Genetics

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

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De novo prediction of RNA-protein interactions with graph neural networks.

RNA-binding proteins (RBPs) are key co- and post-transcriptional regulators of gene expression, play...

Predicting genes associated with RNA methylation pathways using machine learning.

RNA methylation plays an important role in functional regulation of RNAs, and has thus attracted an ...

Haplotype and population structure inference using neural networks in whole-genome sequencing data.

Accurate inference of population structure is important in many studies of population genetics. Here...

Clinical and Biological Significances of a Ferroptosis-Related Gene Signature in Lung Cancer Based on Deep Learning.

Acyl-CoA synthetase long-chain family member 4 (ACSL4) has been linked to the occurrence of tumors a...

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification...

Therapy resistance mechanisms in hematological malignancies.

Hematologic malignancies are model diseases for understanding neoplastic transformation and serve as...

LTPConstraint: a transfer learning based end-to-end method for RNA secondary structure prediction.

BACKGROUND: RNA secondary structure is very important for deciphering cell's activity and disease oc...

DeepBSA: A deep-learning algorithm improves bulked segregant analysis for dissecting complex traits.

Bulked segregant analysis (BSA) is a rapid, cost-effective method for mapping mutations and quantita...

A survey on gene expression data analysis using deep learning methods for cancer diagnosis.

Gene Expression Data is the biological data to extract meaningful hidden information from the gene d...

Path planning for autonomous mobile robots using multi-objective evolutionary particle swarm optimization.

In this article, a new path planning algorithm is proposed. The algorithm is developed on the basis ...

Experimental exploration of a ribozyme neutral network using evolutionary algorithm and deep learning.

A neutral network connects all genotypes with equivalent phenotypes in a fitness landscape and plays...

A hybrid model integrating long short-term memory with adaptive genetic algorithm based on individual ranking for stock index prediction.

Modeling and forecasting stock prices have been important financial research topics in academia. Thi...

A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies.

We present deep learning-based approaches for exploring the complex array of morphologies exhibited ...

Attention Mask R-CNN with edge refinement algorithm for identifying circulating genetically abnormal cells.

Recent studies have suggested that circulating tumor cells with abnormalities in gene copy numbers i...

Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders.

There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in ...

Model-free prediction test with application to genomics data.

Testing the significance of predictors in a regression model is one of the most important topics in ...

Biomarker identification by reversing the learning mechanism of an autoencoder and recursive feature elimination.

RNA-Seq has made significant contributions to various fields, particularly in cancer research. Recen...

iDRBP-ECHF: Identifying DNA- and RNA-binding proteins based on extensible cubic hybrid framework.

Proteins interact with nucleic acids to regulate the life activities of organisms. Therefore, how to...

Identification of Human Cell Cycle Phase Markers Based on Single-Cell RNA-Seq Data by Using Machine Learning Methods.

The cell cycle is composed of a series of ordered, highly regulated processes through which a cell g...

De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks.

With the emergence of high throughput single cell techniques, the understanding of the molecular and...

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