Interpretable Visualization of Scientific Hypotheses in Literature-based Discovery

Tyagin, Ilya; Safro, Ilya

doi:10.1101/2021.10.29.466471

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…Future work includes integration of the benchmarking process in the hypothesis system visualization [48], spreading to other than biomedical areas [49], integration of novel importance measures, and healthcare benchmarking cases. [42] is a publicly available database focused on collecting the information about environmental exposures effects on human health.…”

Section: Discussionmentioning

confidence: 99%

Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique

Tyagin,

Safro

2024

BMC Bioinformatics

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Background Automated hypothesis generation (HG) focuses on uncovering hidden connections within the extensive information that is publicly available. This domain has become increasingly popular, thanks to modern machine learning algorithms. However, the automated evaluation of HG systems is still an open problem, especially on a larger scale. Results This paper presents a novel benchmarking framework Dyport for evaluating biomedical hypothesis generation systems. Utilizing curated datasets, our approach tests these systems under realistic conditions, enhancing the relevance of our evaluations. We integrate knowledge from the curated databases into a dynamic graph, accompanied by a method to quantify discovery importance. This not only assesses hypotheses accuracy but also their potential impact in biomedical research which significantly extends traditional link prediction benchmarks. Applicability of our benchmarking process is demonstrated on several link prediction systems applied on biomedical semantic knowledge graphs. Being flexible, our benchmarking system is designed for broad application in hypothesis generation quality verification, aiming to expand the scope of scientific discovery within the biomedical research community. Conclusions Dyport is an open-source benchmarking framework designed for biomedical hypothesis generation systems evaluation, which takes into account knowledge dynamics, semantics and impact. All code and datasets are available at: https://github.com/IlyaTyagin/Dyport.

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Section: Discussionmentioning

confidence: 99%

Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique

Tyagin,

Safro

2024

BMC Bioinformatics

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“…EMMAA ( 27 ) and AGATHA ( 28 ) leverage COVID-19 data retrieved from the literature to create models and generate hypotheses to help researchers discover explicit or implicit connections between biomedical entities. EMMAA is a framework for extracting causal and mechanistic relations for COVID-19.…”

Section: Participating Teamsmentioning

confidence: 99%

Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII

et al. 2022

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The coronavirus disease 2019 (COVID-19) pandemic has compelled biomedical researchers to communicate data in real time to establish more effective medical treatments and public health policies. Nontraditional sources such as preprint publications, i.e. articles not yet validated by peer review, have become crucial hubs for the dissemination of scientific results. Natural language processing (NLP) systems have been recently developed to extract and organize COVID-19 data in reasoning systems. Given this scenario, the BioCreative COVID-19 text mining tool interactive demonstration track was created to assess the landscape of the available tools and to gauge user interest, thereby providing a two-way communication channel between NLP system developers and potential end users. The goal was to inform system designers about the performance and usability of their products and to suggest new additional features. Considering the exploratory nature of this track, the call for participation solicited teams to apply for the track, based on their system’s ability to perform COVID-19-related tasks and interest in receiving user feedback. We also recruited volunteer users to test systems. Seven teams registered systems for the track, and >30 individuals volunteered as test users; these volunteer users covered a broad range of specialties, including bench scientists, bioinformaticians and biocurators. The users, who had the option to participate anonymously, were provided with written and video documentation to familiarize themselves with the NLP tools and completed a survey to record their evaluation. Additional feedback was also provided by NLP system developers. The track was well received as shown by the overall positive feedback from the participating teams and the users. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-4/

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SCIHYPO - A Deep Learning Framework for Data-Driven Scientific Hypothesis Generation from Extensive Literature Analysis

Tadiparthi,

Pasupuleti,

Sathwika Talluri

et al. 2024

2024 International Conference on Expert Clouds and Applications (ICOECA)

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Interpretable Visualization of Scientific Hypotheses in Literature-based Discovery

Cited by 3 publications

References 46 publications

Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique

Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique

Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII

SCIHYPO - A Deep Learning Framework for Data-Driven Scientific Hypothesis Generation from Extensive Literature Analysis

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