Prescriptions

Comparison of ACM and CLAMP for Entity Extraction in Clinical Notes.

Rapid increase in adoption of electronic health records in health care institutions has motivated the use of entity extraction tools to extract meaningful information from clinical notes with unstructured and narrative style. This paper investigates the performance of two such tools in automatic entity extraction. In specific, this work focuses on automatic medication extraction performance of Ama...

Deep Learning for Predicting Gamma-Ray Interaction Positions in LYSO Detector.

Positron Emission Tomography (PET) is among the most commonly used medical imaging modalities in clinical practice, especially for oncological applications. In contrast to conventional imaging modalities like X-ray Computed Tomography (CT) or Magnetic Resonance Imaging (MRI), PET retrieves in vivo information about biochemical processes rather than just anatomical structures. However, physical lim...

Modelling Drug-Target Binding Affinity using a BERT based Graph Neural network.

Understanding the interactions between novel drugs and target proteins is fundamentally important in disease research as discovering drug-protein inte...

Understanding the Gap Between Information Models and Realism-Based Ontologies Using the Generic Component Model.

The wide-spread use of Common Data Models and information models in biomedical informatics encourages assumptions that those models could provide the ...

[Research Progress on the Application of Artificial Intelligence in Rehabilitation Medicine in China].

The development of science and technology and the increasing demand of rehabilitation have driven the integration between artificial intelligence and ...

Efficacy and safety of a switch from twice-daily tacrolimus to once-daily generic tacrolimus in stable liver transplant patients.

BACKGROUND: Once-daily tacrolimus reduces non-compliance relative to twice-daily tacrolimus. However, little is known about the safety and efficacy of...

SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization.

MOTIVATION: Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detec...

Deep learning identifies synergistic drug combinations for treating COVID-19.

Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory synd...

DeepADEMiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter.

OBJECTIVE: Research on pharmacovigilance from social media data has focused on mining adverse drug events (ADEs) using annotated datasets, with public...

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.

The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differen...

Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design.

Artificial intelligence (AI) based drug design has demonstrated great potential to fundamentally change the pharmaceutical industries. Currently, a ke...

An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction.

Accurately identifying potential drug-target interactions (DTIs) is a key step in drug discovery. Although many related experimental studies have been...

Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction.

Structure-based virtual screenings (SBVSs) play an important role in drug discovery projects. However, it is still a challenge to accurately predict t...

DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration.

Recent pharmacogenomic studies that generate sequencing data coupled with pharmacological characteristics for patient-derived cancer cell lines led to...

Integrating multi-scale neighbouring topologies and cross-modal similarities for drug-protein interaction prediction.

MOTIVATION: Identifying the proteins that interact with drugs can reduce the cost and time of drug development. Existing computerized methods focus on...

Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19.

Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being condu...

Hysteresis modeling and compensation of a rotary series elastic actuator with nonlinear stiffness.

Series elastic actuators (SEAs) have widely been adapted in robots where safe human-robot interaction is required for accurate and robust force contro...

A model for predicting drug-disease associations based on dense convolutional attention network.

The development of new drugs is a time-consuming and labor-intensive process. Therefore, researchers use computational methods to explore other therap...

Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning.

Most studies on drug addiction degree are made based on statistical scales, addicts' account, and subjective judgement of rehabilitation doctors. No o...

PharmGKB, an Integrated Resource of Pharmacogenomic Knowledge.

The Pharmacogenomics Knowledgebase (PharmGKB) is an integrated online knowledge resource for the understanding of how genetic variation contributes to...