Prescriptions

Proactivity of fish and leadership of self-propelled robotic fish during interaction.

Fish interacting with biomimetic robotic fish is beneficial for animal behavior research, particularly in the study of collective behavior. Compared with passive-dragging robotic fish, self-propelled robotic fish floats in water, and its movement matches the flow field formed by the caudal fin oscillation, leading to more realistic interaction with animals. In this paper, we propose a self-propell...

Predicting drug adverse effects using a new Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD).

Electrical data could be a new source of big-data for training artificial intelligence (AI) for drug discovery. A Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) was built using a standardized methodology to test drug effects on electrical gastrointestinal (GI) pacemaker activity. The current report used data obtained from 89 drugs with 4867 datasets to evaluate the potential use of th...

Preliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational status.

The detection of tumour gene mutations by DNA or RNA sequencing is crucial for the prescription of effective targeted therapies. Recent developments s...

Explainability and white box in drug discovery.

Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the challenges in drug discovery. Although traditional AI te...

Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives.

Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe...

Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy.

Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein-protein interactions. The inte...

An ensemble of AHP-EW and AE-RNN for food safety risk early warning.

Food safety problems are becoming increasingly severe in modern society, and establishing an accurate food safety risk warning and analysis model is o...

Adrenal lesion classification with abdomen caps and the effect of ROI size.

Accurate classification of adrenal lesions on magnetic resonance (MR) images are very important for diagnosis and treatment planning. The detection an...

Contextualized medication information extraction using Transformer-based deep learning architectures.

OBJECTIVE: To develop a natural language processing (NLP) system to extract medications and contextual information that help understand drug changes. ...

DrugormerDTI: Drug Graphormer for drug-target interaction prediction.

Drug-target interactions (DTI) prediction is a crucial task in drug discovery. Existing computational methods accelerate the drug discovery in this re...

Digital by design approach to develop a universal deep learning AI architecture for automatic chromatographic peak integration.

Chromatographic data processing has garnered attention due to multiple Food and Drug Administration 483 citations and warning letters, highlighting th...

Diagnosis of gastric cancer based on hybrid genes selection approach.

Gastric cancer (GC) is the third leading cause of cancer death worldwide. In the field of medicine, machine learning is widely used in genetic data mi...

Development and External Validation of an Artificial Intelligence Model for Identifying Radiology Reports Containing Recommendations for Additional Imaging.

Reported rates of recommendations for additional imaging (RAIs) in radiology reports are low. Bidirectional encoder representations from transformers...

Artificial Intelligence-Based Quantitative Structure-Property Relationship Model for Predicting Human Intestinal Absorption of Compounds with Serotonergic Activity.

Oral medicines represent the largest pharmaceutical market area. To achieve a therapeutic effect, a drug must penetrate the intestinal walls, the main...

SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy.

Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations thro...

Do Anthropomorphic Chatbots Increase Counseling Satisfaction and Reuse Intention? The Moderated Mediation of Social Rapport and Social Anxiety.

The growing demand for mental health services and artificial intelligence chatbots to replace human agents have led to increased attention to chatbot ...

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective.

In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the...

Parkinson's disease detection and classification using EEG based on deep CNN-LSTM model.

The progressive loss of motor function in the brain is a hallmark of Parkinson's disease (PD). Electroencephalogram (EEG) signals are commonly used fo...

Static anti-windup compensator design for locally Lipschitz systems under input and output delays.

This paper proposes a static anti-windup compensator (AWC) design methodology for the locally Lipschitz nonlinear systems, containing time-varying int...

Application of artificial intelligence centric workflows for evaluation of neuroradiology emergencies.

The goal of this study was to perform a pilot study to assess user-interface of radiologists with an artificial-intelligence (AI) centric workflow for...