ArticleProceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data2018
Large-Scale Validation of Hypothesis Generation Systems via Candidate Ranking.
Article in Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 25 citations in OpenAlex.
- Quantifying Evidence for Competing Biomedical Hypotheses using Large Language Models and Bayesian Analysis.bioRxiv : the preprint server for biology · 2026Article
- AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia.Artificial intelligence in medicine · 2025Article
- Exposure to angiotensin-converting enzyme inhibitors that cross the blood-brain barrier and the risk of dementia among people with HIV.AIDS (London, England) · 2024Article
- Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique.BMC bioinformatics · 2024Article
- Data-Driven Hypothesis Generation in Clinical Research: What We Learned from a Human Subject Study?Medical research archives · 2024Article
- Exposure to angiotensin-converting enzyme inhibitors that cross the blood-brain barrier and the risk of dementia among patients with human immunodeficiency virus.medRxiv : the preprint server for health sciences · 2024Article
- Using think-aloud protocol to identify cognitive events while generating data-driven scientific hypotheses by inexperienced clinical researchers.AMIA ... Annual Symposium proceedings. AMIA Symposium · 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
- How do clinical researchers generate data-driven scientific hypotheses? Cognitive events using think-aloud protocol.medRxiv : the preprint server for health sciences · 2023Article
- Drug repurposing for COVID-19 via knowledge graph completion.Journal of biomedical informatics · 2021Article
- Inhibition of the Dead Box RNA Helicase 3 Prevents HIV-1 Tat and Cocaine-Induced Neurotoxicity by Targeting Microglia Activation.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2020Article
- Broad-coverage biomedical relation extraction with SemRep.BMC bioinformatics · 2020Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
The first step of many research projects is to define and rank a short list of candidates for study. In the modern rapidity of scientific progress, some turn to automated hypothesis generation (HG) systems to aid this process. These systems can identify implicit or overlooked connections within a large scientific corpus, and while their importance grows alongside the pace of science, they lack thorough validation. Without any standard numerical evaluation method, many validate general-purpose HG systems by rediscovering a handful of historical findings, and some wishing to be more thorough may run laboratory experiments based on automatic suggestions. These methods are expensive, time consuming, and cannot scale. Thus, we present a numerical evaluation framework for the purpose of validating HG systems that leverages thousands of validation hypotheses. This method evaluates a HG system by its ability to rank hypotheses by plausibility; a process reminiscent of human candidate selection. Because HG systems do not produce a ranking criteria, specifically those that produce topic models, we additionally present novel metrics to quantify the plausibility of hypotheses given topic model system output. Finally, we demonstrate that our proposed validation method aligns with real-world research goals by deploying our method within MOLIERE, our recent topic-driven HG system, in order to automatically generate a set of candidate genes related to HIV-associated neurodegenerative disease (HAND). By performing laboratory experiments based on this candidate set, we discover a new connection between HAND and Dead Box RNA Helicase 3 (DDX3). Reproducibility: code, validation data, and results can be found at sybrandt.com/2018/validation.
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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.