Prescriptions

Association study based on topological constraints of protein-protein interaction networks.

The non-random interaction pattern of a protein-protein interaction network (PIN) is biologically informative, but its potentials have not been fully utilized in omics studies. Here, we propose a network-permutation-based association study (NetPAS) method that gauges the observed interactions between two sets of genes based on the comparison between permutation null models and the empirical networ...

Visual interaction networks: A novel bio-inspired computational model for image classification.

Inspired by biological mechanisms and structures in neuroscience, many biologically inspired visual computational models have been presented to provide new solutions for visual recognition task. For example, convolutional neural network (CNN) was proposed according to the hierarchical structure of biological vision, which could achieve superior performance in large-scale image classification. In t...

Finding undiagnosed patients with hepatitis C infection: an application of artificial intelligence to patient claims data.

Hepatitis C virus (HCV) remains a significant public health challenge with approximately half of the infected population untreated and undiagnosed. In...

Supervised Machine Learning Algorithms for Bioelectromagnetics: Prediction Models and Feature Selection Techniques Using Data from Weak Radiofrequency Radiation Effect on Human and Animals Cells.

The emergence of new technologies to incorporate and analyze data with high-performance computing has expanded our capability to accurately predict an...

A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports.

Chemical event evolutionary graph (CEEG) is an effective tool to perform safety analysis, early warning, and emergency disposal for chemical accidents...

Exploring the mechanism of TCM formulae in the treatment of different types of coronary heart disease by network pharmacology and machining learning.

Traditional Chinese medicine (TCM) has long been used in the clinical treatment of coronary heart disease (CHD). TCM is characterized by syndrome-base...

How Will Machine Learning Inform the Clinical Care of Atrial Fibrillation?

Machine learning applications in cardiology have rapidly evolved in the past decade. With the availability of machine learning tools coupled with vast...

Modelling Multimodal Dialogues for Social Robots Using Communicative Acts.

Social Robots need to communicate in a way that feels natural to humans if they are to effectively bond with the users and provide an engaging interac...

Touching the social robot PARO reduces pain perception and salivary oxytocin levels.

Human-human social touch improves mood and alleviates pain. No studies have so far tested the effect of human-robot emotional touch on experimentally ...

Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer.

Colonoscopy is commonly used to screen for colorectal cancer (CRC). We develop a deep learning model called CRCNet for optical diagnosis of CRC by tra...

Deep learning based methods for gamma ray interaction location estimation in monolithic scintillation crystal detectors.

In this work, we explore deep learning based techniques using the information from mean detector response functions (MDRFs) as a new method to estimat...

Chemical Degradation of Intravenous Chemotherapy Agents and Opioids by a Novel Instrument.

To assess chemical degradation of various liquid chemotherapy and opioid drugs in the novel RxDestruct™ instrument. Intravenous (IV) drug solutions ...

An innovative method for screening and evaluating the degree of diabetic retinopathy and drug treatment based on artificial intelligence algorithms.

Current methods of evaluating the degree of diabetic retinopathy are highly subjective and have no quantitative standard. To objectively evaluate the ...

Deep learning for symbols detection and classification in engineering drawings.

Engineering drawings are commonly used in different industries such as Oil and Gas, construction, and other types of engineering. Digitising these dra...

Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning.

Drug-drug interactions (DDIs) are crucial for public health and patient safety, which has aroused widespread concern in academia and industry. The exi...

Breast ultrasound region of interest detection and lesion localisation.

In current breast ultrasound computer aided diagnosis systems, the radiologist preselects a region of interest (ROI) as an input for computerised brea...

Adaptive robot mediated upper limb training using electromyogram-based muscle fatigue indicators.

Studies on improving the adaptability of upper limb rehabilitation training do not often consider the implications of muscle fatigue sufficiently. In ...

Can Building "Artificially Intelligent Cities" Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar's Perspective.

In recent years, artificial intelligence (AI) has started to manifest itself at an unprecedented pace. With highly sophisticated capabilities, AI has ...

Extracting drug-drug interactions from texts with BioBERT and multiple entity-aware attentions.

Drug-drug interactions (DDIs) extraction is one of the important tasks in the field of biomedical relation extraction, which plays an important role i...

Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data.

The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mut...