AIMC Topic: Machine Learning

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Machine learning is the key to diagnose COVID-19: a proof-of-concept study.

Scientific reports
The reverse transcription-polymerase chain reaction (RT-PCR) assay is the accepted standard for coronavirus disease 2019 (COVID-19) diagnosis. As any test, RT-PCR provides false negative results that can be rectified by clinicians by confronting clin...

Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox.

Genome biology
The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing i...

Machine learning predicts nucleosome binding modes of transcription factors.

BMC bioinformatics
BACKGROUND: Most transcription factors (TFs) compete with nucleosomes to gain access to their cognate binding sites. Recent studies have identified several TF-nucleosome interaction modes including end binding (EB), oriented binding, periodic binding...

A Protocol for the Diagnosis of Autism Spectrum Disorder Structured in Machine Learning and Verbal Decision Analysis.

Computational and mathematical methods in medicine
Autism Spectrum Disorder is a mental disorder that afflicts millions of people worldwide. It is estimated that one in 160 children has traces of autism, with five times the higher prevalence in boys. The protocols for detecting symptoms are diverse. ...

Machine Learning Approaches to Determine Feature Importance for Predicting Infant Autopsy Outcome.

Pediatric and developmental pathology : the official journal of the Society for Pediatric Pathology and the Paediatric Pathology Society
INTRODUCTION: Sudden unexpected death in infancy (SUDI) represents the commonest presentation of postneonatal death. We explored whether machine learning could be used to derive data driven insights for prediction of infant autopsy outcome.

Four Generations of High-Dimensional Neural Network Potentials.

Chemical reviews
Since their introduction about 25 years ago, machine learning (ML) potentials have become an important tool in the field of atomistic simulations. After the initial decade, in which neural networks were successfully used to construct potentials for r...

Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids.

Clinical therapeutics
PURPOSE: Coronavirus disease-2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. However...

Balancing Data on Deep Learning-Based Proteochemometric Activity Classification.

Journal of chemical information and modeling
In silico analysis of biological activity data has become an essential technique in pharmaceutical development. Specifically, the so-called proteochemometric models aim to share information between targets in machine learning ligand-target activity p...

Recent Advances in Imaging of Preclinical, Sporadic, and Autosomal Dominant Alzheimer's Disease.

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics
Observing Alzheimer's disease (AD) pathological changes in vivo with neuroimaging provides invaluable opportunities to understand and predict the course of disease. Neuroimaging AD biomarkers also allow for real-time tracking of disease-modifying tre...