ArticleBioMed research international2015
Supervised Learning Based Hypothesis Generation from Biomedical Literature.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 20 citations in OpenAlex.
- Learning to Rank Complex Biomedical Hypotheses for Accelerating Scientific Discovery.Proceedings. IEEE International Conference on Healthcare Informatics · 2024Article
- Data-Driven Hypothesis Generation in Clinical Research: What We Learned from a Human Subject Study?Medical research archives · 2024Article
- Data-driven hypothesis generation among inexperienced clinical researchers: A comparison of secondary data analyses with visualization (VIADS) and other tools.Journal of clinical and translational science · 2024Article
- Data-driven hypothesis generation among inexperienced clinical researchers: A comparison of secondary data analyses with visualization (VIADS) and other tools.medRxiv : the preprint server for health sciences · 2023Article
- Computational Literature-based Discovery for Natural Products Research: Current State and Future Prospects.Frontiers in bioinformatics · 2022Review
- A systematic review on literature-based discovery workflow.PeerJ. Computer science · 2019Article
- SemaTyP: a knowledge graph based literature mining method for drug discovery.BMC bioinformatics · 2018Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, the amount of biomedical literatures is growing at an explosive speed, and there is much useful knowledge undiscovered in this literature. Researchers can form biomedical hypotheses through mining these works. In this paper, we propose a supervised learning based approach to generate hypotheses from biomedical literature. This approach splits the traditional processing of hypothesis generation with classic ABC model into AB model and BC model which are constructed with supervised learning method. Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction (IE) from texts. Then through combining the two models, the approach reconstructs the ABC model and generates biomedical hypotheses from literature. The experimental results on the three classic Swanson hypotheses show that our approach outperforms SemRep system.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.