Prescriptions

Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction mention extraction.

BACKGROUND: Social media is a useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from Twitter. Medical information extraction from social media is challenging, mainly...

Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature.

In the era of precision medicine, cancer therapy can be tailored to an individual patient based on the genomic profile of a tumour. Despite the ever-increasing abundance of cancer genomic data, linking mutation profiles to drug efficacy remains a challenge. Herein, we report Cancer Drug Response profile scan (CDRscan) a novel deep learning model that predicts anticancer drug responsiveness based o...

Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs.

BACKGROUND: Adverse drug reactions (ADRs), also called as drug adverse events (AEs), are reported in the FDA drug labels; however, it is a big challen...

Ischemic stroke lesion segmentation using stacked sparse autoencoder.

Automatic segmentation of ischemic stroke lesion volumes from multi-spectral Magnetic Resonance Imaging (MRI) sequences plays a vital role in quantify...

Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease.

Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical appli...

Machine Learning to Predict, Detect, and Intervene Older Adults Vulnerable for Adverse Drug Events in the Emergency Department.

Adverse drug events (ADEs) are common and have serious consequences in older adults. ED visits are opportunities to identify and alter the course of s...

Evaluation of Natural Language Processing (NLP) systems to annotate drug product labeling with MedDRA terminology.

INTRODUCTION: The FDA Adverse Event Reporting System (FAERS) is a primary data source for identifying unlabeled adverse events (AEs) in a drug or biol...

Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network.

Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP450) inhibition is an important consideration in drug discovery. I...

Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images.

Likely drug candidates which are identified in traditional pre-clinical drug screens often fail in patient trials, increasing the societal burden of d...

Design of a generic, open platform for machine learning-assisted indexing and clustering of articles in PubMed, a biomedical bibliographic database.

Many investigators have carried out text mining of the biomedical literature for a variety of purposes, ranging from the assignment of indexing terms ...

Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis.

Dynamic Causal Modeling (DCM) is an advanced biophysical model which explicitly describes the entire process from experimental stimuli to functional m...

Productive performance, egg quality, hematological parameters and serum chemistry of laying hens fed diets supplemented with certain fat-soluble vitamins, individually or combined, during summer season.

This present study aimed to determine the efficacy of supplementing layer diets with vitamin A (0, 8,000 and 16,000 IU/kg diet) and vitamin E (0, 250 ...

Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies.

Adverse drug reactions, particularly those that result in drug-induced liver injury (DILI), are a major cause of drug failure in clinical trials and d...

Extending the DIDEO ontology to include entities from the natural product drug interaction domain of discourse.

BACKGROUND: Prompted by the frequency of concomitant use of prescription drugs with natural products, and the lack of knowledge regarding the impact o...

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram.

Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients wit...

Using machine learning to identify patterns of lifetime health problems in decedents with autism spectrum disorder.

Very little is known about the health problems experienced by individuals with autism spectrum disorder (ASD) throughout their life course. We retrosp...

Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy.

Timely determination of antimicrobial susceptibility for a bacterial infection enables precision prescription, shortens treatment time, and helps mini...

Drug Repositioning by Integrating Known Disease-Gene and Drug-Target Associations in a Semi-supervised Learning Model.

Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this g...

Deep learning in pharmacogenomics: from gene regulation to patient stratification.

This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulat...

Multi-target drug repositioning by bipartite block-wise sparse multi-task learning.

BACKGROUND: Finding potential drug targets is a crucial step in drug discovery and development. Recently, resources such as the Library of Integrated ...