Genetics

m6A-SPP: Identification of RNA N6-methyladenosine modification sites through multi-source biological features and a hybrid deep learning architecture.

The N6-methyladenosine(m6A) modification plays crucial regulatory roles in various biological processes including gene expression regulation, RNA stability, splicing, and translation. Accurate prediction of m6A modification sites is essential for understanding their biological functions and implications in diseases. To address this, we introduce m6A-SPP, a novel deep learning framework for predict...

Predicting Mutation-Disease Associations Through Protein Interactions Via Deep Learning.

Disease is one of the primary factors affecting life activities, with complex etiologies often influenced by gene expression and mutation. Currently, wet lab experiments have analyzed the mechanisms of mutations, but these are usually limited by the costs of wet experiments and constraints in sample types and scales. Therefore, this paper constructs a real-world mutation-induced disease dataset an...

CR-deal: Explainable Neural Network for circRNA-RBP Binding Site Recognition and Interpretation.

circRNAs are a type of single-stranded non-coding RNA molecules, and their unique feature is their closed circular structure. The interaction between ...

Deep Learning-Enhanced Hand-Driven Microfluidic Chip for Multiplexed Nucleic Acid Detection Based on RPA/CRISPR.

The early detection of high-risk human papillomavirus (HR-HPV) is crucial for the assessment and improvement of prognosis in cervical cancer. However,...

Immunohistochemistry and machine learning study of DNA replication-associated proteins in uterine epithelial tumors and precursor lesions.

Endometrioid adenocarcinoma (EA) has been on the increase in recent years in developed countries. Early detection of endometrioid adenocarcinoma in th...

Deep learning and genomic best linear unbiased prediction integration: An approach to identify potential nonlinear genetic relationships between traits.

Genomic prediction (GP) aims to predict the breeding values of multiple complex traits, usually assumed to be multivariate normally distributed by the...

Pathway Enrichment-Based Unsupervised Learning Identifies Novel Subtypes of Cancer-Associated Fibroblasts in Pancreatic Ductal Adenocarcinoma.

Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq)...

Explainable artificial intelligence in forensic DNA analysis: Alleles identification in challenging electropherograms using supervised machine learning methods.

Challenging samples in capillary electrophoresis (CE)-based short tandem repeat (STR) analysis often produce artefactual signals that cannot be comple...

GeneDX-PBMC: An adversarial autoencoder framework for unlocking Alzheimer's disease biomarkers using blood single-cell RNA sequencing data.

OBJECTIVE: To identify blood-based biomarkers and therapeutic targets for Alzheimer's disease (AD) by leveraging single-cell RNA sequencing (scRNA-seq...

[Rapid identification and analysis of hemoglobin isoelectric focusing electrophoresis images based on deep learning].

Gel electrophoresis is used to separate and analyze macromolecules （such as DNA， RNA， and proteins） and their fragments， and highly reproducible and e...

Machine Learning-Assisted Analysis of the Oral Cancer Immune Microenvironment: From Single-Cell Level to Prognostic Model Construction.

Oral cancer is among the most prevalent malignant tumours worldwide; prognosis can be affected by several factors, including molecular subtypes, immun...

DRBP-EDP: classification of DNA-binding proteins and RNA-binding proteins using ESM-2 and dual-path neural network.

Regulation of DNA or RNA at the transcriptional, post-transcriptional, and translational levels are key steps in the central dogma of molecular biolog...

Learning to utilize internal protein 3D nanoenvironment descriptors in predicting CRISPR-Cas9 off-target activity.

Despite advances in determining the factors influencing cleavage activity of a CRISPR-Cas9 single guide RNA (sgRNA) at an (off-)target DNA sequence, a...

Advancing the frontier of artificial intelligence on emerging technologies to redefine cancer diagnosis and care.

BACKGROUND: Artificial Intelligence (AI) is capable of revolutionizing cancer therapy and advancing precision oncology via integrating genomics data a...

Digenic variant interpretation with hypothesis-driven explainable AI.

The digenic inheritance hypothesis holds the potential to enhance diagnostic yield in rare diseases. Computational approaches capable of accurately in...

Improving prediction of bacterial sRNA regulatory targets with expression data.

Small regulatory RNAs (sRNAs) are widespread in bacteria. However, characterizing the targets of sRNA regulation in a way that scales with the increas...

Integrative PPI network and random forest analysis identifies KIF4A, TOP2A, and ASPM protein macromolecules as novel protein biomarkers in renal carcinoma pathogenesis.

The pathogenesis of kidney cancer is not fully understood, so there is an urgent need to identify new biomarkers to improve diagnosis and treatment. T...

MRI-based multimodal AI model enables prediction of recurrence risk and adjuvant therapy in breast cancer.

Timely intervention and improved prognosis for breast cancer patients rely on early metastasis risk detection and accurate treatment predictions. This...

The predictive power of profiling the DNA methylome in human health and disease.

Early and accurate diagnosis significantly improves the chances of disease survival. DNA methylation (5mC), the major DNA modification in the human ge...

Recent advances in microbial synthesis of polyphenols.

Polyphenols are plant-derived secondary metabolites known for their antioxidants, anti-inflammatory, and antimicrobial properties, with flavonoids bei...