Prescriptions

Advances with support vector machines for novel drug discovery.

Novel drug discovery remains an enormous challenge, with various computer-aided drug design (CADD) approaches having been widely employed for this purpose. CADD, specifically the commonly used support vector machines (SVMs), can employ machine learning techniques. SVMs and their variations offer numerous drug discovery applications, which range from the classification of substances (as active or i...

Identification of Drug-Side Effect Association via Semisupervised Model and Multiple Kernel Learning.

Drug-side effect association contains the information on marketed medicines and their recorded adverse drug reactions. Traditional experimental method is time consuming and expensive. All associations of drugs and side-effects are seen as a bipartite network. Therefore, many computational approaches have been developed to deal with this problem, which are used to predict new potential associations...

Force Analysis and Evaluation of a Pelvic Support Walking Robot with Joint Compliance.

The force analysis of a pelvic support walking robot with joint compliance is discussed in this paper. During gait training, pelvic motions of hemiple...

Artificial intelligence in drug development: present status and future prospects.

Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex proble...

Localization of Ventricular Activation Origin from the 12-Lead ECG: A Comparison of Linear Regression with Non-Linear Methods of Machine Learning.

We have previously developed an automated localization method based on multiple linear regression (MLR) model to estimate the activation origin on a g...

Robot-Assisted Pedestrian Regulation Based on Deep Reinforcement Learning.

Pedestrian regulation can prevent crowd accidents and improve crowd safety in densely populated areas. Recent studies use mobile robots to regulate pe...

Using machine learning to investigate self-medication purchasing in England via high street retailer loyalty card data.

The availability alongside growing awareness of medicine has led to increased self-treatment of minor ailments. Self-medication is where one 'self' di...

An overview of neural networks for drug discovery and the inputs used.

: Artificial intelligence systems based on neural networks (NNs) find rules for drug discovery according to training molecules, but first, the molecul...

Preliminary study on alginate/NIPAM hydrogel-based soft microrobot for controlled drug delivery using electromagnetic actuation and near-infrared stimulus.

Currently, microrobots are receiving attention because of their small size and motility, which can be applied to minimal invasive therapy. Additionall...

Natural language processing-based assessment of consistency in summaries of product characteristics of generic antimicrobials.

To investigate consistency in summaries of product characteristics (SmPCs) of generic antimicrobials, we used natural language processing (NLP) to ana...

Planar conformity of movements in 3D reaching tasks for persons with Multiple Sclerosis.

Robotic rehabilitation of the upper limb has been proved beneficial for people with Multiple Sclerosis (MS). In order to provide task-specific therapy...

Predicting drug-resistant epilepsy - A machine learning approach based on administrative claims data.

Patients with drug-resistant epilepsy (DRE) are at high risk of morbidity and mortality, yet their referral to specialist care is frequently delayed. ...

A computational strategy for finding novel targets and therapeutic compounds for opioid dependence.

Opioids are widely used for treating different types of pains, but overuse and abuse of prescription opioids have led to opioid epidemic in the United...

Risky Business: Judging the Use of Non-Vitamin K Antagonist Oral Anticoagulants for Non-Valvular Atrial Fibrillation in Patients with Renal Dysfunction.

Warfarin, for many years, was the only oral anticoagulant availablt on the market for the prevention of stroke in patients with atrial fibrillation. D...

Integrating network topology, gene expression data and GO annotation information for protein complex prediction.

The prediction of protein complexes based on the protein interaction network is a fundamental task for the understanding of cellular life as well as t...

Novel Neural Network Approach to Predict Drug-Target Interactions Based on Drug Side Effects and Genome-Wide Association Studies.

AIMS: We propose a novel machine learning approach to expand the knowledge about drug-target interactions. Our method may help to develop effective, l...

A multi-strain probiotic administered via drinking water enhances feed conversion efficiency and meat quality traits in indigenous chickens.

Whereas the use of probiotics is commonplace in commercial production of improved chicken strains, little is known about the impact of these live micr...

The PInSoRo dataset: Supporting the data-driven study of child-child and child-robot social dynamics.

The study of the fine-grained social dynamics between children is a methodological challenge, yet a good understanding of how social interaction betwe...

Drug Repurposing Prediction for Immune-Mediated Cutaneous Diseases using a Word-Embedding-Based Machine Learning Approach.

Immune-mediated diseases affect more than 20% of the population, and many autoimmune diseases affect the skin. Drug repurposing (or repositioning) is ...

Prediction of clinically relevant drug-induced liver injury from structure using machine learning.

Drug-induced liver injury (DILI) is the most common cause of acute liver failure and often responsible for drug withdrawals from the market. Clinical ...