-expressed genes. In this study, CEP-IP is introduced as a novel explainable machine learning framework to address this gap. METHODS: Prostate cancer (PCa)...

A Reinforcement Learning-Guided Genetic Algorithm Integrating Medicinal Chemistry-Inspired Molecular Transformations.

Achieving optimal target activity while maintaining synthetic accessibility and drug-likeness represents a major challenge in computational drug discovery. Existing de novo generative models often yield chemically invalid or synthetically intractable structures and struggle to optimize multiple objectives simultaneously. Here, we introduce ALCHIMIA, an interpretable hybrid framework combining rein...

Small RNAs as systemic signals in plant defense: mechanisms, challenges, and future directions.

Small RNAs (sRNAs) have emerged as central regulators of gene expression, coordinating development, stress responses, and immunity across plant tissue...

Hybrid experimental-machine learning framework for media optimization enhances antioxidant production in Micrococcus endophyticus SS-1.

Culture medium composition is a key determinant of microbial antioxidant production, yet systematic optimization remains challenging due to nonlinear ...

Bridging In Silico Design and Experimental Validation: Virtual Screening and In Vitro Assessment of Biomimetic Anticancer Peptides Inspired by Horseshoe Crab Hemolymph Proteins.

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, necessitating the development of more selective and safer the...

SaintGSE: Transformer-based efficient and explainable gene set enrichment analysis.

OBJECTIVE: Gene set enrichment analysis (GSEA) is widely used to interpret genome-wide expression data, but pathway inference from differentially expr...

An ensemble pipeline, PhageHost, for phage tail fiber discovery and accurate Klebsiella pneumoniae host prediction using protein language models.

The high specificity of phages toward their hosts holds great promise for phage therapy while posing a challenge to computational prediction approache...

In silico virtual knockout identifies PXDNL as a fibroblast-specific driver of sarcopenia and GABA as a potential modulator.

Sarcopenia lacks causal mechanisms and translatable targets. We integrated virtual gene knockout with multi-omics (n = 238 biopsies, 5 GEO cohorts) an...

Deep learning and cryogenic electron microscopy modeling for gene editing dynamics.

Advances in cryogenic electron microscopy (cryo-EM) data modeling and deep learning are reshaping our ability to interrogate and engineer genome-editi...

Rapid customization of base editors via machine learning-powered combinatorial mutagenesis.

Being able to control the complementarity and hindrance between target DNA and base editor proteins enables precise, bystander-free editing. Here, we ...

Noninvasive Prediction of Bone Metastasis-Free Survival in Lung Adenocarcinoma Using Interpretable CT-based Deep Learning Model.

RATIONALE AND OBJECTIVES: Preoperatively identifying patients at high risk of bone metastasis (BM) remains challenging in resectable lung adenocarcino...

MiSiPi.Rna: an integrated tool for characterizing small regulatory RNA processing.

Argonaute proteins mediate gene silencing via small regulatory RNAs that are generated by distinctive biogenesis pathways. In animals, three main clas...

Integrating epigenetic and immune biomarkers to enable dynamic precision therapy in triple-negative breast cancer.

Mamba-ACP: a Hybrid State-Space and Transformer Framework for Interpretable Anticancer Peptide Prediction.

Anticancer peptides (ACPs) represent a promising class of therapeutic agents that selectively destroy cancer cells while sparing healthy tissues. Desp...

Artificial Intelligence Design for Race-Based Prostate Cancer Stage Classification With Multilayer Perceptron: Feature Selection Optimization Approach.

BACKGROUND: Prostate cancer progression exhibits significant variability influenced by biological and racial factors. DNA methylation profiling has sh...

Circulating extracellular vesicle long RNA profiling combined with machine learning unveils novel diagnostic signature and molecular features in chronic pancreatitis.

BACKGROUND: No clinically useful non-invasive biomarkers have been developed for diagnosis of chronic pancreatitis (CP), and molecular features of CP ...

Cell-type-specific causal effects of CEBPD in CD8 + S100B + Tcells and ZFP36 in monocytes modulate the protective and risk phenotypes in psoriasis.

Psoriasis is a chronic immune-mediated inflammatory skin disease characterized by dysregulated keratinocyte proliferation, immune cell infiltration, a...

Competing gene regulatory networks drive naive and memory B cell differentiation.

Elucidating the gene regulatory networks (GRNs) that control human B cell differentiation is crucial for understanding immune responses to infection, ...

Deep learning inference of cell type-specific gene expression from breast tumor histopathology.

Cell type-specific gene expression from single-cell RNA sequencing (RNA-seq) is valuable for breast cancer precision oncology but available cohorts ar...

upsML: A high-accuracy machine learning classifier for predicting Plasmodium falciparum var gene upstream groups.

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), encoded by the hypervariable var gene family, is central to malaria pathogenesis, influ...