Genetics

Bioinformatics and machine learning approaches reveal key genes and underlying molecular mechanisms of atherosclerosis: A review.

Atherosclerosis (AS) causes thickening and hardening of the arterial wall due to accumulation of extracellular matrix, cholesterol, and cells. In this study, we used comprehensive bioinformatics tools and machine learning approaches to explore key genes and molecular network mechanisms underlying AS in multiple data sets. Next, we analyzed the correlation between AS and immune fine cell infiltrati...

BertSNR: an interpretable deep learning framework for single-nucleotide resolution identification of transcription factor binding sites based on DNA language model.

MOTIVATION: Transcription factors are pivotal in the regulation of gene expression, and accurate identification of transcription factor binding sites (TFBSs) at high resolution is crucial for understanding the mechanisms underlying gene regulation. The task of identifying TFBSs from DNA sequences is a significant challenge in the field of computational biology today. To address this challenge, a v...

Advancing mRNA subcellular localization prediction with graph neural network and RNA structure.

MOTIVATION: The asymmetrical distribution of expressed mRNAs tightly controls the precise synthesis of proteins within human cells. This non-uniform d...

Peptide Sequencing Via Protein Language Models

We introduce a protein language model for determining the complete sequence of a peptide based on measurement of a limited set of amino acids. To da...

Refinement of genetic variants needs attention

Variant calling refinement is crucial for distinguishing true genetic variants from technical artifacts in high-throughput sequencing data. Manual r...

Jailbreaking Text-to-Image Models with LLM-Based Agents

Recent advancements have significantly improved automated task-solving capabilities using autonomous agents powered by large language models (LLMs)....

An Integral R-Banded Karyotype Analysis System of Bone Marrow Metaphases Based on Deep Learning.

CONTEXT.—: Conventional karyotype analysis, which provides comprehensive cytogenetic information, plays a significant role in the diagnosis and risk s...

Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data.

BACKGROUND: Hemoglobinopathies, the most common inherited blood disorder, are frequently underdiagnosed. Early identification of carriers is important...

Integrating single-cell transcriptomics and machine learning to predict breast cancer prognosis: A study based on natural killer cell-related genes.

Breast cancer (BC) is the most commonly diagnosed cancer in women globally. Natural killer (NK) cells play a vital role in tumour immunosurveillance. ...

Protein multi-level structure feature-integrated deep learning method for mutational effect prediction.

Through iterative rounds of mutation and selection, proteins can be engineered to enhance their desired biological functions. Nevertheless, identifyin...

A novel sand cat swarm optimization algorithm-based SVM for diagnosis imaging genomics in Alzheimer's disease.

In recent years, brain imaging genomics has advanced significantly in revealing underlying pathological mechanisms of Alzheimer's disease (AD) and pro...

Machine learning and experimental validation of novel biomarkers for hypertrophic cardiomyopathy and cancers.

Hypertrophic cardiomyopathy (HCM) is a hereditary cardiac disorder marked by anomalous thickening of the myocardium, representing a significant contri...

Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self-supervised deep learning.

BACKGROUND: Tumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC...

Exploiting Metabolic Defects in Glioma with Nanoparticle-Encapsulated NAMPT Inhibitors.

The treatment of primary central nervous system tumors is challenging due to the blood-brain barrier and complex mutational profiles, which is associa...

Self-Sovereign Identity for Consented and Content-Based Access to Medical Records using Blockchain

Electronic Health Records (EHRs) and Medical Data are classified as personal data in every privacy law, meaning that any related service that includ...

Are gene-by-environment interactions leveraged in multi-modality neural networks for breast cancer prediction?

Polygenic risk scores (PRSs) can significantly enhance breast cancer risk prediction when combined with clinical risk factor data. While many studie...

A Scalable Tool For Analyzing Genomic Variants Of Humans Using Knowledge Graphs and Machine Learning

The integration of knowledge graphs and graph machine learning (GML) in genomic data analysis offers several opportunities for understanding complex...

BERT and LLMs-Based avGFP Brightness Prediction and Mutation Design

This study aims to utilize Transformer models and large language models (such as GPT and Claude) to predict the brightness of Aequorea victoria gree...

Multi-modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification

Schizophrenia (SZ) is a severe brain disorder marked by diverse cognitive impairments, abnormalities in brain structure, function, and genetic facto...

Interpreting artificial neural networks to detect genome-wide association signals for complex traits

Investigating the genetic architecture of complex diseases is challenging due to the multifactorial and interactive landscape of genomic and environ...