Genetics

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

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Showing 1009-1029 of 10,366 articles
Predicting cell properties with AI from 3D imaging flow cytometer data.

Predicting the properties of tissues or organisms from the genomics data is widely accepted by the m...

Key RNA-binding proteins in renal fibrosis: a comprehensive bioinformatics and machine learning framework for diagnostic and therapeutic insights.

BACKGROUND: Renal fibrosis is a critical factor in chronic kidney disease progression, with limited ...

RNA-protein interaction prediction using network-guided deep learning.

Accurate computational determination of RNA-protein interactions remains challenging, particularly w...

Breaking barriers: noninvasive AI model for BRAF mutation identification.

OBJECTIVE: BRAF is the most common mutation found in thyroid cancer and is particularly associated w...

Identifying RNA-small Molecule Binding Sites Using Geometric Deep Learning with Language Models.

RNAs are emerging as promising therapeutic targets, yet identifying small molecules that bind to the...

Cloning, Characterization, and Computer-Aided Evolution of a Thermostable Laccase of the DUF152 Family From Klebsiella michiganensis.

Bacterial laccases exhibit relatively high optimal reaction temperatures and possess a broad substra...

A deep-learning model for predicting tyrosine kinase inhibitor response from histology in gastrointestinal stromal tumor.

Over 90% of gastrointestinal stromal tumors (GISTs) harbor mutations in KIT or PDGFRA that can predi...

Interpretable AI for inference of causal molecular relationships from omics data.

The discovery of molecular relationships from high-dimensional data is a major open problem in bioin...

Machine learning-based integration develops a hypoxia-derived signature for improving outcomes in glioma.

The growth of glioma is frequently accompanied by a hypoxic microenvironment. Nevertheless, the clin...

A Perspective on Artificial Intelligence for Molecular Pathologists.

The widespread adoption of next-generation sequencing technology in molecular pathology has enabled ...

Integrating single-cell sequencing and machine learning to uncover the role of mitophagy in subtyping and prognosis of esophageal cancer.

Globally, esophageal cancer stands as a prominent contributor to cancer-related fatalities, distingu...

Functionally characterizing obesity-susceptibility genes using CRISPR/Cas9, in vivo imaging and deep learning.

Hundreds of loci have been robustly associated with obesity-related traits, but functional character...

Reliability-enhanced data cleaning in biomedical machine learning using inductive conformal prediction.

Accurately labeling large datasets is important for biomedical machine learning yet challenging whil...

metaCDA: A Novel Framework for CircRNA-Driven Drug Discovery Utilizing Adaptive Aggregation and Meta-Knowledge Learning.

In the emerging field of RNA drugs, circular RNA (circRNA) has attracted much attention as a novel m...

Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.

Machine learning (ML) is changing the world of computational protein design, with data-driven method...

Deep learning-based quick MLC sequencing for MRI-guided online adaptive radiotherapy: a feasibility study for pancreatic cancer patients.

One bottleneck of magnetic resonance imaging (MRI)-guided online adaptive radiotherapy is the time-c...

DeepInterAware: Deep Interaction Interface-Aware Network for Improving Antigen-Antibody Interaction Prediction from Sequence Data.

Identifying interactions between candidate antibodies and target antigens is a key step in developin...

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