Prescriptions

Integrative analysis of chemical properties and functions of drugs for adverse drug reaction prediction based on multi-label deep neural network.

The prediction of adverse drug reactions (ADR) is an important step of drug discovery and design process. Different drug properties have been employed for ADR prediction but the prediction capability of drug properties and drug functions in integrated manner is yet to be explored. In the present work, a multi-label deep neural network and MLSMOTE based methodology has been proposed for ADR predict...

Prediction of protein-protein interaction using graph neural networks.

Proteins are the essential biological macromolecules required to perform nearly all biological processes, and cellular functions. Proteins rarely carry out their tasks in isolation but interact with other proteins (known as protein-protein interaction) present in their surroundings to complete biological activities. The knowledge of protein-protein interactions (PPIs) unravels the cellular behavio...

Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks.

In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computa...

The Use of BP Neural Network Algorithm and Natural Language Processing in the Impact of Social Audit on Enterprise Innovation Ability.

At present, there are still some problems in the document management of enterprise innovation projects, such as non-standard management, lagging updat...

Assessing the potentiality of algorithms and artificial intelligence adoption to disrupt patient primary care with a safer and faster medication management: a systematic review protocol.

INTRODUCTION: In primary care, almost 75% of outpatient visits by family doctors and general practitioners involve continuation or initiation of drug ...

Artificial Intelligence and Machine Learning for Safe Medicines.

Authors' views on the role of artificial intelligence and machine learning in pharmacovigilance. (MP4  139807 kb).

Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations.

The tools of artificial intelligence (AI) have enormous potential to enhance activities in pharmacovigilance. Pharmacovigilance experts need not be AI...

Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance.

TransCelerate reports on the results of 2019, 2020, and 2021 member company (MC) surveys on the use of intelligent automation in pharmacovigilance pro...

Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review.

INTRODUCTION: Artificial intelligence based on machine learning has made large advancements in many fields of science and medicine but its impact on p...

Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations.

Increasing availability of electronic health databases capturing real-world experiences with medical products has garnered much interest in their use ...

Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources.

With the rapid development of artificial intelligence (AI) technologies, and the large amount of pharmacovigilance-related data stored in an electroni...

Supervised Machine Learning-Based Decision Support for Signal Validation Classification.

INTRODUCTION: Signal validation in pharmacovigilance is the process of evaluating data to decide whether evidence is sufficient to justify further ass...

High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites.

Drug metabolite identification is a bottleneck of drug metabolism studies due to the need for time-consuming chromatographic separation and structural...

Finite-Time Interactive Control of Robots with Multiple Interaction Modes.

This paper proposes a finite-time multi-modal robotic control strategy for physical human-robot interaction. The proposed multi-modal controller consi...

A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients.

In a growing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for implementing complex multi-parametric ...

The Blockchain Technology Applied in the Development of Real Economy in Jiangsu under Deep Learning.

This study focuses on the financing difficulties of small and medium enterprises (SMEs) in China to study the application of blockchain technology in ...

Cascaded Parsing of Human-Object Interaction Recognition.

This paper addresses the task of detecting and recognizing human-object interactions (HOI) in images. Considering the intrinsic complexity and structu...

Memory Recall: A Simple Neural Network Training Framework Against Catastrophic Forgetting.

It is widely acknowledged that biological intelligence is capable of learning continually without forgetting previously learned skills. Unfortunately,...

Reliable CA-(Q)SAR generation based on entropy weight optimized by grid search and correction factors.

Chromosome aberration (CA) is a serious genotoxicity of a compound, leading to carcinogenicity and developmental side effects. In the present manuscri...

In silico prediction of potential drug-induced nephrotoxicity with machine learning methods.

In recent years, drug-induced nephrotoxicity has been one of the main reasons for the failure of drug development. Early prediction of the nephrotoxic...