AIMC Topic: Drug Interactions
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Binding affinity prediction for binary drug-target interactions using semi-supervised transfer learning.
Journal of computer-aided molecular design
Jun 30, 2021
In the field of drug-target interactions prediction, the majority of approaches formulated the problem as a simple binary classification task. These methods used binary drug-target interaction datasets to train their models. The prediction of drug-ta...
Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes.
BMC bioinformatics
Jun 11, 2021
BACKGROUND: Drug-drug interaction (DDI) is a serious public health issue. The L1000 database of the LINCS project has collected millions of genome-wide expressions induced by 20,000 small molecular compounds on 72 cell lines. Whether this unified and...
Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics.
Molecular diversity
May 24, 2021
Convolutional neural networks (CNNs) have been used to extract information from various datasets of different dimensions. This approach has led to accurate interpretations in several subfields of biological research, like pharmacogenomics, addressing...
An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints.
BioMed research international
Apr 24, 2021
Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be e...
Application of network link prediction in drug discovery.
BMC bioinformatics
Apr 12, 2021
BACKGROUND: Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the f...
Automatically classifying the evidence type of drug-drug interaction research papers as a step toward computer supported evidence curation.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
A longstanding issue with knowledge bases that discuss drug-drug interactions (DDIs) is that they are inconsistent with one another. Computerized support might help experts be more objective in assessing DDI evidence. A requirement for such systems i...
Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects.
Nature communications
Dec 1, 2020
We present comboFM, a machine learning framework for predicting the responses of drug combinations in pre-clinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell context-specific drug interactions through...
Predicting adverse drug reactions of two-drug combinations using structural and transcriptomic drug representations to train an artificial neural network.
Chemical biology & drug design
Oct 16, 2020
Adverse drug reactions (ADRs) are pharmacological events triggered by drug interactions with various sources of origin including drug-drug interactions. While there are many computational studies that explore models to predict ADRs originating from s...
Medical Information Extraction in the Age of Deep Learning.
Yearbook of medical informatics
Aug 21, 2020
OBJECTIVES: We survey recent developments in medical Information Extraction (IE) as reported in the literature from the past three years. Our focus is on the fundamental methodological paradigm shift from standard Machine Learning (ML) techniques to ...
BIOINTMED: integrated biomedical knowledge base with ontologies and clinical trials.
Medical & biological engineering & computing
Jul 24, 2020
Biomedical data are complex and heterogeneous. An ample reliable quantity of data is important for understanding and exploring the domain. The work aims to integrate biomedical data from various heterogeneous sources like dictionaries or corpus and a...
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