AIMC Topic: Neural Networks, Computer

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Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds.

Molecular pharmaceutics
Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and pharmaceutical chemistry need and consider it a huge goal to define target proteins of new antiretroviral compounds. ChEMBL manages Big Data features with a compl...

Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models.

Computers in biology and medicine
One of the significant issues in global healthcare systems is improving the supply chain performance and addressing the uncertainties in demand. Blood products, especially platelets, have the most challenging supply chains in the health system given ...

CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA-Disease Associations.

International journal of molecular sciences
It is well known that the unusual expression of long non-coding RNAs (lncRNAs) is closely related to the physiological and pathological processes of diseases. Therefore, inferring the potential lncRNA-disease associations are helpful for understandin...

Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish.

Journal of biophotonics
Optical projection tomography (OPT) is a 3D mesoscopic imaging modality that can utilize absorption or fluorescence contrast. 3D images can be rapidly reconstructed from tomographic data sets sampled with sufficient numbers of projection angles using...

A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
One of the challenges in motor imagery (MI) classification tasks is finding an easy-handled electroencephalogram (EEG) representation method which can preserve not only temporal features but also spatial ones. To fully utilize the features on various...

Deep Learning and Machine Vision Approaches for Posture Detection of Individual Pigs.

Sensors (Basel, Switzerland)
Posture detection targeted towards providing assessments for the monitoring of health and welfare of pigs has been of great interest to researchers from different disciplines. Existing studies applying machine vision techniques are mostly based on me...

A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone.

Sensors (Basel, Switzerland)
As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans' daily behavior. It has spurred the demand for intelligent sensors and has been giving rise to the explosive gro...

Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study.

Scientific reports
Medical images such as magnetic resonance (MR) imaging provide valuable information for cancer detection, diagnosis, and prognosis. In addition to the anatomical information these images provide, machine learning can identify texture features from th...

A null model of the mouse whole-neocortex micro-connectome.

Nature communications
In connectomics, the study of the network structure of connected neurons, great advances are being made on two different scales: that of macro- and meso-scale connectomics, studying the connectivity between populations of neurons, and that of micro-s...

Could deep learning in neural networks improve the QSAR models?

SAR and QSAR in environmental research
Assessing chemical toxicity is a multidisciplinary process, traditionally involving in vivo, in vitro and in silico tests. Currently, toxicological goal is to reduce new tests on chemicals, exploiting all information yet available. Recent advancement...