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Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed…
Preventable adverse drug reactions as a result of medical errors present a growing concern in modern medicine. As drug-drug interactions (DDIs) may cause adverse reactions, being able to extracting DDIs from drug labels into…
Concomitant administration of drugs can cause drug-drug interactions (DDIs). Some drug combinations are beneficial, but other ones may cause negative effects which are previously unrecorded. Previous works on DDI prediction usually rely on…
Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. However, only 4% of PPIs are annotated with PTMs in biological knowledge databases such as IntAct, mainly performed…
Biomedical named entity recognition (BNER) serves as the foundation for numerous biomedical text mining tasks. Unlike general NER, BNER require a comprehensive grasp of the domain, and incorporating external knowledge beyond training data…
Background: Medication review is a structured interview of the patient, performed by the pharmacist and aimed at optimizing drug treatments. In practice, medication review is a long and cognitively-demanding task that requires specific…
Drug overdose remains a critical global health issue, often driven by misuse of opioids, painkillers, and psychiatric medications. Traditional research methods face limitations, whereas social media offers real-time insights into…
Diseases involve complex processes and modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, biological knowledge pertaining to a disease can…
Medical relation extraction discovers relations between entity mentions in text, such as research articles. For this task, dependency syntax has been recognized as a crucial source of features. Yet in the medical domain, 1-best parse trees…
Relation extraction is a core problem for natural language processing in the biomedical domain. Recent research on relation extraction showed that prompt-based learning improves the performance on both fine-tuning on full training set and…
In this research, we explored the improvement in terms of multi-class disease classification via pre-trained language models over Medical-Abstracts-TC-Corpus that spans five medical conditions. We excluded non-cancer conditions and examined…
Predicting and discovering drug-drug interactions (DDIs) using machine learning has been studied extensively. However, most of the approaches have focused on text data or textual representation of the drug structures. We present the first…
The versatility of large language models (LLMs) has been explored across various sectors, but their application in healthcare poses challenges, particularly in the domain of pharmaceutical contraindications where accurate and reliable…
The objective of this research is to introduce a network specialized in predicting drugs that can be repurposed by investigating real-world evidence sources, such as clinical trials and biomedical literature. Specifically, it aims to…
Current approaches for clinical information extraction are inefficient in terms of computational costs and memory consumption, hindering their application to process large-scale electronic health records (EHRs). We propose an efficient…
RNA-RNA interaction (RRI) is ubiquitous and has complex roles in the cellular functions. In human health studies, miRNA-target and lncRNAs are among an elite class of RRIs that have been extensively studied. Bacterial ncRNA-target and RNA…
Medical Relation Extraction (MRE) task aims to extract relations between entities in medical texts. Traditional relation extraction methods achieve impressive success by exploring the syntactic information, e.g., dependency tree. However,…
This study is dedicated to exploring the application of prompt learning methods to advance Named Entity Recognition (NER) within the medical domain. In recent years, the emergence of large-scale models has driven significant progress in NER…
In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction and combination therapy design tasks have been proposed. Here, we present a unified…
Electronic Health Records maintained in health care settings are a potential source of substantial clinical knowledge. The massive volume of data, unstructured nature of records and obligatory requirement of domain acquaintance together…