Genetics

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

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Deep learning based method for predicting DNA N6-methyladenosine sites.

DNA N6 methyladenine (6mA) plays an important role in many biological processes, and accurately iden...

Machine Learning-Driven Biomarker Discovery for Skeletal Complications in Type 1 Gaucher Disease Patients.

Type 1 Gaucher disease (GD1) is a rare, autosomal recessive disorder caused by glucocerebrosidase de...

MultiModRLBP: A Deep Learning Approach for Multi-Modal RNA-Small Molecule Ligand Binding Sites Prediction.

This study aims to tackle the intricate challenge of predicting RNA-small molecule binding sites to ...

Deep Learning Sequence Models for Transcriptional Regulation.

Deciphering the regulatory code of gene expression and interpreting the transcriptional effects of g...

Machine learning-guided multi-site combinatorial mutagenesis enhances the thermostability of pectin lyase.

Enhancing the thermostability of enzymes is crucial for industrial applications. Methods such as dir...

Enhancing schizophrenia phenotype prediction from genotype data through knowledge-driven deep neural network models.

This article explores deep learning model design, drawing inspiration from the omnigenic model and g...

Geometric deep learning of protein-DNA binding specificity.

Predicting protein-DNA binding specificity is a challenging yet essential task for understanding gen...

Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non-small-cell lung cancer.

Accurate survival prediction for Non-Small Cell Lung Cancer (NSCLC) patients remains a significant c...

Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning.

There remains a clinical need for better approaches to rapid drug susceptibility testing in view of ...

Utilizing Deep Neural Networks to Fill Gaps in Small Genomes.

With the widespread adoption of next-generation sequencing technologies, the speed and convenience o...

Towards key genes identification for breast cancer survival risk with neural network models.

Breast cancer, one common malignant tumor all over the world, has a considerably high rate of recurr...

Machine learning-driven diagnosis of multiple sclerosis from whole blood transcriptomics.

Multiple sclerosis (MS) is a neurological disorder characterized by immune dysregulation. It begins ...

Integrated machine learning screened glutamine metabolism-associated biomarker SLC1A5 to predict immunotherapy response in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) stands as one of the most prevalent malignancies. While PD-1 immune c...

GraphPro: An interpretable graph neural network-based model for identifying promoters in multiple species.

Promoters are DNA sequences that bind with RNA polymerase to initiate transcription, regulating this...

CRISPR-Enhanced Photocurrent Polarity Switching for Dual-lncRNA Detection Combining Deep Learning for Cancer Diagnosis.

Abnormal expression in long noncoding RNAs (lncRNAs) is closely associated with cancers. Herein, a n...

Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.

Integrating multi-omics data into predictive models has the potential to enhance accuracy, which is ...

Machine learning-based biomarker screening for acute myeloid leukemia prognosis and therapy from diverse cell-death patterns.

Acute myeloid leukemia (AML) exhibits pronounced heterogeneity and chemotherapy resistance. Aberrant...

Current genomic deep learning models display decreased performance in cell type-specific accessible regions.

BACKGROUND: A number of deep learning models have been developed to predict epigenetic features such...

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