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Computational network biology: data, models, and applications
Biological entities are involved in intricate and complex interactions, in which uncovering the
biological information from the network concepts are of great significance. Benefiting from …
biological information from the network concepts are of great significance. Benefiting from …
In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts
During drug development, safety is always the most important issue, including a variety of
toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial …
toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial …
Target identification among known drugs by deep learning from heterogeneous networks
Without foreknowledge of the complete drug target information, development of promising
and affordable approaches for effective treatment of human diseases is challenging. Here …
and affordable approaches for effective treatment of human diseases is challenging. Here …
In silico investigation of phytoconstituents from Indian medicinal herb 'Tinospora cordifolia (giloy)' against SARS-CoV-2 (COVID-19) by molecular dynamics approach
P Chowdhury - Journal of Biomolecular Structure and Dynamics, 2021 - Taylor & Francis
The recent appearance of COVID-19 virus has created a global crisis due to unavailability of
any vaccine or drug that can effectively and deterministically work against it. Naturally …
any vaccine or drug that can effectively and deterministically work against it. Naturally …
Identification of drug-side effect association via multiple information integration with centered kernel alignment
In medicine research, drug discovery aims to develop a drug to patients who will benefit from
it and try to avoid some side effects. However, the tradition experiment is time consuming …
it and try to avoid some side effects. However, the tradition experiment is time consuming …
Network-based methods for prediction of drug-target interactions
Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming
and costly to determine DTIs experimentally. Over the past decade, various computational …
and costly to determine DTIs experimentally. Over the past decade, various computational …
Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties
Abstract Objective Drug–drug interactions (DDIs) are an important consideration in both
drug development and clinical application, especially for co-administered medications …
drug development and clinical application, especially for co-administered medications …
[HTML][HTML] Computational prediction of drug-drug interactions based on drugs functional similarities
Therapeutic activities of drugs are often influenced by co-administration of drugs that may
cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and …
cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and …
Application of text mining in the biomedical domain
WWM Fleuren, W Alkema - Methods, 2015 - Elsevier
In recent years the amount of experimental data that is produced in biomedical research and
the number of papers that are being published in this field have grown rapidly. In order to …
the number of papers that are being published in this field have grown rapidly. In order to …
Predicting drug side effects by multi-label learning and ensemble learning
W Zhang, F Liu, L Luo, J Zhang - BMC bioinformatics, 2015 - Springer
Background Predicting drug side effects is an important topic in the drug discovery. Although
several machine learning methods have been proposed to predict side effects, there is still …
several machine learning methods have been proposed to predict side effects, there is still …