AIMC Topic: Machine Learning

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Improving Enhancer Identification with a Multi-Classifier Stacked Ensemble Model.

Journal of molecular biology
Enhancers are DNA regions that are responsible for controlling the expression of genes. Enhancers are usually found upstream or downstream of a gene, or even inside a gene's intron region, but are normally located at a distant location from the genes...

A practical guide to machine-learning scoring for structure-based virtual screening.

Nature protocols
Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in si...

[The Swecrit Biobank, associated clinical registries, and machine learning (artificial intelligence) improve critical care knowledge].

Lakartidningen
The unique Swecrit Biobank and its associated clinical registries for sepsis, ARDS, cardiac arrest, trauma, and COVID-19 include more than 150,000 blood samples and descriptions of critically ill patients. These assets provide a unique opportunity to...

Psychometric Properties of a Machine Learning-Based Patient-Reported Outcome Measure on Medication Adherence: Single-Center, Cross-Sectional, Observational Study.

Journal of medical Internet research
BACKGROUND: Medication adherence plays a critical role in controlling the evolution of chronic disease, as low medication adherence may lead to worse health outcomes, higher mortality, and morbidity. Assessment of their patients' medication adherence...

Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies.

PLoS computational biology
Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging an...

DeepADRA2A: predicting adrenergic α2a inhibitors using deep learning.

Journal of biomolecular structure & dynamics
Adrenergic α2a (ADRA2A) receptors play a crucial role in modulating various physiological actions, thereby influencing the proper functioning of different systems in the body. ADRA2A regulation is associated with a wide range of effects, including al...

Artificial intelligence for dementia prevention.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical t...

Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study.

Surgical endoscopy
BACKGROUND: With Surgomics, we aim for personalized prediction of the patient's surgical outcome using machine-learning (ML) on multimodal intraoperative data to extract surgomic features as surgical process characteristics. As high-quality annotatio...

AC-PLT: An algorithm for computer-assisted coding of semantic property listing data.

Behavior research methods
In this paper, we present a novel algorithm that uses machine learning and natural language processing techniques to facilitate the coding of feature listing data. Feature listing is a method in which participants are asked to provide a list of featu...