External Evaluation of Event Extraction Classifiers for Automatic
            Pathway Curation: An extended study of the mTOR pathway

Kusa, Wojciech; Spranger, Michael

doi:10.18653/v1/w17-2331

Cited by 3 publications

(4 citation statements)

References 26 publications

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“…Several strategies [3][4][5][6][7][8][9] have been proposed to determine the cause-effect relation from texts without the cause-effect series consideration except [8]. Girju [3] proposed decision tree learning the causal relation from a sentence based on the lexico syntactic pattern (NP1 causal-verb NP2).…”

Section: Related Workmentioning

confidence: 99%

“…The model's [8] is based on noun features including hidden causal chains solved by latent topics. Events of automatic pathway curation using the popular mTOR pathway (mTOR is a kinase that in humans is encoded by the MTOR gene) [9] were extracted by using different training datasets and learning algorithms. Their event extraction based on the noun derivative extracts the entities (genes, proteins etc), reactions (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…The EDU expression has the following Thai linguistic patterns after stemming words and the stop word removal. There are several techniques [3][4][5][6][7][8][9] having been applied for determining the causeeffect/causality/causal relation but not including the stimulation relation from texts (see section 2). However, the Thai documents have several specific characteristics, such as zero anaphora or the implicit noun phrase, without word and sentence delimiters, and etc.…”

Section: Introductionmentioning

confidence: 99%

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Event-Concept Pair Series Extraction to Represent Medical Complications from Texts

Pechsiri¹,

Phainoun²

2018

IJEECS

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<p>This research aims to determine an event-concept pair series as consequent events, particularly a Cause-Effect-concept pair (called ‘CEpair’) series on disease documents downloaded from hospital-web-boards. CEpair series are used for representing medical/disease complications which benefit for Solving system. Each causative/effect event concept is expressed by a verb phrase of an elementary discourse unit (EDU) which is a simple sentence. The research has three problems; how to determine each adjacent-EDU pair having the cause-effect relation, how to determine a CEpair series mingled with non-causeeffect-relation EDUs, and how to identify the complication of several extracted CEpair series from the documents. Therefore, we extract NWordCo-concept set having the causative/effect concepts from EDUs’ verb phrases including a support vector machine to solve each NWordCo size. We apply the Naïve Bayes classifier to learn and extract an NWordCoconcept pair set as a knowledge template having the cause-effect relation from the documents. We then propose using the knowledge template to extract several CEpair series. We also apply the intersection of the NWordCo-concept sets to identify the commoncause/effect for representing the complication-development parts of the extracted-CEpair series. The research results provide the high percent correctness of the CEpair-series determination from the documents.</p>

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Event-Concept Pair Series Extraction to Represent Medical Complications from Texts

Pechsiri¹,

Phainoun²

2018

IJEECS

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“…(19) introduced a joint framework for document-level biomedical event extraction, employing a dependency-based Graph Convolutional Network (GCN) for local context and a hypergraph for global context. In their study, (20) provided a comprehensive evaluation of various datasets using the TEES 2.2 system. More recently, (21) employed multiple natural language processing systems alongside the Integrated Network and Dynamical Reasoning Assembler (INDRA), while (22) compared human and machine-curated HCM molecular mechanisms models using the INDRA system.…”

Section: Introductionmentioning

confidence: 99%

Large Language Models for Pathway Curation: A Preliminary Investigation

Karkera,

Kumar

et al. 2024

Preprint

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The pathway curation task involves analyzing scientific literature to identify and represent cellular processes as pathways. This process, often time-consuming and labor-intensive, requires significant curation efforts amidst the rapidly growing biomedical literature. Natural Language Processing (NLP) offers a promising method to automatically extract these interactions from scientific texts. Despite immense progress, there remains room for improvement in these systems. The emergence of Large Language Models (LLMs) provides a promising solution for this challenge. Our study conducts a preliminary investigation into leveraging LLMs for the pathway curation task. This paper first presents a review of the current state-of-the- art algorithms for the pathway curation task. Our objective is to check the feasibility and formulate strategies of using these LLMs to improve the accuracy of pathway curation task. Our experiments demonstrate that our GPT-3.5 based fine-tuned models outperforms existing state-of-the-art methods. Specifically, our model achieved a 10 basis point improvement in over- all recall and F1 score compared to the best existing algorithms. These findings highlight the potential of LLMs in pathway curation tasks, warranting further research and substantial efforts in this direction.

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