AIMC Topic: Machine Learning

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Is one-shot learning a viable option in drug discovery?

Expert opinion on drug discovery Mar 18, 2019
Humans Neural Networks, Computer Drug Development Machine Learning Drug Discovery
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Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition.

Neural computation Mar 18, 2019
Practical motor imagery electroencephalogram (EEG) data-based applications are limited by the waste of unlabeled samples in supervised learning and excessive time consumption in the pretraining period. A semisupervised deep stacking network with an a...
Brain-Computer Interfaces Motor Activity Imagination Signal Processing, Computer-Assisted Pattern Recognition, Automated Brain Electroencephalography Humans Machine Learning Software
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Machine learning for the prediction of postpartum complications is promising, but needs rigorous evaluation.

BJOG : an international journal of obstetrics and gynaecology Mar 18, 2019
Female Humans Machine Learning Maternal Health Postpartum Period
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Solvent-Specific Featurization for Predicting Free Energies of Solvation through Machine Learning.

Journal of chemical information and modeling Mar 18, 2019
A featurization algorithm based on functional class fingerprints has been implemented within the DeepChem machine learning framework. It is based on descriptors more appropriate for solvation, taking into account intermolecular properties, and has be...
Machine Learning Cheminformatics Solvents Thermodynamics
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Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning.

Journal of chemical information and modeling Mar 18, 2019
Virtual screening is a promising method for obtaining novel hit compounds in drug discovery. It aims to enrich potentially active compounds from a large chemical library for further biological experiments. However, the accuracy of current virtual scr...
Machine Learning Molecular Docking Simulation Protein Conformation Thermodynamics Cheminformatics Drug Evaluation, Preclinical User-Computer Interface Ligands Proteins
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The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction.

Journal of chemical information and modeling Mar 18, 2019
The identification of possible targets for a known bioactive compound is of the utmost importance for drug design and development. Molecular docking is one possible approach for in-silico protein target prediction, whereas a molecule is docked into s...
Support Vector Machine Neural Networks, Computer Machine Learning Protein Conformation Molecular Docking Simulation Computational Biology
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Digital health at fifteen: more human (more needed).

BMC medicine Mar 18, 2019
There is growing appreciation that the success of digital health - whether digital tools, digital interventions or technology-based change strategies - is linked to the extent to which human factors are considered throughout design, development and i...
Telemedicine Ergonomics Machine Learning Humans Male Delivery of Health Care Female Time Factors
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Modeling second-order boundary perception: A machine learning approach.

PLoS computational biology Mar 18, 2019
Visual pattern detection and discrimination are essential first steps for scene analysis. Numerous human psychophysical studies have modeled visual pattern detection and discrimination by estimating linear templates for classifying noisy stimuli defi...
Photic Stimulation Contrast Sensitivity Visual Perception Humans Psychophysics Machine Learning Models, Theoretical
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Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important.

NeuroImage Mar 17, 2019
Combining neuroimaging and clinical information for diagnosis, as for example behavioral tasks and genetics characteristics, is potentially beneficial but presents challenges in terms of finding the best data representation for the different sources ...
Alzheimer Disease Neuroimaging Humans Algorithms Image Interpretation, Computer-Assisted Machine Learning Depression
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Machine Learning Enhances the Efficiency of Cognitive Screenings for Primary Care.

Journal of geriatric psychiatry and neurology Mar 17, 2019
BACKGROUND: Incorporation of cognitive screening into the busy primary care will require the development of highly efficient screening tools. We report the convergence validity of a very brief, self-administered, computerized assessment protocol agai...
Neuropsychological Tests Mass Screening Cognition Disorders Female Reproducibility of Results Male Primary Health Care Aged Humans Machine Learning
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