Genetics

Clinical development of molecular residual disease (MRD) and multi-cancer early detection (MCED) using liquid biopsy multiomics with artificial intelligence (AI).

BACKGROUND: Early detection of cancer and precise recurrence monitoring remain major unmet needs in oncology. Conventional screening is limited to a few cancer types, leaving nearly half of cancers without established programs. Multi-cancer early detection (MCED) tests based on circulating tumor biomarkers have shown promise, but sensitivity for early-stage remains a challenge. In parallel, detect...

Transcriptomic analysis and machine learning have identified shared diagnostic genes and a possible mechanism linking bipolar disorder and epilepsy.

It is well known that bipolar disorder (BD) and epilepsy (EP) are common neurological diseases. The objective of this study was to screen for potential biomarkers applicable to the diagnosis of EP and BD. The gene expression profiles from both the BD and EP datasets were sourced from the Gene Expression Omnibus database. To pinpoint the core shared genes, we conducted differential expression analy...

The DNA-Based Disease Diagnosis Model with Strand Displacement Reaction.

MicroRNAs (miRNAs) serve as crucial biomarkers in disease diagnosis. Although silicon-based electronic machine learning models provide efficient means...

Development of a Classifier for Metabolic Subtypes of Nasopharyngeal Carcinoma to Guide Personalized Immunotherapy Strategies: Biomarker Analysis of the Phase III CONTINUUM and DIPPER Trials.

PURPOSE: Personalized immunotherapy strategies are urgently needed for patients with locoregionally advanced nasopharyngeal carcinoma (NPC). We aim to...

Cell-free DNA methylome and fragmentome analysis for relapse monitoring of Ewing sarcoma.

Liquid biopsies and cell-free DNA (cfDNA) offer minimally invasive methods for the diagnosis and monitoring of Ewing Sarcoma (EwS). EwS have a low tum...

Integrative multi-omics and machine learning identify mitochondrial biomarkers for pathogen-specific sepsis stratification and translational prioritization.

BACKGROUND: Sepsis is a leading cause of critical illness and mortality, yet substantial heterogeneity limits risk stratification and biomarker transl...

Integrated weighted gene co-expression network analysis and machine learning analysis identifies SEC14L5 as a potential biomarker for polycystic ovary syndrome.

BACKGROUND AND AIMS: To identify novel biomarkers and therapeutic targets for polycystic ovary syndrome (PCOS) using integrated bioinformatics approac...

From tests to truth: A misclassification-aware machine learning framework for estimating brucellosis seroprevalence in wild canids.

Brucellosis is an important zoonotic disease affecting humans, livestock, and wildlife, yet prevalence estimates in wild species are often underestima...

PAH-former: Transfer learning for efficient discovery of pulmonary arterial hypertension-associated genes.

BACKGROUND AND AIMS: Pulmonary arterial hypertension (PAH) is a severe disease with limited effective therapies, making the discovery of new therapeut...

Predictive divergence in machine learning models for clinical mortality risk: A multicohort study of covid-19 patients.

BACKGROUND: Machine learning (ML) algorithms are increasingly used in healthcare to support clinical decision-making. While models with similar overal...

MoGraphDRP: Multi-omics and graph fusion with bilinear attention for predicting drug sensitivity.

Accurate prediction of drug response in cancer cells is a fundamental step toward achieving precision medicine and designing personalized therapies. I...

Integrated analysis of therapeutic strategies and prognostic factors in advanced lung adenocarcinoma: Retrospective study with emphasis on gene assays, multimodality treatment approaches and predictive machine learning models.

Patients with advanced lung adenocarcinoma have a range of treatment options, including targeted therapy and gene assay-guided chemotherapy. The aim o...

Neuroimaging insights into the neurophysiological subtypes of major depressive disorder.

Major depressive disorder (MDD) is a highly heterogeneous condition that limits the reliability of symptom-based diagnosis and treatment selection. In...

Clinical application progress and prospects of non-invasive preimplantation genetic testing (niPGT): A review.

Preimplantation genetic testing (PGT) is a critical tool in reproductive medicine for selecting genetically healthy embryos, thereby reducing the risk...

Automated interpretable artificial intelligence genomic prediction with AIGP.

Predicting phenotypes from genomic mutations remains a major genetic challenge. Traditional statistical methods (such as GBLUP and BayesR) have limita...

Functional Data Strengthen Clinical Validation of PhenoScore Phenotype-Guided AI for ANKRD11 Missense Variants.

KBG syndrome (KBGS, OMIM #148050) is a rare genetic disorder caused by heterozygous truncating or missense variants in the ANKRD11 gene or a deletion ...

DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning.

Despite the recent advancements driven by deep learning, de novo peptide sequencing is still constrained by incomplete peptide fragmentation and insuf...

A data-centric approach to detecting and mitigating demographic bias in pediatric mental health text.

BACKGROUND: Healthcare Artificial Intelligence (AI) offers transformative potential but often inherits biases from training data, worsening disparitie...

High MAP3K6 Expression in Spinal Cord Injury Tissues Enhances Neuronal Apoptosis: Insights from Multi-Omics Data Integrating WGCNA, Machine Learning, and Experimental Validation.

Spinal cord injury (SCI) is a major global health issue with severe complications, yet effective biomarkers remain elusive. We analyzed the GSE226238 ...

Pseudohypoxia and Family History Are Key Predictors of Severe Outcomes in Hereditary Pheochromocytoma and Paraganglioma Syndromes.

OBJECTIVE: Pheochromocytoma and paraganglioma (PPGL) have high genetic predisposition rates. In this multicenter study, we aimed to identify risk-modu...