Showing 1-25 of 285 resultsfor "Index Terms":DrugBank
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Web Service Solution for Adverse Drug Events and Medication Errors
2018 IEEE International Conference on Big Data (Big Data)
Year: 2018 | Conference Paper |
Investigating the incidence, type, and preventability of adverse drug events (ADE) and medication errors is crucial to improving the quality of health care service. ADEs, medications errors can be extracted from practice data, patients feedback and especially from medication order. In this study, we examine the dataset filled with medication orders and with a web service based approach, each medic...Show More
Efficient performance of complex knowledge work is of crucial importance to saving resources in the global economy and long term sustainability. A lot remains to be leveraged in engineering computer-based systems for assisting humans via cognitive and performance aids. The performance of knowledge-intensive tasks (simply, knowledge-work) involves complex and dynamic interactions between human cogn...Show More
Integrative analysis of chemo-transcriptomic profiles for drug-feature specific gene expression signatures
2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2014 | Conference Paper |
One of the major challenges in systems biology is to understand the complex responses of a biological system to external perturbations or internal signalling depending on its biological conditions. Genome-wide transcriptomic profiling of cellular systems under various chemical perturbations allows the manifestation of certain features of the chemicals through their transcriptomic expression profil...Show More
An Optimized Hamiltonian Quantum Generative Network Based Drug-Drug Interactions Prediction Through Combining Local And Global Features
Seeniappan Kaliappan;R Maranan;M. Muthukannan;Ram Deshmukh;Jegan Gnanasekaran;M. Ramya
2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG)
Year: 2024 | Conference Paper |
This work is important in clinical practice and drug development because DDIs are a major factor that poses serious risks to patients’ safety. To enable medication to be given properly with the least side effects, probable DDIs must be predicted correctly. This work introduces a new Spike driven Transformer based Hamiltonian Quantum Generative Adversarial Network with Gorilla Troops Optimizer (SDT...Show More
DeepH-DTA: Deep Learning for Predicting Drug-Target Interactions: A Case Study of COVID-19 Drug Repurposing
Mohamed Abdel-Basset;Hossam Hawash;Mohamed Elhoseny;Ripon K. Chakrabortty;Michael Ryan
The rapid spread of novel coronavirus pneumonia (COVID-19) has led to a dramatically increased mortality rate worldwide. Despite many efforts, the rapid development of an effective vaccine for this novel virus will take considerable time and relies on the identification of drug-target (DT) interactions utilizing commercially available medication to identify potential inhibitors. Motivated by this,...Show More
Microbial drug target identification using different computational approaches: Specific application to Pseudomonas aeruginosa
2008 International Conference on Innovations in Information Technology
Year: 2008 | Conference Paper |
Cited by: Papers (2)
Traditionally, most bacterial drug targets are identified using expensive and time-consuming genetic screens, biochemical tests and cellular assays. In recent times, drug-target identification by in silico methods has emerged causing a phenomenal achievement in the field of drug discovery. This paper focuses on describing how microbial drug target identification can be carried out using bioinforma...Show More
Computational Drug Discovery Approach for Drug Design against Zika Virus
2018 International Conference on Bioinformatics and Systems Biology (BSB)
Year: 2018 | Conference Paper |
High Throughput Virtual Screening of various candidate molecules to predict the possibility for acting as inhibitors against NS5 protein of Zika Virus is done in this work. The ontology of NS5 protein of ZIKV is to replicate its viral RNA by enzyme RNA Dependent RNA polymerase and mechanize its viral RNA modification functionality by methyltransferase. Objective of the current study is to screen o...Show More
In-Silico Structural Analysis of Membrane-Associated RING-CH Type 2 (MARCH 2) Protein
2023 International Conference on Integration of Computational Intelligent System (ICICIS)
Year: 2023 | Conference Paper |
Ubiquitination involves the binding of ubiquitin to improperly folded proteins to break them down. The E3 ligase enzyme, crucial in the reaction, has a relationship with cancer as it regulates cancer-related promoters or suppressors. The family of E3 ligases known as MARCH proteins control immune system responses and have elevated expression in certain cancers, making them a potential target for c...Show More
In Silico Identification of Inhibitors as Antagonist for HCV Treatment
2018 International Conference on Bioinformatics and Systems Biology (BSB)
Year: 2018 | Conference Paper |
HCV virus is a major cause of liver damage which leads to the Hepatocellular Carcinoma and in most of the cases death of patient. NS3 protein widely recognised for its important role in replication of the virus. The current study deals with the pharmacological analysis and preclinical trials of inhibitors by using in silico techniques. The objective of this study is to screen the potential inhibit...Show More
Mining the biomedical literature to predict shared drug targets in DrugBank
2017 XLIII Latin American Computer Conference (CLEI)
Year: 2017 | Conference Paper |
The current drug development pipelines are characterised by long processes with high attrition rates and elevated costs. More than 80% of new compounds fail in the later stages of testing due to severe side-effects caused by unknown biomolecular targets of the compounds. In this work, we present a measure that can predict shared targets for drugs in DrugBank through large scale analysis of the bio...Show More
Leveraging Textual Drug Information for Effective Drug-Drug Interaction Identification
2024 Innovations in Intelligent Systems and Applications Conference (ASYU)
Year: 2024 | Conference Paper |
Identification of drug-drug interaction (DDI) seeks to determine whether two drugs influence each other's mechanisms within the human body. This study focuses on evaluating textual fields for the purpose of identifying DDIs. Specifically, twelve distinct textual fields that describe drug characteristics were utilized, with data obtained from DrugBank. The textual fields that demonstrated a signifi...Show More
Detecting and Classifying Drug Interaction using Data mining Techniques
2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)
Year: 2022 | Conference Paper |
Cited by: Papers (40)
Thousands of licensed drugs are now available to patients. The Warnings and Precautions section of a medication's label was created to identify and characterize severe or clinically significant adverse reactions and other potential safety issues. This method gathers a patient's gender, age, pregnancy status, and current health issues. The suggested method involves data collecting and analysis. We ...Show More
Drug drug interaction extraction from literature using a skeleton long short term memory neural network
2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2017 | Conference Paper |
Cited by: Papers (16)
Drug Drug Interactions (DDIs) can cause harmful effect. Two shared tasks, DDIExtraction 2011 and DDIExtraction 2013, have been held to promote the implementation and comparative assessment of natural language processing techniques in the field of the pharmacovigilance domain. However, few model can meanwhile achieve state-of-the-art performance on both tasks. A major reason is the lack of represen...Show More
An ensemble approach for drug side effect prediction
2013 IEEE International Conference on Bioinformatics and Biomedicine
Year: 2013 | Conference Paper |
Cited by: Papers (14)
In silico prediction of drug side-effects in early stage of drug development is becoming more popular now days, which not only reduces the time for drug design but also reduces the drug development costs. In this article we propose an ensemble approach to predict drug side-effects of drug molecules based on their chemical structure. Our idea originates from the observation that similar drugs have ...Show More
Question answering for biology and medicine
J. Gobeill;E. Patsche;D. Theodoro;A.-L. Veuthey;C. Lovis;P. Ruch
2009 9th International Conference on Information Technology and Applications in Biomedicine
Year: 2009 | Conference Paper |
Cited by: Papers (13)
Biomedical professionals have at their disposal a huge amount of data, such as literature, i.e. textual contents, or databases, i.e. structured contents. But when they have a question, they often have to deal with too many documents in order to efficiently find the appropriate answer in a reasonable time. We have developed a Question Answering system which aims to analyze the user's question, to r...Show More
Drug-Drug Interaction Extraction from Biomedical Texts via Relation BERT
2020 RIVF International Conference on Computing and Communication Technologies (RIVF)
Year: 2020 | Conference Paper |
Cited by: Papers (11)
There is a large number of drugs introduced every year and a number of interactions between drugs also has quick growth. As a result, biomedical texts following new drugs and interactions expand [15]. Several published studies of drug safety have revealed that drug-drug interactions (DDIs) may be detected too late, when millions of patients have already been exposed [25]. Therefore, the management...Show More
Semantic Relation Extraction for Herb-Drug Interactions from the Biomedical Literature Using an Unsupervised Learning Approach
2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)
Year: 2018 | Conference Paper |
Cited by: Papers (8)
Sharing principles of drug-drug interaction, herb-drug interaction (HDI) investigates the impacts of herb-based products on activities of other conventional drugs when combining them in certain medical treatments. For years, patients using herb-based medications have built a misconception about the absolute safety of products derived from natural sources. The current fact revealed that patients ha...Show More
GAN Based Approach for Drug Design
Aninditha Ramesh;Anusha S Rao;Sanjana Moudgalya;K S Srinivas
2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)
Year: 2021 | Conference Paper |
Cited by: Papers (7)
Deep Learning models have been a tremendous breakthrough in the field of Drug discovery, greatly simplifying the pre-clinical phase of this intricate task. With an intention to ease this further, we introduce a novel method to generate target-specific molecules using a Generative Adversarial Network (GAN). The dataset consists of drugs whose target proteins belong to the class of Tyrosine kinase a...Show More
CNN-DDI: A novel deep learning method for predicting drug-drug interactions
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2020 | Conference Paper |
Cited by: Papers (6)
Predicting drug-drug interactions (DDIs) is one of the major concerns in patients' medication, which is crucial for patient safety and public health. Most of studies study whether drugs interact or not. In this study, we focus on 65 categories of drug-drug interaction-associated events and proposed a new method based on convolutional neural network (CNN), named CNN-DDI, for predicting DDIs. First,...Show More
The Era of Bioinformatics
2006 2nd International Conference on Information & Communication Technologies
Year: 2006 | Conference Paper |
Cited by: Papers (6)
The recent technological developments in the field of molecular biology have vastly contributed in feeding the scientific research community with massive amounts of biological data that will be eventually stored and analyzed. Scientists and researchers involved in the field of molecular biology are facing the challenge of analyzing and interpreting massive numbers of Cs, Gs, As and Ts sequences th...Show More
Drug Repositioning for SARS-CoV-2 Based on Graph Neural Network
Haifeng Liu;Hongfei Lin;Chen Shen;Liang Yang;Yuan Lin;Bo Xu;Zhihao Yang;Jian Wang;Yuanyuan Sun
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2020 | Conference Paper |
Cited by: Papers (6)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the strain of coronavirus that causes coronavirus disease 2019 (COVID-19), which leads to over 800,000 deaths and is still no specific medicines. Drug repositioning aiming to infer potential drugs for diseases and achieve much attention during the SARS-CoV-2 epidemic. However, find a specific drug of SARS-CoV-2 is still a large challe...Show More
A Systematic Framework for Drug Repurposing based on Literature Mining
2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2019 | Conference Paper |
Cited by: Papers (4)
Drug repurposing, referring to the discovery of new indications for an existing drug, has become an important aspect of drug development. This study seeks to extract hidden disease-drug associations from existing literature. Specifically, we propose a systematic framework assuming that a drug has a new use for a certain disease if the clinical disease symptoms and clinical drug effects complement ...Show More
A novel approach based on deep residual learning to predict drug’s anatomical therapeutic chemical code
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2020 | Conference Paper |
Cited by: Papers (4)
Correctly identifying the potential Anatomical Therapeutic Chemical (ATC) codes for drugs can accelerate drug development and reduce the cost of experiments. However, most of the existing methods only analyze the first-level ATC code of drugs and lack of the ability to learn basic features from sparsely known drug-ATC code associations. In this paper, we propose a novel method based on deep residu...Show More
Semantic Inference for Pharmacokinetic Drug-Drug Interactions
2014 IEEE International Conference on Semantic Computing
Year: 2014 | Conference Paper |
Cited by: Papers (4)
Drug-drug interaction (DDI) study is an important aspect of therapy management and drug efficacy. DDI study investigates how drugs interact with each other and determine whether these interactions may lead to dire effects or nullify the therapeutic effects of each other. In this paper we model metabolic pathways of drugs that include the reaction effects between drugs and the related enzymes. By m...Show More
The persistent use of antibiotics for many years has transformed numerous organisms into multiple drug resistant. This has made the domain of drug discovery vital to cure several infectious diseases. Staphylococcus aureus is a Gram-positive bacterium that has developed resistance to multiple drugs. The main objective of this study is to identify the potential drug targets. The subtractive genomics...Show More
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