AIMC Topic: Machine Learning

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DeepSite: protein-binding site predictor using 3D-convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: An important step in structure-based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years b...

DIRECTION: a machine learning framework for predicting and characterizing DNA methylation and hydroxymethylation in mammalian genomes.

Bioinformatics (Oxford, England)
MOTIVATION: 5-Methylcytosine and 5-Hydroxymethylcytosine in DNA are major epigenetic modifications known to significantly alter mammalian gene expression. High-throughput assays to detect these modifications are expensive, labor-intensive, unfeasible...

Computational health economics for identification of unprofitable health care enrollees.

Biostatistics (Oxford, England)
Health insurers may attempt to design their health plans to attract profitable enrollees while deterring unprofitable ones. Such insurers would not be delivering socially efficient levels of care by providing health plans that maximize societal benef...

Machine Learning Interface for Medical Image Analysis.

Journal of digital imaging
TensorFlow is a second-generation open-source machine learning software library with a built-in framework for implementing neural networks in wide variety of perceptual tasks. Although TensorFlow usage is well established with computer vision dataset...

Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests.

Bioinformatics (Oxford, England)
MOTIVATION: Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classific...

Multi-label classifier based on histogram of gradients for predicting the anatomical therapeutic chemical class/classes of a given compound.

Bioinformatics (Oxford, England)
MOTIVATION: Given an unknown compound, is it possible to predict its Anatomical Therapeutic Chemical class/classes? This is a challenging yet important problem since such a prediction could be used to deduce not only a compound's possible active ingr...

Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility.

Bioinformatics (Oxford, England)
MOTIVATION: The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions b...

Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis.

SAR and QSAR in environmental research
The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to ...