Genetics

Latest AI and machine learning research in genetics for healthcare professionals.

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Alzheimer's Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery.

Alzheimer's disease (AD) is a major neurodegenerative dementia, with its complex pathophysiology cha...

Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma.

To integrate machine learning and multiomic data on lactylation-related genes (LRGs) for molecular t...

Application of machine learning and genomics for orphan crop improvement.

Orphan crops are important sources of nutrition in developing regions and many are tolerant to bioti...

Analysis of the genetic basis of fiber-related traits and flowering time in upland cotton using machine learning.

Cotton is an important crop for fiber production, but the genetic basis underlying key agronomic tra...

Immunolipid magnetic bead-based circulating tumor cell sorting: a novel approach for pathological staging of colorectal cancer.

OBJECTIVE: This study aimed to assess whether circulating tumor cells (CTCs) from colorectal cancer ...

Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.

INTRODUCTION: Type 2 diabetes (T2D) is a complex metabolic disorder with significant global health i...

KanCell: dissecting cellular heterogeneity in biological tissues through integrated single-cell and spatial transcriptomics.

KanCell is a deep learning model based on Kolmogorov-Arnold networks (KAN) designed to enhance cellu...

Advancing DNA Structural Analysis: A SERS Approach Free from Citrate Interference Combined with Machine Learning.

Surface-enhanced Raman spectroscopy (SERS) has become an indispensable tool for biomolecular analysi...

Identification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approach.

Monkeypox virus (MPXV), a zoonotic pathogen, re-emerged in 2022 with the Clade IIb variant, raising ...

Identifying candidate RNA-seq biomarkers for severity discrimination in chemical injuries: A machine learning and molecular dynamics approach.

INTRODUCTION: Biomarkers play a crucial role across various fields by providing insights into biolog...

Artificial intelligence and machine learning in cell-free-DNA-based diagnostics.

The discovery of circulating fetal and tumor cell-free DNA (cfDNA) molecules in plasma has opened up...

Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach.

Application of single-cell/nucleus genomic sequencing to patient-derived tissues offers potential so...

Modeling gene interactions in polygenic prediction via geometric deep learning.

Polygenic risk score (PRS) is a widely used approach for predicting individuals' genetic risk of com...

Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach.

Anomalous chromosomes are the cause of genetic diseases such as cancer, Alzheimer's, Parkinson's, ep...

Paying attention to the SARS-CoV-2 dialect : a deep neural network approach to predicting novel protein mutations.

Predicting novel mutations has long-lasting impacts on life science research. Traditionally, this pr...

Genomic and algorithm-based predictive risk assessment models for benzene exposure.

AIM: In this research, we leveraged bioinformatics and machine learning to pinpoint key risk genes a...

Association of and gene polymorphisms and ERAP2 protein with the susceptibility and severity of rheumatoid arthritis in the Ukrainian population.

INTRODUCTION: Rheumatoid arthritis (RA) is a long-term autoimmune disorder that primarily affects jo...

Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat ( L.).

Phytochromes are essential photoreceptors in plants that sense red and far-red light, playing a vita...

A scalable tool for analyzing genomic variants of humans using knowledge graphs and graph machine learning.

Advances in high-throughput genome sequencing have enabled large-scale genome sequencing in clinical...

Unlocking autism's complexity: the e Initiative's path to comprehensive motor function analysis.

The long-standing practice of using manualized inventories and observational assessments to diagnose...

General structure-activity relationship models for the inhibitors of Adenosine receptors: A machine learning approach.

Adenosine receptors (A, A, A, A) play critical roles in cellular signaling and are implicated in var...

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