Genetics

Application of Feature Selection and Deep Learning for Cancer Prediction Using DNA Methylation Markers.

DNA methylation is a process that can affect gene accessibility and therefore gene expression. In this study, a machine learning pipeline is proposed for the prediction of breast cancer and the identification of significant genes that contribute to the prediction. The current study utilized breast cancer methylation data from The Cancer Genome Atlas (TCGA), specifically the TCGA-BRCA dataset. Feat...

Deep learning-based approach to the characterization and quantification of histopathology in mouse models of colitis.

Inflammatory bowel disease (IBD) is a chronic immune-mediated disease of the gastrointestinal tract. While therapies exist, response can be limited within the patient population. Researchers have thus studied mouse models of colitis to further understand pathogenesis and identify new treatment targets. Flow cytometry and RNA-sequencing can phenotype immune populations with single-cell resolution b...

The Use of Thinking Visualization Techniques in College Teaching Based on Improved Genetic Algorithms.

Current educational resources do not maximize energy efficiency, and scientific and proven teaching methods are necessary for today's university educa...

iPromoter-CLA: Identifying promoters and their strength by deep capsule networks with bidirectional long short-term memory.

BACKGROUND AND OBJECTIVE: The promoter is a fragment of DNA and a specific sequence with transcriptional regulation function in DNA. Promoters are loc...

Automated filtering of genome-wide large deletions through an ensemble deep learning framework.

Computational methods based on whole genome linked-reads and short-reads have been successful in genome assembly and detection of structural variants ...

Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks.

Taxonomic classification, that is, the assignment to biological clades with shared ancestry, is a common task in genetics, mainly based on a genome si...

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, playing a crucial role in many biological processes. E...

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 we present HaploNet, a method for performing dime...

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 increasing interest in biology and drug discovery....

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 and is implicated in the ferroptosis process. Deep ...

Therapy resistance mechanisms in hematological malignancies.

Hematologic malignancies are model diseases for understanding neoplastic transformation and serve as prototypes for developing effective therapies. In...

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 occurrence. The first method which was used by the a...

mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning.

Colorectal cancer (CRC) is one of the most fatal cancers of the digestive system. Although cancer stem cells and metabolic reprogramming have an impor...

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 quantitative trait loci (QTLs) in animals and plants based...

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 dataset. This gene information is used for disease ...

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 of the algorithm for finding the best value using ...

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 an important role in the mutational robustness an...

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 by the opportunistic human pathogen Candida albica...

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 statistics. This problem is especially difficult w...

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 the genetics of complex traits, including psychiat...