Scientists usually study the molecular machinery that controls gene expression from the perspective of a linear, two-dimensional genome—even though DNA and its bound proteins function in three ...

Abstract: Learning over time for machine learning (ML) models is emerging as a new field, often called continual learning or lifelong Machine learning (LML). Today, deep learning and neural networks ...

This project implements an advanced Virtual Machine Placement (VMP) optimization system that leverages multi-objective genetic algorithms, machine learning predictions, blockchain technology, and ...

Abstract: Artificial intelligence has transformed healthcare through improved disease prediction and diagnostic accuracy. However, the prediction of osteoporosis remains challenging due to limitations ...

This study integrated scRNA-seq and bulk RNA-seq data to identify macrophage subpopulations in degenerative tissues and constructed co-expression modules using hdWGCNA. Functional enrichment was ...

This project focuses on detecting cyber attacks using machine learning techniques. It employs various algorithms to analyze network traffic and identify potential threats in real-time.

The workflow encompasses patient datacollection and screening, univariate regression analysis for initial variable selection, systematic comparison of 91 machine learning models,selection and ...

A machine learning algorithm used gene expression profiles of patients with gout to predict flares. The PyTorch neural network performed best, with an area under the curve of 65%. The PyTorch model ...

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We integrated genome and transcriptome data from double-coated Hetian sheep and single-coated Chinese Merino sheep. Candidate genes associated with coat fleece type and environmental adaptation were ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. This study explores the development of two predictive models for the yield sooting ...