Genetics

Deep learning-based models for preimplantation mouse and human embryos based on single-cell RNA sequencing.

The rapid growth of single-cell transcriptomic technology has produced an increasing number of datasets for both embryonic development and in vitro pluripotent stem cell-derived models. This avalanche of data surrounding pluripotency and the process of lineage specification has meant it has become increasingly difficult to define specific cell types or states in vivo, and compare these with in vit...

Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistant from whole genome sequencing and gene expression.

Whole genome sequencing (WGS) potentially represents a rapid approach for antimicrobial resistance genotype-to-phenotype prediction. However, the challenge still exists to predict fully minimum inhibitory concentrations (MICs) and antimicrobial susceptibility phenotypes based on WGS data. This study aimed to establish an artificial intelligence-based computational approach in predicting antimicrob...

Machine learning-driven estimation of mutational burden highlights DNAH5 as a prognostic marker in colorectal cancer.

BACKGROUND: Tumor Mutational Burden (TMB) have emerged as pivotal predictive biomarkers in determining prognosis and response to immunotherapy in colo...

Visualization Methods for DNA Sequences: A Review and Prospects.

The efficient analysis and interpretation of biological sequence data remain major challenges in bioinformatics. Graphical representation, as an emerg...

Early and noninvasive prediction of response to neoadjuvant therapy for breast cancer via longitudinal ultrasound and MR deep learning: A multicentre study.

RATIONALE AND OBJECTIVES: The early prediction of response to neoadjuvant chemotherapy (NAC) will aid in the development of personalized treatments fo...

Barley Grain Proteome Assessment Using Multi-Environment Trial Data and Machine Learning.

Proteomics can be used to assess individual protein abundances, which could reflect genotypic and environmental effects and potentially predict grain/...

Deep learning enables the use of ultra-high-density array in DNBSEQ.

DNBSEQ employs a patterned array to facilitate massively parallel sequencing of DNA nanoballs (DNBs), leading to a considerable boost in throughput. B...

Advantages of Metabolomics-Based Multivariate Machine Learning to Predict Disease Severity: Example of COVID.

The COVID-19 outbreak caused saturations of hospitals, highlighting the importance of early patient triage to optimize resource prioritization. Herein...

Integration of Machine Learning and Experimental Validation to Identify Anoikis-Related Prognostic Signature for Predicting the Breast Cancer Tumor Microenvironment and Treatment Response.

Anoikis-related genes (ANRGs) are crucial in the invasion and metastasis of breast cancer (BC). The underlying role of ANRGs in the prognosis of brea...

Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study.

MicroRNAs (miRs) are short, noncoding RNA strands that regulate the activity of mRNAs by affecting the repression of protein translation, and their dy...

Analyzing immune cell infiltrates in skeletal muscle of infantile-onset Pompe disease using bioinformatics and machine learning.

Pompe disease, a severe lysosomal storage disorder, is marked by heart problems, muscle weakness, and respiratory difficulties. This study aimed to id...

Discovery of key molecular signatures for diagnosis and therapies of glioblastoma by combining supervised and unsupervised learning approaches.

Glioblastoma (GBM) is the most malignant brain cancer and one of the leading causes of cancer-related death globally. So, identifying potential molecu...

Improving methylmalonic acidemia (MMA) screening and MMA genotype prediction using random forest classifier in two Chinese populations.

BACKGROUND: Methylmalonic acidemia (MMA) is one of the most common hereditary organic acid metabolism disorders that endangers the lives and health of...

Interpretation knowledge extraction for genetic testing via question-answer model.

BACKGROUND: Sequencing-based genetic testing is widely used in biomedical research, including pathogenic microorganism detection with metagenomic next...

Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification.

This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as , , , , ...

DeSide: A unified deep learning approach for cellular deconvolution of tumor microenvironment.

Cellular deconvolution via bulk RNA sequencing (RNA-seq) presents a cost-effective and efficient alternative to experimental methods such as flow cyto...

Deep learning method for detecting fluorescence spots in cancer diagnostics via fluorescence in situ hybridization.

Fluorescence in Situ Hybridization (FISH) is a technique for macromolecule identification that utilizes the complementarity of DNA or DNA/RNA double s...

A deep learning model for prediction of autism status using whole-exome sequencing data.

Autism is a developmental disability. Research demonstrated that children with autism benefit from early diagnosis and early intervention. Genetic fac...

Machine Learning-enhanced Signature of Metastasis-related T Cell Marker Genes for Predicting Overall Survival in Malignant Melanoma.

In this study, we aimed to investigate disparities in the tumor immune microenvironment (TME) between primary and metastatic malignant melanoma (MM) u...

Challenges and Prospects of DNA-Encoded Library Data Interpretation.

DNA-encoded library (DEL) technology is a powerful platform for the efficient identification of novel chemical matter in the early drug discovery proc...