Genetics

Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach.

Application of single-cell/nucleus genomic sequencing to patient-derived tissues offers potential solutions to delineate disease mechanisms in humans. However, individual cells in patient-derived tissues are in different pathological stages, and hence, such cellular variability impedes subsequent differential gene expression analyses. To overcome such a heterogeneity issue, we present a novel deep...

Artificial intelligence and machine learning in cell-free-DNA-based diagnostics.

The discovery of circulating fetal and tumor cell-free DNA (cfDNA) molecules in plasma has opened up tremendous opportunities in noninvasive diagnostics such as the detection of fetal chromosomal aneuploidies and cancers and in posttransplantation monitoring. The advent of high-throughput sequencing technologies makes it possible to scrutinize the characteristics of cfDNA molecules, opening up the...

Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach.

Anomalous chromosomes are the cause of genetic diseases such as cancer, Alzheimer's, Parkinson's, epilepsy, and autism. Karyotype analysis is the stan...

Paying attention to the SARS-CoV-2 dialect : a deep neural network approach to predicting novel protein mutations.

Predicting novel mutations has long-lasting impacts on life science research. Traditionally, this problem is addressed through wet-lab experiments, wh...

A scalable tool for analyzing genomic variants of humans using knowledge graphs and graph machine learning.

Advances in high-throughput genome sequencing have enabled large-scale genome sequencing in clinical practice and research studies. By analyzing genom...

Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat ( L.).

Phytochromes are essential photoreceptors in plants that sense red and far-red light, playing a vital role in regulating plant growth and development ...

Association of and gene polymorphisms and ERAP2 protein with the susceptibility and severity of rheumatoid arthritis in the Ukrainian population.

INTRODUCTION: Rheumatoid arthritis (RA) is a long-term autoimmune disorder that primarily affects joints. Although RA is chiefly associated with HLA c...

Biocomputing at the crossroad between emulating artificial intelligence and cellular supremacy.

Biocomputation aims to create sophisticated biological systems capable of addressing important problems in (bio)medicine with a machine-like precision...

MCTASmRNA: A deep learning framework for alternative splicing events classification.

Alternative splicing (AS) plays crucial post-transcriptional gene function regulation roles in eukaryotic. Despite progress in studying AS at the RNA ...

Perspective: Multiomics and Artificial Intelligence for Personalized Nutritional Management of Diabetes in Patients Undergoing Peritoneal Dialysis.

Managing diabetes in patients on peritoneal dialysis (PD) is challenging due to the combined effects of dietary glucose, glucose from dialysate, and o...

Deep learning algorithms for predicting pathological complete response in MRI of rectal cancer patients undergoing neoadjuvant chemoradiotherapy: a systematic review.

PURPOSE: This systematic review examines the utility of deep learning algorithms in predicting pathological complete response (pCR) in rectal cancer p...

Automated post-run analysis of arrayed quantitative PCR amplification curves using machine learning.

BACKGROUND: The TaqMan Array Card (TAC) is an arrayed, high-throughput qPCR platform that can simultaneously detect multiple targets in a single react...

Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy.

Messenger RNA (mRNA) vaccines offer an adaptable and scalable platform for cancer immunotherapy, requiring optimal design to elicit a robust and targe...

An Intelligent Prediction Model for the Synthesis Conditions of Metal-Organic Frameworks Utilizing Artificial Neural Networks Enhanced by Genetic Algorithm Optimization.

In the field of emerging materials, metal-organic frameworks (MOFs) have gained prominence due to their unique porous structures, showing versatility ...

Deep self-representation learning with hyper-laplacian regularization for brain imaging genetics association analysis.

Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quanti...

Further Development of SAMPDI-3D: A Machine Learning Method for Predicting Binding Free Energy Changes Caused by Mutations in Either Protein or DNA.

BACKGROUND/OBJECTIVES: Predicting the effects of protein and DNA mutations on the binding free energy of protein-DNA complexes is crucial for understa...

Digital Health and Genomics.

Digital health technologies have a crucial role in streamlining the use of genomics and facilitating access to genomic health care. There are efforts ...

Bimodal In Situ Analyzer for Circular RNA in Extracellular Vesicles Combined with Machine Learning for Accurate Gastric Cancer Detection.

Circular RNAs in extracellular vesicles (EV-circRNAs) are gaining recognition as potential biomarkers for the diagnosis of gastric cancer (GC). Most c...

Bacterial Wastewater-Based Epidemiology Using Surface-Enhanced Raman Spectroscopy and Machine Learning.

Although wastewater-based epidemiology has been used extensively for the surveillance of viral diseases, it has not been used to a similar extent for ...

Highlights of Precision Medicine, Genetics, Epigenetics and Artificial Intelligence in Pompe Disease.

Pompe disease is a neuromuscular disorder caused by a deficiency of the enzyme acid alpha-glucosidase (), which leads to lysosomal glycogen accumulati...