Prescriptions

Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic region is an established therapy for advanced Parkinson's disease (PD). However, patients often require time-intensive post-operative management to balance their coupled stimulation and medication treatments. Given the large and complex parameter space associated with this task, we propose that clinical decision support systems (CDSS) bas...

Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs).

There are a large number of tomato cultivars with a wide range of morphological, chemical, nutritional and sensorial characteristics. Many factors are known to affect the nutrient content of tomato cultivars. A complete understanding of the effect of these factors would require an exhaustive experimental design, multidisciplinary scientific approach and a suitable statistical method. Some multivar...

Exploring Spanish health social media for detecting drug effects.

BACKGROUND: Adverse Drug reactions (ADR) cause a high number of deaths among hospitalized patients in developed countries. Major drug agencies have de...

Prediction of drug-induced eosinophilia adverse effect by using SVM and naïve Bayesian approaches.

Drug-induced eosinophilia is a potentially life-threatening adverse effect; clinical manifestations, eosinophilia-myalgia syndrome, mainly include sev...

Identifying the Basal Ganglia network model markers for medication-induced impulsivity in Parkinson's disease patients.

Impulsivity, i.e. irresistibility in the execution of actions, may be prominent in Parkinson's disease (PD) patients who are treated with dopamine pre...

The role of machine learning in neuroimaging for drug discovery and development.

Neuroimaging has been identified as a potentially powerful probe for the in vivo study of drug effects on the brain with utility across several phases...

Clinical Documents Clustering Based on Medication/Symptom Names Using Multi-View Nonnegative Matrix Factorization.

Clinical documents are rich free-text data sources containing valuable medication and symptom information, which have a great potential to improve hea...

Evaluation by Expert Dancers of a Robot That Performs Partnered Stepping via Haptic Interaction.

Our long-term goal is to enable a robot to engage in partner dance for use in rehabilitation therapy, assessment, diagnosis, and scientific investigat...

Validation of a Crowdsourcing Methodology for Developing a Knowledge Base of Related Problem-Medication Pairs.

BACKGROUND: Clinical knowledge bases of problem-medication pairs are necessary for many informatics solutions that improve patient safety, such as cli...

Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System.

Drug-target interaction (DTI) is a key aspect in pharmaceutical research. With the ever-increasing new drug data resources, computational approaches h...

An end-to-end hybrid algorithm for automated medication discrepancy detection.

BACKGROUND: In this study we implemented and developed state-of-the-art machine learning (ML) and natural language processing (NLP) technologies and b...

Toward a complete dataset of drug-drug interaction information from publicly available sources.

Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete sou...

Prediction of overall in vitro microsomal stability of drug candidates based on molecular modeling and support vector machines. Case study of novel arylpiperazines derivatives.

Other than efficacy of interaction with the molecular target, metabolic stability is the primary factor responsible for the failure or success of a co...

Robust representation and recognition of facial emotions using extreme sparse learning.

Recognition of natural emotions from human faces is an interesting topic with a wide range of potential applications, such as human-computer interacti...

An integrated Taguchi and response surface methodological approach for the optimization of an HPLC method to determine glimepiride in a supersaturatable self-nanoemulsifying formulation.

We studied the application of Taguchi orthogonal array (TOA) design during the development of an isocratic stability indicating HPLC method for glimep...

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder.

Protein-protein interactions (PPIs) play essential roles in many biological processes. In protein-protein interaction networks, hubs involve in number...

Facilitatory effects of anti-spastic medication on robotic locomotor training in people with chronic incomplete spinal cord injury.

BACKGROUND: The objective of this study was to investigate whether an anti-spasticity medication can facilitate the effects of robotic locomotor tread...

Extracting drug-drug interactions from literature using a rich feature-based linear kernel approach.

Identifying unknown drug interactions is of great benefit in the early detection of adverse drug reactions. Despite existence of several resources for...

Comparing a knowledge-driven approach to a supervised machine learning approach in large-scale extraction of drug-side effect relationships from free-text biomedical literature.

BACKGROUND: Systems approaches to studying drug-side-effect (drug-SE) associations are emerging as an active research area for both drug target discov...

Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms.

Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains un...