Prescriptions

Machine Learning Methods in Drug Discovery.

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning...

Let's Work Together: A Meta-Analysis on Robot Design Features That Enable Successful Human-Robot Interaction at Work.

OBJECTIVE: This meta-analysis reviews robot design features of interface, controller, and appearance and statistically summarizes their effect on successful human-robot interaction (HRI) at work (that is, task performance, cooperation, satisfaction, acceptance, trust, mental workload, and situation awareness).

Closing the Digital Health Evidence Gap: Development of a Predictive Score to Maximize Patient Outcomes.

Clinical studies of telemedicine (TM) programs for chronic illness have demonstrated mixed results across settings and populations. With recent uptak...

Accuracy of machine learning-based prediction of medication adherence in clinical research.

Medication non-adherence represents a significant barrier to treatment efficacy. Remote, real-time measurement of medication dosing can facilitate dyn...

A Multi-View Deep Neural Network Model for Chemical-Disease Relation Extraction From Imbalanced Datasets.

Understanding the chemical-disease relations (CDR) is a crucial task in various biomedical domains. Manual mining of these information from biomedical...

Matrix Factorization-based Technique for Drug Repurposing Predictions.

Classical drug design methodologies are hugely costly and time-consuming, with approximately 85% of the new proposed molecules failing in the first th...

Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level.

Background Stroke is a major cardiovascular disease that causes significant health and economic burden in the United States. Neighborhood community-ba...

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.

Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, cur...

Profiling SARS-CoV-2 Main Protease (M) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields.

The current COVID-19 pandemic caused by a novel coronavirus SARS-CoV-2 urgently calls for a working therapeutic. Here, we report a computation-based w...

Kernel methods and their derivatives: Concept and perspectives for the earth system sciences.

Kernel methods are powerful machine learning techniques which use generic non-linear functions to solve complex tasks. They have a solid mathematical ...

Ensemble transfer learning for the prediction of anti-cancer drug response.

Transfer learning, which transfers patterns learned on a source dataset to a related target dataset for constructing prediction models, has been shown...

Learning interaction dynamics with an interactive LSTM for conversational sentiment analysis.

Conversational sentiment analysis is an emerging, yet challenging subtask of the sentiment analysis problem. It aims to discover the affective state a...

FPSC-DTI: drug-target interaction prediction based on feature projection fuzzy classification and super cluster fusion.

Identifying drug-target interactions (DTIs) is an important part of drug discovery and development. However, identifying DTIs is a complex process tha...

LMI-DForest: A deep forest model towards the prediction of lncRNA-miRNA interactions.

The interactions between miRNAs and long non-coding RNAs (lncRNAs) are subject to intensive recent studies due to its critical role in gene regulation...

Wearable Biofeedback Improves Human-Robot Compliance during Ankle-Foot Exoskeleton-Assisted Gait Training: A Pre-Post Controlled Study in Healthy Participants.

The adjunctive use of biofeedback systems with exoskeletons may accelerate post-stroke gait rehabilitation. Wearable patient-oriented human-robot inte...

Predicting adverse drug reactions of two-drug combinations using structural and transcriptomic drug representations to train an artificial neural network.

Adverse drug reactions (ADRs) are pharmacological events triggered by drug interactions with various sources of origin including drug-drug interaction...

Personalized treatment for coronary artery disease patients: a machine learning approach.

Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not ...

Artificial Intelligence, Real-World Automation and the Safety of Medicines.

Despite huge technological advances in the capabilities to capture, store, link and analyse data electronically, there has been some but limited impac...

DeepACTION: A deep learning-based method for predicting novel drug-target interactions.

Drug-target interactions (DTIs) play a key role in drug development and discovery processes. Wet lab prediction of DTIs is time-consuming, expensive, ...

Sampling methods and feature selection for mortality prediction with neural networks.

Along with digitization, automatic data-driven decision support systems become increasingly popular. Mortality prediction is a vital part of that deci...