Genetics

Molecular Stratification of Antiphospholipid Syndrome Through Integrative Analysis of the Whole-Blood RNA Transcriptome.

OBJECTIVE: Antiphospholipid syndrome (APS) is a thromboinflammatory disorder characterized by clinical and mechanistic heterogeneity that complicates early diagnosis and hinders targeted treatment. We aimed to identify distinct molecular endotypes among antiphospholipid antibody (aPL)-positive patients using whole-blood transcriptomics. METHODS: Whole-blood RNA sequencing was performed on 174 aPL-...

Computational modeling and optimization of scFv-based receptors to support CAR-T design targeting CD19 for enhanced binding robustness and reduced off-target propensity.

Chimeric Antigen Receptor T-cell (CAR-T) therapy has revolutionized the treatment of B-cell malignancies, with CD19 being a primary target due to its stable expression in lymphomas. However, current CAR-T therapies face challenges related to antigen escape, treatment resistance, and toxicity. In this study, we employed a computational approach to design and optimize scFv-based receptors to support...

Interpretable prognostic modeling of glioblastoma using cross-cohort transcriptome integration and machine learning approaches.

Glioblastoma (GBM) is an aggressive brain tumor with highly variable patient outcomes due to pronounced molecular heterogeneity. Prognosis remains dis...

Best Practices for Developing Linear Models With Multiple Explanatory Variables.

Linear models, including t-test, ANOVA, regression, ANCOVA, and generalized linear models, are foundational tools in statistical analysis. For large d...

Protocol to perform cell-type-specific transcriptome-wide association study using scPrediXcan framework.

The scPrediXcan framework enables cell-type-specific transcriptome-wide association studies (TWASs) by integrating deep learning-based prediction of g...

NanoLoop: A Deep Learning Framework Leveraging Nanopore Sequencing for Chromatin Loop Prediction.

Chromatin loops play a crucial role in gene regulation and cellular function, providing key insights into understanding the 3D structure of the genome...

Potential mechanisms of Si-Wu-Tang against esophageal squamous cell carcinoma: A machine learning pharmacological study.

The purpose of this study is to explore the potential mechanism of Si-Wu-Tang (SWT) against esophageal squamous cell carcinoma (ESCC). Initially, 18 a...

Machine learning for predicting CKD stages in patients with autosomal dominant polycystic kidney disease: a nationwide cohort study in Japan.

Interpreting cancer genetics through a two-step "evolutionary cascade hypothesis": bridging neutral and selective perspectives.

BACKGROUND: DNA mutations are the fundamental engines of cancer, driving its initiation and progression. The forces that fuel malignancy are also the ...

eccDNA2Ca: an ensemble deep learning framework for interpretable prediction of cancer-associated extrachromosomal circular DNA.

BACKGROUND: Extrachromosomal circular DNA (eccDNA) is increasingly recognized as a critical driver of oncogene amplification, therapeutic resistance, ...

An integrative genomic framework to identify remote ischemic preconditioning-responsive genes associated with stroke risk.

BACKGROUND: Remote ischemic preconditioning (RIPC) is a promising, non-invasive strategy for reduction in ischemic injury after stroke, yet its molecu...

Natural and Artificial Hematopoietic Cell Reprogramming.

Hematopoietic cell reprogramming, defined as the conversion from one hematopoietic or immune cell type into another, has been shown to hold transforma...

Prediction model for ctDNA detectability in liquid comprehensive genomic profiling of advanced pancreatic cancer based on Japanese real-world data.

BACKGROUND: Circulating tumor DNA (ctDNA) is sometimes undetectable in liquid comprehensive genomic profiling (CGP) of advanced-stage pancreatic cance...

Start4All protocol for a Bayesian cost-effectiveness model of tuberculosis screening and diagnosis in seven high burden low-income and middle-income countries.

INTRODUCTION: High costs of screening and diagnostic tests remain a major barrier to timely tuberculosis (TB) identification in resource-limited setti...

PlantCTCIP: Chromatin Interaction Prediction Using Convolutional Neural Network and Transformer in Plants.

Chromatin interactions establish spatial proximity between distant regulatory elements and their target genes, significantly influencing gene expressi...

Diagnostic model and identification of IGFBP2 as an aging-related biomarker and therapeutic target for systemic sclerosis-associated interstitial lung disease through integrated bioinformatics and machine learning approaches.

BACKGROUND: Cellular senescence is a critical contributor to the pathogenesis of systemic sclerosis-associated interstitial lung disease (SSc-ILD). Ho...

AP-Lab: An AI-Driven Autonomous Pilot-Scale Platform Bridging Materials Discovery and Industrial Manufacturing.

Artificial intelligence (AI) has accelerated materials discovery, yet its translation to industrial manufacturing remains limited due to two critical ...

Association of Circulating T Cell and Tumor Microenvironment Profiles with Immune Checkpoint Blockade Outcome in Sarcoma.

PURPOSE: Immune checkpoint blockade (ICB) benefits only a subset of sarcoma patients. Biomarkers of response and resistance are needed to help guide p...

Ancestry-Associated Performance Variability of Open-Source AI Models for EGFR Prediction in Lung Cancer.

IMPORTANCE: Artificial intelligence (AI) models are emerging as rapid, low-cost tools for predicting targetable genomic alterations directly from rout...

Comprehensive Serum Proteomics Uncovers Epigenetic and Inflammatory Pathways Underlying Primary Open-Angle Glaucoma.

Glaucoma is a leading cause of irreversible blindness, driven by progressive Retinal ganglion cell loss and optic nerve degeneration. The molecular me...