AIMC Topic: Machine Learning

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Protein Family-Specific Models Using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data.

Journal of chemical information and modeling
Machine learning has shown enormous potential for computer-aided drug discovery. Here we show how modern convolutional neural networks (CNNs) can be applied to structure-based virtual screening. We have coupled our densely connected CNN (DenseNet) wi...

G-Networks to Predict the Outcome of Sensing of Toxicity.

Sensors (Basel, Switzerland)
G-Networks and their simplified version known as the Random Neural Network have often been used to classify data. In this paper, we present a use of the Random Neural Network to the early detection of potential of toxicity chemical compounds through ...

Emergent mechanisms of evidence integration in recurrent neural networks.

PloS one
Recent advances in machine learning have enabled neural networks to solve tasks humans typically perform. These networks offer an exciting new tool for neuroscience that can give us insight in the emergence of neural and behavioral mechanisms. A big ...

Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning.

Scientific reports
RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow fo...

A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model.

International journal of computer assisted radiology and surgery
PURPOSE: With the recent introduction of fully assisting scanner technologies by Siemens Healthineers (Erlangen, Germany), a patient surface model was introduced to the diagnostic imaging device market. Such a patient representation can be used to au...

Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization.

Proceedings of the National Academy of Sciences of the United States of America
Humans and most animals can learn new tasks without forgetting old ones. However, training artificial neural networks (ANNs) on new tasks typically causes them to forget previously learned tasks. This phenomenon is the result of "catastrophic forgett...

A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders.

Journal of voice : official journal of the Voice Foundation
The human voice production system is an intricate biological device capable of modulating pitch and loudness. Inherent internal and/or external factors often damage the vocal folds and result in some change of voice. The consequences are reflected in...

Machine learning multivariate pattern analysis predicts classification of posttraumatic stress disorder and its dissociative subtype: a multimodal neuroimaging approach.

Psychological medicine
BACKGROUND: The field of psychiatry would benefit significantly from developing objective biomarkers that could facilitate the early identification of heterogeneous subtypes of illness. Critically, although machine learning pattern recognition method...

askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men.

European urology
BACKGROUND: Clinical registries provide physicians with a means for making data-driven decisions but few opportunities exist for patients to interact with registry data to help make decisions.

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance.

Journal of sports sciences
Objective assessment of an athlete's performance is of importance in elite sports to facilitate detailed analysis. The implementation of automated detection and recognition of sport-specific movements overcomes the limitations associated with manual ...