Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning-Reference-Cited by-同舟云学术

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Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

Published:2022-05-30 Issue:2 Volume:100 Page:169-184
ISSN:1747-0277
Container-title:Chemical Biology & Drug Design
language:en
Short-container-title:Chem Biol Drug Des

Author:

Murali Vidhya¹,Muralidhar Y. Pradyumna²,Königs Cassandra³,Nair Meera⁴,Madhu Sethulekshmi⁴,Nedungadi Prema⁵,Srinivasa Gowri²,Athri Prashanth¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering Amrita School of Engineering Bengaluru India

2. PES Center for Pattern Recognition, Department of Computer Science and Engineering PES University Bengaluru India

3. Bioinformatics and Medical Informatics Bielefeld University Northrhine‐Westphalia Germany

4. Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri Kerala India

5. Department of Computer Science and Engineering Amrita School of Engineering Kerala India

Publisher

Wiley

Subject

Molecular Medicine,Biochemistry,Drug Discovery,Pharmacology,Organic Chemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/cbdd.14092

Reference83 articles.

1. An experimental comparison of cross-validation techniques for estimating the area under the ROC curve

2. A New Approach for Drug Target and Bioactivity Prediction: The Multifingerprint Similarity Search Algorithm (MuSSeL)

3. Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks

4. The ChEMBL bioactivity database: an update

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1. Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study;Journal of Biomedical Informatics;2023-07

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