Extracting Biological Pathway Models From NLP Event Representations

Spranger, Michael; Palaniappan, Sucheendra K.; Ghosh, Samik

doi:10.18653/v1/w15-3805

Cited by 5 publications

(8 citation statements)

References 14 publications

(16 reference statements)

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“…gene names, abbreviations, etc. ), inclusion of non-textual material in biomedical event extraction pipelines, and the automatic integration of event extraction models with biological pathways (facilitate the understanding of biological interactions) [102,2,54].…”

Section: Discussionmentioning

confidence: 99%

Event Extraction from Biomedical Literature

Basher

Purdy

Birol

2015

Preprint

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The breadth and scope of the biomedical literature hinders a timely and thorough comprehension of its content. PubMed, the leading repository for biomedical literature, currently holds over 26 million records, and is growing at a rate of over 1.2 million records per year, with about 300 records added daily that mention 'cancer' in the title or abstract. Natural language processing (NLP) can assist in accessing and interpreting this massive volume of literature, including its quality. NLP approaches to the automatic extraction of biomedical entities and relationships may assist the development of explanatory models that can comprehensively scan and summarize biomedical articles for end users. Users can also formulate structured queries against these entities, and their interactions, to mine the latest developments in related areas of interest. In this article, we explore the latest advances in automated event extraction methods in the biomedical domain, focusing primarily on tools participated in the Biomedical NLP (BioNLP) Shared Task (ST) competitions. We review the leading BioNLP methods, summarize their results, and their innovative contributions in this field.

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Section: Discussionmentioning

confidence: 99%

Event Extraction from Biomedical Literature

Basher

Purdy

Birol

2015

Preprint

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“…Methods such as model-based reinforcement learning and generative adversarial learning can be applied initially to investigate how to develop system that learn laws of nature at scale. Some recent studies demonstrate deep learning networks trained over millions of articles generate extensive molecular interactions 31 and the potential relationship between molecules and disease only using articles a year (or years) before such a relationship was discovered 32 . Deep learning was also used to uncover hierarchical structure and functions of cells 33 , deep generative models for discovering hidden structures 34 , precision phenotyping to predict genetic anomalies 35 , and many more.…”

Section: Experimental Contextmentioning

confidence: 99%

Nobel Turing Challenge: creating the engine for scientific discovery

Kitano

2021

npj Syst Biol Appl

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Scientific discovery has long been one of the central driving forces in our civilization. It uncovered the principles of the world we live in, and enabled us to invent new technologies reshaping our society, cure diseases, explore unknown new frontiers, and hopefully lead us to build a sustainable society. Accelerating the speed of scientific discovery is therefore one of the most important endeavors. This requires an in-depth understanding of not only the subject areas but also the nature of scientific discoveries themselves. In other words, the “science of science” needs to be established, and has to be implemented using artificial intelligence (AI) systems to be practically executable. At the same time, what may be implemented by “AI Scientists” may not resemble the scientific process conducted by human scientist. It may be an alternative form of science that will break the limitation of current scientific practice largely hampered by human cognitive limitation and sociological constraints. It could give rise to a human-AI hybrid form of science that shall bring systems biology and other sciences into the next stage. The Nobel Turing Challenge aims to develop a highly autonomous AI system that can perform top-level science, indistinguishable from the quality of that performed by the best human scientists, where some of the discoveries may be worthy of Nobel Prize level recognition and beyond.

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“…These in principle correspond to biological species and reactions. We translate the NLP representation into SBML path-ways and perform additional annotation (Spranger et al, 2015) of species and reactions. For the sentence in Figure 2, the extracted SBML is visualized in Figure 2.…”

Section: Bridging the Gapmentioning

confidence: 99%

“…The 501 papers were processed using the Turku Event Extraction System mentioned earlier. From the extracted NLP events we created SBML representations of pathway maps for each text using (Spranger et al, 2015). The SBML was further annotated using various tools (discussed below) and, finally, loaded into a single pathway map.…”

Section: Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

Measuring the State of the Art of Automated Pathway Curation Using Graph Algorithms - A Case Study of the mTOR Pathway

Spranger

Palaniappan²,

Ghosh

2016

Proceedings of the 15th Workshop on Biomedical Natural Language Processing

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This paper evaluates the difference between human pathway curation and current NLP systems. We propose graph analysis methods for quantifying the gap between human curated pathway maps and the output of state-of-the-art automatic NLP systems. Evaluation is performed on the popular mTOR pathway. Based on analyzing where current systems perform well and where they fail, we identify possible avenues for progress.

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Extracting Biological Pathway Models From NLP Event Representations

Cited by 5 publications

References 14 publications

Event Extraction from Biomedical Literature

Event Extraction from Biomedical Literature

Nobel Turing Challenge: creating the engine for scientific discovery

Measuring the State of the Art of Automated Pathway Curation Using Graph Algorithms - A Case Study of the mTOR Pathway

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