Genetics

Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis.

Many complex diseases share common genetic determinants and are comorbid in a population. We hypothesized that the co-occurrences of diseases and their overlapping genetic etiology can be exploited to simultaneously improve multiple diseases' polygenic risk scores (PRS). This hypothesis was tested using a multi-task learning (MTL) approach based on an explainable neural network architecture. We fo...

A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression.

BACKGROUND: Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre-symptomatic risk assessment but also for building personalized therapeutic strategies.

Harnessing the power of artificial intelligence to advance cell therapy.

Cell therapies are powerful technologies in which human cells are reprogrammed for therapeutic applications such as killing cancer cells or replacing ...

A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre-treatment tumor biopsies.

BACKGROUND: Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) has demonstrated a strong correlation to improved survival in breas...

High accuracy epidermal growth factor receptor mutation prediction via histopathological deep learning.

BACKGROUND: The detection of epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer is critical for tyrosine ki...

Prediction of on-target and off-target activity of CRISPR-Cas13d guide RNAs using deep learning.

Transcriptome engineering applications in living cells with RNA-targeting CRISPR effectors depend on accurate prediction of on-target activity and off...

Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring.

Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell ...

Deep learning applications in single-cell genomics and transcriptomics data analysis.

Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare popul...

The prediction of drug sensitivity by multi-omics fusion reveals the heterogeneity of drug response in pan-cancer.

Cancer drug response prediction based on genomic information plays a crucial role in modern pharmacogenomics, enabling individualized therapy. Given t...

Sampling effect in predicting the evolutionary response of populations to climate change.

Genomic data and machine learning approaches have gained interest due to their potential to identify adaptive genetic variation across populations and...

GR-m6A: Prediction of N6-methyladenosine sites in mammals with molecular graph and residual network.

RNA N6-methyladenine (m6A), which is produced by the methylation of the N6 position of eukaryotic adenine, is a relatively common post-transcriptional...

Noninvasive genetic screening: current advances in artificial intelligence for embryo ploidy prediction.

This review discusses the use of artificial intelligence (AI) algorithms in noninvasive prediction of embryo ploidy status for preimplantation genetic...

Amelogenesis imperfecta. case report.

The main origin of amelogenesis imperfecta (AI) is a genetic alteration inherited by a family member which affects the dental enamel of the teeth of a...

Gene-specific machine learning for pathogenicity prediction of rare BRCA1 and BRCA2 missense variants.

Machine learning-based pathogenicity prediction helps interpret rare missense variants of BRCA1 and BRCA2, which are associated with hereditary cancer...

Prediction of stock price movement using an improved NSGA-II-RF algorithm with a three-stage feature engineering process.

Prediction of stock price has been a hot topic in artificial intelligence field. Computational intelligent methods such as machine learning or deep le...

A one-stage deep learning based method for automatic analysis of droplet-based digital PCR images.

Droplet-based dPCR offers many advantages over chip-based dPCR, such as lower processing cost, higher droplet density, higher throughput, while requir...

ConvCoroNet: a deep convolutional neural network optimized with iterative thresholding algorithm for Covid-19 detection using chest X-ray images.

Covid-19 is a global pandemic. Early and accurate detection of positive cases prevent the further spread of this epidemic and help to treat rapidly th...

Accurate prediction of isothermal gas chromatographic Kováts retention indices.

We describe a freely available web server called Retention Index Predictor (RIpred) (https://ripred.ca) that rapidly and accurately predicts Gas Chrom...

An automated framework for evaluation of deep learning models for splice site predictions.

A novel framework for the automated evaluation of various deep learning-based splice site detectors is presented. The framework eliminates time-consum...

Clinical Evaluation of Commercial HARDSON COVID-19 Antigen Rapid Test Kit for Routine COVID-19 Diagnosis.

OBJECTIVE: This study aimed to evaluate the sensitivity, specificity, and accuracy of the commercial HARDSON COVID-19 Antigen Rapid Test Kit for diagn...