AIMC Topic: Machine Learning

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Short-Term Memory Binding Distinguishing Amnestic Mild Cognitive Impairment from Healthy Aging: A Machine Learning Study.

Journal of Alzheimer's disease : JAD
BACKGROUND: Amnestic mild cognitive impairment (aMCI) is the most common preclinical stage of Alzheimer's disease (AD). A strategy to reduce the impact of AD is the early aMCI diagnosis and clinical intervention. Neuroimaging, neurobiological, and ge...

A Recent Appraisal of Artificial Intelligence and In Silico ADMET Prediction in the Early Stages of Drug Discovery.

Mini reviews in medicinal chemistry
In silico ADMET models have progressed significantly over the past ~4 decades, but still, the pharmaceutical industry is vexed by the late-stage toxicity failure of lead molecules. This problem of late-stage attrition of the drug candidates because o...

Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models.

Current topics in medicinal chemistry
BACKGROUND: Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) models proposed for new microorganisms with promising properties is an important goal for chemical biology.

Screening for Early-Stage Alzheimer's Disease Using Optimized Feature Sets and Machine Learning.

Journal of Alzheimer's disease : JAD
BACKGROUND: Detecting early-stage Alzheimer's disease in clinical practice is difficult due to a lack of efficient and easily administered cognitive assessments that are sensitive to very mild impairment, a likely contributor to the high rate of unde...

Bionoi: A Voronoi Diagram-Based Representation of Ligand-Binding Sites in Proteins for Machine Learning Applications.

Methods in molecular biology (Clifton, N.J.)
Bionoi is a new software to generate Voronoi representations of ligand-binding sites in proteins for machine learning applications. Unlike many other deep learning models in biomedicine, Bionoi utilizes off-the-shelf convolutional neural network arch...

Multifunctionality in a reservoir computer.

Chaos (Woodbury, N.Y.)
Multifunctionality is a well observed phenomenological feature of biological neural networks and considered to be of fundamental importance to the survival of certain species over time. These multifunctional neural networks are capable of performing ...

The Role of Machine Learning in Centralized Authorization Process of Nanomedicines in European Union.

Current topics in medicinal chemistry
BACKGROUND: Machine Learning (ML) has experienced an increasing use, given the possibilities to expand the scientific knowledge of different disciplines, such as nanotechnology. This has allowed the creation of Cheminformatic models capable of predic...

Epistasis Analysis: Classification Through Machine Learning Methods.

Methods in molecular biology (Clifton, N.J.)
Complex disease is different from Mendelian disorders. Its development usually involves the interaction of multiple genes or the interaction between genes and the environment (i.e. epistasis). Although the high-throughput sequencing technologies for ...

Epistasis Detection Based on Epi-GTBN.

Methods in molecular biology (Clifton, N.J.)
Epistasis detection is a hot topic in bioinformatics due to its relevance to the detection of specific phenotypic traits and gene-gene interactions. Here, we present a step-by-step protocol to apply Epi-GTBN, a machine learning-based method based on ...