AIMC Topic: Neural Networks, Computer

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DeepPPThermo: A Deep Learning Framework for Predicting Protein Thermostability Combining Protein-Level and Amino Acid-Level Features.

Journal of computational biology : a journal of computational molecular cell biology
Using wet experimental methods to discover new thermophilic proteins or improve protein thermostability is time-consuming and expensive. Machine learning methods have shown powerful performance in the study of protein thermostability in recent years....

Deep learning uncertainty quantification for clinical text classification.

Journal of biomedical informatics
INTRODUCTION: Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the st...

AI approach to biventricular function assessment in cine-MRI: an ultra-small training dataset and multivendor study.

Physics in medicine and biology
. It was a great challenge to train an excellent and generalized model on an ultra-small data set composed of multi-orientation cardiac cine magnetic resonance imaging (MRI) images. We try to develop a 3D deep learning method based on an ultra-small ...

Deep learning based uncertainty prediction of deformable image registration for contour propagation and dose accumulation in online adaptive radiotherapy.

Physics in medicine and biology
Online adaptive radiotherapy aims to fully leverage the advantages of highly conformal therapy by reducing anatomical and set-up uncertainty, thereby alleviating the need for robust treatments. This requires extensive automation, among which is the u...

Machine learning identification of Pseudomonas aeruginosa strains from colony image data.

PLoS computational biology
When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have u...

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions.

PLoS biology
Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive model...

DeepCompoundNet: enhancing compound-protein interaction prediction with multimodal convolutional neural networks.

Journal of biomolecular structure & dynamics
Virtual screening has emerged as a valuable computational tool for predicting compound-protein interactions, offering a cost-effective and rapid approach to identifying potential candidate drug molecules. Current machine learning-based methods rely o...

Fluoranthene biotreatment using prominent freshwater microalgae: physiological responses of microalgae and artificial neural network modeling of the bioremoval process.

International journal of phytoremediation
Due to the intensified industrial activities and other anthropogenic actions, contamination of polycyclic aromatic hydrocarbons (PAHs) has been growing at an alarming rate, turning in to a serious environmental concern. Bioremediation, as an eco-frie...

Multi-scale representation of surface-enhanced Raman spectroscopy data for deep learning-based liver cancer detection.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early can...