Genetics

Deep learning-based identification of genetic variants: application to Alzheimer's disease classification.

Deep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Deep learning is challenging in genome-wide association studies (GWAS) with high-dimensional genomic data. Here we propose a novel three-step approach (SWAT-CNN) for identification of genetic variants using deep learning to identify phenotype-related single nucleotide polymorphisms...

BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches.

N6-methyladenine (6mA) is associated with important roles in DNA replication, DNA repair, transcription, regulation of gene expression. Several experimental methods were used to identify DNA modifications. However, these experimental methods are costly and time-consuming. To detect the 6mA and complement these shortcomings of experimental methods, we proposed a novel, deep leaning approach called ...

[Research progress of micronucleus visualization analysis and artificial intelligence detection strategy].

The micronucleomics test can comprehensively display a variety of harmful endpoints, such as DNA damage and repair, chromosome breakage or loss and ce...

DeepSVP: integration of genotype and phenotype for structural variant prioritization using deep learning.

MOTIVATION: Structural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex an...

A deep learning framework for characterization of genotype data.

Dimensionality reduction is a data transformation technique widely used in various fields of genomics research. The application of dimensionality redu...

[Artificial intelligence-based fluorescence method versus traditional flow cytometry in detection of sperm DNA fragmentation index].

OBJECTIVE: To compare the result of the artificial intelligence (AI) recognition-based fluorescence method and that of traditional flow cytometry in t...

Universal encoding of pan-cancer histology by deep texture representations.

Cancer histological images contain rich biological and clinical information, but quantitative representation can be problematic and has prevented the ...

Diffractive deep neural network adjoint assist or (DNA): a fast and efficient nonlinear diffractive neural network implementation.

The recent advent of diffractive deep neural networks or DNNs has opened new avenues for the design and optimization of multi-functional optical mater...

[Unsupervised deep learning for identifying the O -carboxymethyl guanine by nanopore sequencing].

O -carboxymethyl guanine(O -CMG) is a highly mutagenic alkylation product of DNA that causes gastrointestinal cancer in organisms. Existing studies us...

UFold: fast and accurate RNA secondary structure prediction with deep learning.

For many RNA molecules, the secondary structure is essential for the correct function of the RNA. Predicting RNA secondary structure from nucleotide s...

Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes.

Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such...

DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data.

MOTIVATION: DNA methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detectio...

HDMC: a novel deep learning-based framework for removing batch effects in single-cell RNA-seq data.

MOTIVATION: With the development of single-cell RNA sequencing (scRNA-seq) techniques, increasingly more large-scale gene expression datasets become a...

Automated classification of cytogenetic abnormalities in hematolymphoid neoplasms.

MOTIVATION: Algorithms for classifying chromosomes, like convolutional deep neural networks (CNNs), show promise to augment cytogeneticists' workflows...

Automated extraction of genes associated with antibiotic resistance from the biomedical literature.

The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phen...

Uncertainty-aware and interpretable evaluation of Cas9-gRNA and Cas12a-gRNA specificity for fully matched and partially mismatched targets with Deep Kernel Learning.

The choice of guide RNA (gRNA) for CRISPR-based gene targeting is an essential step in gene editing applications, but the prediction of gRNA specifici...

A functional module states framework reveals transcriptional states for drug and target prediction.

Cells are complex systems in which many functions are performed by different genetically defined and encoded functional modules. To systematically und...

A comparison of deep learning-based pre-processing and clustering approaches for single-cell RNA sequencing data.

The emergence of single cell RNA sequencing has facilitated the studied of genomes, transcriptomes and proteomes. As available single-cell RNA-seq dat...

DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding.

Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular loca...

DeepMotifSyn: a deep learning approach to synthesize heterodimeric DNA motifs.

The cooperativity of transcription factors (TFs) is a widespread phenomenon in the gene regulation system. However, the interaction patterns between T...