Genetics

Development and validation of a random forest algorithm for source attribution of animal and human Typhimurium and monophasic variants of Typhimurium isolates in England and Wales utilising whole genome sequencing data.

Source attribution has traditionally involved combining epidemiological data with different pathogen characterisation methods, including 7-gene multi locus sequence typing (MLST) or serotyping, however, these approaches have limited resolution. In contrast, whole genome sequencing data provide an overview of the whole genome that can be used by attribution algorithms. Here, we applied a random for...

A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction.

To explore a robust tool for advancing digital breeding practices through an artificial intelligence-driven phenotype prediction expert system, we undertook a thorough analysis of 11 non-linear regression models. Our investigation specifically emphasized the significance of Support Vector Regression (SVR) and SHapley Additive exPlanations (SHAP) in predicting soybean branching. By using branching ...

Modular Spiking Neural Membrane Systems for Image Classification.

A variant of membrane computing models called Spiking Neural P systems (SNP systems) closely mimics the structure and behavior of biological neurons. ...

DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences.

Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and anal...

scFSNN: a feature selection method based on neural network for single-cell RNA-seq data.

While single-cell RNA sequencing (scRNA-seq) allows researchers to analyze gene expression in individual cells, its unique characteristics like over-d...

Artificial intelligence measured 3D body composition to predict pathological response in rectal cancer patients.

BACKGROUND: The treatment of locally advanced rectal cancer (LARC) is moving towards total neoadjuvant therapy and potential organ preservation. Of pa...

Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space.

Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ...

Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality.

Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization...

A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors.

Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) have revolutionized breast cancer therapy. However, <50% of patients have an objective response,...

An Improved Clinical and Genetics-Based Prediction Model for Diabetic Foot Ulcer Healing.

The goal of this investigation was to use comprehensive prediction modeling tools and available genetic information to try to improve upon the perfor...

BiPSTP: Sequence feature encoding method for identifying different RNA modifications with bidirectional position-specific trinucleotides propensities.

RNA modification, a posttranscriptional regulatory mechanism, significantly influences RNA biogenesis and function. The accurate identification of mod...

Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning.

Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learni...

bioactivities and enzyme interactions of abietatrien-3-ol by bio-guided isolation from subsp. .

Abietatrien-3-ol (ATO) was isolated from the aerial part of the subsp. methanol-chloroform extract and studied antioxidant, enzyme inhibition, and D...

Development of a deep learning model to distinguish the cause of optic disc atrophy using retinal fundus photography.

The differential diagnosis for optic atrophy can be challenging and requires expensive, time-consuming ancillary testing to determine the cause. While...

Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species.

RNA modification plays a crucial role in cellular regulation. However, traditional high-throughput sequencing methods for elucidating their functional...

Identification and segregation of genes with improved recurrent neural network trained with optimal gene level and mutation level features.

Even though many different approaches have been employed to address the complex mutational heterogeneity of cancer, finding driver genes is still prob...

GANSamples-ac4C: Enhancing ac4C site prediction via generative adversarial networks and transfer learning.

RNA modification, N4-acetylcytidine (ac4C), is enzymatically catalyzed by N-acetyltransferase 10 (NAT10) and plays an essential role across tRNA, rRNA...

Comparing robotic and manual injection methods in zebrafish embryos for high-throughput RNA silencing using CRISPR-RfxCas13d.

In this study, the authors compared the efficiency of automated robotic and manual injection methods for the CRISPR-RfxCas13d (CasRx) system for mRNA ...

Visualizing and Comparing Machine Learning Predictions to Improve Human-AI Teaming on the Example of Cell Lineage.

We visualize the predictions of multiple machine learning models to help biologists as they interactively make decisions about cell lineage-the develo...

GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM.

BACKGROUND: Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease assoc...