AIMC Topic: Machine Learning

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Machine Learning Reveals the Critical Interactions for SARS-CoV-2 Spike Protein Binding to ACE2.

The journal of physical chemistry letters
SARS-CoV and SARS-CoV-2 bind to the human ACE2 receptor in practically identical conformations, although several residues of the receptor-binding domain (RBD) differ between them. Herein, we have used molecular dynamics (MD) simulations, machine lear...

Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning.

Scientific reports
We developed a method to improve protein thermostability, "loop-walking method". Three consecutive positions in 12 loops of Burkholderia cepacia lipase were subjected to random mutagenesis to make 12 libraries. Screening allowed us to identify L7 as ...

Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study.

Journal of medical Internet research
BACKGROUND: The use of artificial intelligence has revolutionized every area of life such as business and trade, social and electronic media, education and learning, manufacturing industries, medicine and sciences, and every other sector. The new ref...

How to replace a physiotherapist: artificial intelligence and the redistribution of expertise.

Physiotherapy theory and practice
The convergence of large datasets, increased computational power, and enhanced algorithm design has led to the increased success of machine learning (ML) and artificial intelligence (AI) across a wide variety of healthcare professions but which, so f...

Computational representations of protein-ligand interfaces for structure-based virtual screening.

Expert opinion on drug discovery
: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies hav...

Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: A comparative study.

Computers in biology and medicine
The volume of daily patient arrivals at Emergency Departments (EDs) is unpredictable and is a significant reason of ED crowding in hospitals worldwide. Timely forecast of patients arriving at ED can help the hospital management in early planning and ...

Alignment-Free Antimicrobial Peptide Predictors: Improving Performance by a Thorough Analysis of the Largest Available Data Set.

Journal of chemical information and modeling
In the last two decades, a large number of machine-learning-based predictors for the activities of antimicrobial peptides (AMPs) have been proposed. These predictors differ from one another in the learning method and in the training and testing data ...

Robust diagnostic classification via Q-learning.

Scientific reports
Machine learning (ML) models have demonstrated the power of utilizing clinical instruments to provide tools for domain experts in gaining additional insights toward complex clinical diagnoses. In this context these tools desire two additional propert...

Ensemble machine learning of factors influencing COVID-19 across US counties.

Scientific reports
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the causal agent for COVID-19, is a communicable disease spread through close contact. It is known to disproportionately impact certain communities due to both biological susceptibility and...