Genetics

Recent Advances in the Development of CRISPR-Based Live-Cell Molecular Imaging and Sensing.

Visualizing genome organization and transcriptional dynamics with spatial and temporal precision in living cells is essential for elucidating gene regulation and chromatin-associated disease mechanisms, yet conventional methods confront a fundamental tension between endogenous-sequence targeting and live-cell compatibility. Operator-repressor systems require prior insertion of repetitive arrays at...

Decoding the role of chromatin context in the off-target effects of CRISPR gene editing with EGOLD.

Despite the power of CRISPR in genome editing, its clinical application is limited by off-target effects; these effects are currently difficult to evaluate at the genome level but are likely to involve chromatin context. Here, we developed the Endogenous Genome-wide Off-target Library Detection (EGOLD) method for high-throughput detection of off-target effects and identification of chromatin conte...

Single-cell and deep learning identify hypoxia-responsive lncRNAs predicting outcomes in colorectal cancer.

Emerging evidence highlights hypoxia-responsive long non-coding RNAs (lncRNAs) as potential modulators in tumor biology. In this study, we explored th...

Integrating scanning electron microscopy, explainable deep learning, and ITS sequencing for accurate identification in some species Geastrum.

The differentiation of species within the genus Geastrum remains a challenging task due to the strong morphological similarity among taxa and the limi...

Estimating genotype-tissue specific gene expression using hybrid deep learning.

Genotype-tissue expression profiles are critical for understanding how genetic variation influences gene regulation across tissues, yet they are often...

Integrative bioinformatic analysis prioritizes TIMP1 and FN1 as angiogenesis-related candidate genes in diabetic foot ulcers.

BACKGROUND: Diabetic foot ulcers (DFUs) represent a severe chronic complication of diabetes and are characterized by persistent impairment of wound he...

Glycolytic reprogramming in host response to Borrelia burgdorferi: A gene signature revealed by integrative bioinformatics analysis and machine learning.

Lyme disease (LD), a multifaceted condition caused by Borrelia burgdorferi (Bb), remains poorly understood, particularly regarding metabolic pathways....

Application of a multimodal MRI model integrating radiomics and habitat features for predicting glioma pathology and prognosis.

BACKGROUND: Accurate grading and prognostic assessment of glioma requires integrating key molecular biomarkers, including IDH mutation status and the ...

Integrative analysis of the mouse cecal microbiome across diet, age, and weight in the diverse BXD population.

BACKGROUND: The gut microbiota adapts to and shapes the host's metabolic state through affecting circulating metabolites and consequent gene regulator...

scArchon: a scalable benchmarking framework for assessing single-cell perturbation models.

BACKGROUND: The accurate prediction of cellular responses to perturbations, such as drug treatments, remains a pivotal challenge in single-cell transc...

Decoding bacterial transcriptional regulatory networks through integrated multi-omics datasets for artificial intelligence-driven design of programmable biological systems.

Advances in next-generation sequencing (NGS) have provided refined insight into bacterial transcriptional regulatory networks (TRNs) on a genome-wide ...

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer.

This study presents a novel integrative approach for the analysis of high-dimensional gene expression data, leveraging the complementary strengths of ...

Multi-omics precision diagnosis of brucellosis: Advances in biomarker discovery and clinical application.

Brucellosis, a neglected zoonosis caused by intracellular Brucella bacteria, remains a formidable global public health challenge, especially in develo...

Integrative Bioinformatics, Experimental Validation, and Interpretable Machine Learning Reveal Oxyresveratrol-Mediated Protection Against Cadmium-Induced Lung Adenocarcinoma-Related Transcriptional Dysregulation.

Cadmium (Cd) is a toxic heavy metal strongly implicated in lung adenocarcinoma (LUAD) through mechanisms involving oxidative stress, epigenetic dysreg...

Methodological Approaches to and Reported Performance of Applications of Automated Machine Learning in Diabetes Risk Prediction: Rapid Review.

BACKGROUND: Type 2 diabetes (T2D) is a complex, chronic condition that imposes a substantial burden on health care systems. Prevention and early detec...

Integrative bioinformatics analysis unveils neuro-cancer crosstalk-related genes and establishes prognostic risk model in Glioblastoma.

Neuro-cancer crosstalk plays an important role in the development and progression of Glioblastoma (GBM), but its specific mechanisms remain incomplete...

Development and Validation of a SHAP-Interpretable Machine Learning Model for Stroke Risk Prediction Using Circulating MicroRNA Biomarkers.

BACKGROUND/OBJECTIVE: Stroke remains a leading cause of morbidity and mortality worldwide. Circulating microRNAs (miRNAs) have emerged as promising no...

Machine learning empowers precise discovery of disease-resistance genes in plants.

Identifying plant disease-resistance genes is essential for understanding the plant immune system and accelerating the breeding of disease-resistant c...

Establishment of prognostic prediction model based on lipid metabolism related genes in esophageal squamous cell carcinoma by machine learning algorithms.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a major cause of cancer-related mortality worldwide, with a high prevalence and poor prognosi...

Precursor flat urothelial lesions: Molecular landscape, pathogenesis, and biological insights.

Flat urothelial lesions represent a diagnostically challenging and clinically significant spectrum in bladder pathology. Distinguishing urothelial car...