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Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 2019 - 2028

https://doi.org/10.1145/2783258.2788609

Published: 10 August 2015 Publication History

Abstract

We present KnIT, the Knowledge Integration Toolkit, a system for accelerating scientific discovery and predicting previously unknown protein-protein interactions. Such predictions enrich biological research and are pertinent to drug discovery and the understanding of disease. Unlike a prior study, KnIT is now fully automated and demonstrably scalable. It extracts information from the scientific literature, automatically identifying direct and indirect references to protein interactions, which is knowledge that can be represented in network form. It then reasons over this network with techniques such as matrix factorization and graph diffusion to predict new, previously unknown interactions. The accuracy and scope of KnIT's knowledge extractions are validated using comparisons to structured, manually curated data sources as well as by performing retrospective studies that predict subsequent literature discoveries using literature available prior to a given date. The KnIT methodology is a step towards automated hypothesis generation from text, with potential application to other scientific domains.

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Index Terms

Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms

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Published In

cover image ACM Conferences

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2015

2378 pages

ISBN:9781450336642

DOI:10.1145/2783258

General Chairs:
Longbing Cao
University of Technology, Sydney
,
Chengqi Zhang
University of Technology, Sydney
,
Program Chairs:
Thorsten Joachims
Cornell University
,
Geoff Webb
Monash University
,
Dragos D. Margineantu
Boeing Research
,
Graham Williams
Australian Taxation Office

Copyright © 2015 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 10 August 2015

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KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 10 - 13, 2015

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KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://doi.org/10.1016/j.burnso.2023.07.001
Stork LTiddi ISpijker Rten Teije A(2023)Explainable Drug Repurposing in Context via Deep Reinforcement LearningThe Semantic Web10.1007/978-3-031-33455-9_1(3-20)Online publication date: 28-May-2023
https://dl.acm.org/doi/10.1007/978-3-031-33455-9_1
Gul HAmin AAdnan AHuang K(2021)A Systematic Analysis of Link Prediction in Complex NetworkIEEE Access10.1109/ACCESS.2021.30539959(20531-20541)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3053995
Pappu SPasumarthy RBhatt N(2021)Predicting unknown directed links of conserved networks from flow dataJournal of Complex Networks10.1093/comnet/cnab0379:6Online publication date: 18-Nov-2021
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de Haan RTiddi IBeek W(2021)Discovering Research Hypotheses in Social Science Using Knowledge Graph EmbeddingsThe Semantic Web10.1007/978-3-030-77385-4_28(477-494)Online publication date: 6-Jun-2021
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