Genetics

Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers.

PURPOSE: Evaluation of genetic mutations in cancers is important because distinct mutational profiles help determine individualized drug therapy. However, molecular analyses are not routinely performed in all cancers because they are expensive, time-consuming and not universally available. Artificial intelligence (AI) has shown the potential to determine a wide range of genetic mutations on histol...

Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning.

The expanding field of epitranscriptomics might rival the epigenome in the diversity of biological processes impacted. In recent years, the development of new high-throughput experimental and computational techniques has been a key driving force in discovering the properties of RNA modifications. Machine learning applications, such as for classification, clustering or de novo identification, have ...

A scalable unsupervised learning of scRNAseq data detects rare cells through integration of structure-preserving embedding, clustering and outlier detection.

Single-cell RNA-seq analysis has become a powerful tool to analyse the transcriptomes of individual cells. In turn, it has fostered the possibility of...

AcrNET: predicting anti-CRISPR with deep learning.

MOTIVATION: As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e. CRISPR-Ca...

The evolution of cancer genomic medicine in Japan and the role of the National Cancer Center Japan.

The journey to implement cancer genomic medicine (CGM) in oncology practice began in the 1980s, which is considered the dawn of genetic and genomic ca...

Cryptic mutations of PLC family members in brain disorders: recent discoveries and a deep-learning-based approach.

Phospholipase C (PLC) is an essential isozyme involved in the phosphoinositide signalling pathway, which maintains cellular homeostasis. Gain- and los...

DIST: spatial transcriptomics enhancement using deep learning.

Spatially resolved transcriptomics technologies enable comprehensive measurement of gene expression patterns in the context of intact tissues. However...

Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling.

Recognizing binding sites of DNA-binding proteins is a key factor for elucidating transcriptional regulation in organisms. ChIP-exo enables researcher...

Identification of species-specific RNA N6-methyladinosine modification sites from RNA sequences.

N6-methyladinosine (m6A) modification is the most abundant co-transcriptional modification in eukaryotic RNA and plays important roles in cellular reg...

Cooperation of local features and global representations by a dual-branch network for transcription factor binding sites prediction.

Interactions between DNA and transcription factors (TFs) play an essential role in understanding transcriptional regulation mechanisms and gene expres...

AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning.

Alzheimer's disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple...

Identification of metal ion-binding sites in RNA structures using deep learning method.

Metal ion is an indispensable factor for the proper folding, structural stability and functioning of RNA molecules. However, it is very difficult for ...

Identification of significant gene expression changes in multiple perturbation experiments using knockoffs.

Large-scale multiple perturbation experiments have the potential to reveal a more detailed understanding of the molecular pathways that respond to gen...

miWords: transformer-based composite deep learning for highly accurate discovery of pre-miRNA regions across plant genomes.

Discovering pre-microRNAs (miRNAs) is the core of miRNA discovery. Using traditional sequence/structural features, many tools have been published to d...

DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences.

Identifying the function of DNA sequences accurately is an essential and challenging task in the genomic field. Until now, deep learning has been wide...

DeepOM: single-molecule optical genome mapping via deep learning.

MOTIVATION: Efficient tapping into genomic information from a single microscopic image of an intact DNA molecule is an outstanding challenge and its s...

Single-cell gene regulatory network prediction by explainable AI.

The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape o...

[A Case of Locally Advanced Sigmoid Colon Cancer Invading the Urinary Bladder Treated with Neoadjuvant Chemotherapy and Robot Assisted Urinary Bladder-Conserving Surgery].

We report a case of 72s male with locally advanced sigmoid colon cancer. Colonoscopy revealed an advanced sigmoid colon cancer(AV 15 cm, type 2, semi-...

RLBind: a deep learning method to predict RNA-ligand binding sites.

Identification of RNA-small molecule binding sites plays an essential role in RNA-targeted drug discovery and development. These small molecules are e...

A review of methods for predicting DNA N6-methyladenine sites.

Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provid...