1
|
Gill JK, Chetty M, Lim S, Hallinan J. Large language model based framework for automated extraction of genetic interactions from unstructured data. PLoS One 2024; 19:e0303231. [PMID: 38771886 PMCID: PMC11108146 DOI: 10.1371/journal.pone.0303231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 04/23/2024] [Indexed: 05/23/2024] Open
Abstract
Extracting biological interactions from published literature helps us understand complex biological systems, accelerate research, and support decision-making in drug or treatment development. Despite efforts to automate the extraction of biological relations using text mining tools and machine learning pipelines, manual curation continues to serve as the gold standard. However, the rapidly increasing volume of literature pertaining to biological relations poses challenges in its manual curation and refinement. These challenges are further compounded because only a small fraction of the published literature is relevant to biological relation extraction, and the embedded sentences of relevant sections have complex structures, which can lead to incorrect inference of relationships. To overcome these challenges, we propose GIX, an automated and robust Gene Interaction Extraction framework, based on pre-trained Large Language models fine-tuned through extensive evaluations on various gene/protein interaction corpora including LLL and RegulonDB. GIX identifies relevant publications with minimal keywords, optimises sentence selection to reduce computational overhead, simplifies sentence structure while preserving meaning, and provides a confidence factor indicating the reliability of extracted relations. GIX's Stage-2 relation extraction method performed well on benchmark protein/gene interaction datasets, assessed using 10-fold cross-validation, surpassing state-of-the-art approaches. We demonstrated that the proposed method, although fully automated, performs as well as manual relation extraction, with enhanced robustness. We also observed GIX's capability to augment existing datasets with new sentences, incorporating newly discovered biological terms and processes. Further, we demonstrated GIX's real-world applicability in inferring E. coli gene circuits.
Collapse
Affiliation(s)
- Jaskaran Kaur Gill
- Health Innovation and Transformation Centre, Federation University, Ballarat, Victoria, Australia
| | - Madhu Chetty
- Health Innovation and Transformation Centre, Federation University, Ballarat, Victoria, Australia
| | - Suryani Lim
- Health Innovation and Transformation Centre, Federation University, Ballarat, Victoria, Australia
| | - Jennifer Hallinan
- Health Innovation and Transformation Centre, Federation University, Ballarat, Victoria, Australia
- BioThink, Brisbane, Queensland, Australia
| |
Collapse
|
2
|
Lin SJ, Yeh WC, Chiu YW, Chang YC, Hsu MH, Chen YS, Hsu WL. A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles. Database (Oxford) 2022; 2022:6645124. [PMID: 35849027 PMCID: PMC9290865 DOI: 10.1093/database/baac056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 06/20/2022] [Accepted: 07/02/2022] [Indexed: 11/25/2022]
Abstract
In this research, we explored various state-of-the-art biomedical-specific pre-trained Bidirectional Encoder Representations from Transformers (BERT) models for the National Library of Medicine - Chemistry (NLM CHEM) and LitCovid tracks in the BioCreative VII Challenge, and propose a BERT-based ensemble learning approach to integrate the advantages of various models to improve the system’s performance. The experimental results of the NLM-CHEM track demonstrate that our method can achieve remarkable performance, with F1-scores of 85% and 91.8% in strict and approximate evaluations, respectively. Moreover, the proposed Medical Subject Headings identifier (MeSH ID) normalization algorithm is effective in entity normalization, which achieved a F1-score of about 80% in both strict and approximate evaluations. For the LitCovid track, the proposed method is also effective in detecting topics in the Coronavirus disease 2019 (COVID-19) literature, which outperformed the compared methods and achieve state-of-the-art performance in the LitCovid corpus. Database URL: https://www.ncbi.nlm.nih.gov/research/coronavirus/.
Collapse
Affiliation(s)
- Sheng-Jie Lin
- Graduate Institute of Data Science, Taipei Medical University, No. 172-1, Section 2, Keelung Rd, Dáan District , Taipei City 106, Taiwan
| | - Wen-Chao Yeh
- Institute of Information Systems and Applications, National Tsing Hua University, No. 101, Section 2, Guangfu Rd, East District , Hsinchu City 300, Taiwan
| | - Yu-Wen Chiu
- Graduate Institute of Data Science, Taipei Medical University, No. 172-1, Section 2, Keelung Rd, Dáan District , Taipei City 106, Taiwan
| | - Yung-Chun Chang
- Graduate Institute of Data Science, Taipei Medical University, No. 172-1, Section 2, Keelung Rd, Dáan District , Taipei City 106, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, No. 172-1, Section 2, Keelung Rd, Dáan District , Taipei City 106, Taiwan
- Pervasive AI Research Labs, Ministry of Science and Technology, No. 1001, Daxue Rd, East District , Hsinchu City 300, Taiwan
| | - Min-Huei Hsu
- Graduate Institute of Data Science, Taipei Medical University, No. 172-1, Section 2, Keelung Rd, Dáan District , Taipei City 106, Taiwan
| | - Yi-Shin Chen
- Institute of Information Systems and Applications, National Tsing Hua University, No. 101, Section 2, Guangfu Rd, East District , Hsinchu City 300, Taiwan
| | - Wen-Lian Hsu
- Pervasive AI Research Labs, Ministry of Science and Technology, No. 1001, Daxue Rd, East District , Hsinchu City 300, Taiwan
- Department of Computer Science and Information Engineering, Asia University, No. 500, Liufeng Rd, Wufeng District , Taichung City 413, Taiwan
| |
Collapse
|
3
|
Yadav S, Ramesh S, Saha S, Ekbal A. Relation Extraction From Biomedical and Clinical Text: Unified Multitask Learning Framework. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1105-1116. [PMID: 32853152 DOI: 10.1109/tcbb.2020.3020016] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
MOTIVATION To minimize the accelerating amount of time invested on the biomedical literature search, numerous approaches for automated knowledge extraction have been proposed. Relation extraction is one such task where semantic relations between the entities are identified from the free text. In the biomedical domain, extraction of regulatory pathways, metabolic processes, adverse drug reaction or disease models necessitates knowledge from the individual relations, for example, physical or regulatory interactions between genes, proteins, drugs, chemical, disease or phenotype. RESULTS In this paper, we study the relation extraction task from three major biomedical and clinical tasks, namely drug-drug interaction, protein-protein interaction, and medical concept relation extraction. Towards this, we model the relation extraction problem in a multi-task learning (MTL)framework, and introduce for the first time the concept of structured self-attentive network complemented with the adversarial learning approach for the prediction of relationships from the biomedical and clinical text. The fundamental notion of MTL is to simultaneously learn multiple problems together by utilizing the concepts of the shared representation. Additionally, we also generate the highly efficient single task model which exploits the shortest dependency path embedding learned over the attentive gated recurrent unit to compare our proposed MTL models. The framework we propose significantly improves over all the baselines (deep learning techniques)and single-task models for predicting the relationships, without compromising on the performance of all the tasks.
Collapse
|
4
|
Fei H, Zhang Y, Ren Y, Ji D. A span-graph neural model for overlapping entity relation extraction in biomedical texts. Bioinformatics 2021; 37:1581-1589. [PMID: 33245108 DOI: 10.1093/bioinformatics/btaa993] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 10/25/2020] [Accepted: 11/17/2020] [Indexed: 01/14/2023] Open
Abstract
MOTIVATION Entity relation extraction is one of the fundamental tasks in biomedical text mining, which is usually solved by the models from natural language processing. Compared with traditional pipeline methods, joint methods can avoid the error propagation from entity to relation, giving better performances. However, the existing joint models are built upon sequential scheme, and fail to detect overlapping entity and relation, which are ubiquitous in biomedical texts. The main reason is that sequential models have relatively weaker power in capturing long-range dependencies, which results in lower performance in encoding longer sentences. In this article, we propose a novel span-graph neural model for jointly extracting overlapping entity relation in biomedical texts. Our model treats the task as relation triplets prediction, and builds the entity-graph by enumerating possible candidate entity spans. The proposed model captures the relationship between the correlated entities via a span scorer and a relation scorer, respectively, and finally outputs all valid relational triplets. RESULTS Experimental results on two biomedical entity relation extraction tasks, including drug-drug interaction detection and protein-protein interaction detection, show that the proposed method outperforms previous models by a substantial margin, demonstrating the effectiveness of span-graph-based method for overlapping relation extraction in biomedical texts. Further in-depth analysis proves that our model is more effective in capturing the long-range dependencies for relation extraction compared with the sequential models. AVAILABILITY AND IMPLEMENTATION Related codes are made publicly available at http://github.com/Baxelyne/SpanBioER.
Collapse
Affiliation(s)
- Hao Fei
- School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
| | - Yue Zhang
- School of Engineering, Westlake University, Hangzhou 310024, China
| | - Yafeng Ren
- Laboratory of Language and Artificial Intelligence, Guangdong University of Foreign Studies, Guangzhou 510420, China
| | - Donghong Ji
- School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
| |
Collapse
|
5
|
Warikoo N, Chang YC, Hsu WL. LBERT: Lexically aware Transformer-based Bidirectional Encoder Representation model for learning universal bio-entity relations. Bioinformatics 2021; 37:404-412. [PMID: 32810217 DOI: 10.1093/bioinformatics/btaa721] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 06/30/2020] [Accepted: 08/13/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Natural Language Processing techniques are constantly being advanced to accommodate the influx of data as well as to provide exhaustive and structured knowledge dissemination. Within the biomedical domain, relation detection between bio-entities known as the Bio-Entity Relation Extraction (BRE) task has a critical function in knowledge structuring. Although recent advances in deep learning-based biomedical domain embedding have improved BRE predictive analytics, these works are often task selective or use external knowledge-based pre-/post-processing. In addition, deep learning-based models do not account for local syntactic contexts, which have improved data representation in many kernel classifier-based models. In this study, we propose a universal BRE model, i.e. LBERT, which is a Lexically aware Transformer-based Bidirectional Encoder Representation model, and which explores both local and global contexts representations for sentence-level classification tasks. RESULTS This article presents one of the most exhaustive BRE studies ever conducted over five different bio-entity relation types. Our model outperforms state-of-the-art deep learning models in protein-protein interaction (PPI), drug-drug interaction and protein-bio-entity relation classification tasks by 0.02%, 11.2% and 41.4%, respectively. LBERT representations show a statistically significant improvement over BioBERT in detecting true bio-entity relation for large corpora like PPI. Our ablation studies clearly indicate the contribution of the lexical features and distance-adjusted attention in improving prediction performance by learning additional local semantic context along with bi-directionally learned global context. AVAILABILITY AND IMPLEMENTATION Github. https://github.com/warikoone/LBERT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Neha Warikoo
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei 112, Taiwan.,Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.,Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Yung-Chun Chang
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 106, Taiwan.,Clinical Big Data Research Center, Taipei Medical University, Taipei 110, Taiwan.,Pervasive AI Research Labs, Ministry of Science and Technology, Hsinchu City 300, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan.,Pervasive AI Research Labs, Ministry of Science and Technology, Hsinchu City 300, Taiwan
| |
Collapse
|
6
|
Qu J, Steppi A, Zhong D, Hao J, Wang J, Lung PY, Zhao T, He Z, Zhang J. Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach. BMC Genomics 2020; 21:773. [PMID: 33167858 PMCID: PMC7654050 DOI: 10.1186/s12864-020-07185-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 10/26/2020] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Information on protein-protein interactions affected by mutations is very useful for understanding the biological effect of mutations and for developing treatments targeting the interactions. In this study, we developed a natural language processing (NLP) based machine learning approach for extracting such information from literature. Our aim is to identify journal abstracts or paragraphs in full-text articles that contain at least one occurrence of a protein-protein interaction (PPI) affected by a mutation. RESULTS Our system makes use of latest NLP methods with a large number of engineered features including some based on pre-trained word embedding. Our final model achieved satisfactory performance in the Document Triage Task of the BioCreative VI Precision Medicine Track with highest recall and comparable F1-score. CONCLUSIONS The performance of our method indicates that it is ideally suited for being combined with manual annotations. Our machine learning framework and engineered features will also be very helpful for other researchers to further improve this and other related biological text mining tasks using either traditional machine learning or deep learning based methods.
Collapse
Affiliation(s)
- Jinchan Qu
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA
| | - Albert Steppi
- Laboratory of Systems Pharmacology at Harvard Medical School, Boston, MA, 02115, USA
| | - Dongrui Zhong
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA
| | - Jie Hao
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA
| | - Jian Wang
- CloudMedx, Palo Alto, CA, 94301, USA
| | - Pei-Yau Lung
- Verisk - Insurance Solutions, Middletown, CT, 06457, USA
| | - Tingting Zhao
- Department of Geography, Florida State University, Tallahassee, FL, 32306, USA
| | - Zhe He
- College of Communication and Information, Florida State University, Tallahassee, FL, 32306, USA
| | - Jinfeng Zhang
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA.
| |
Collapse
|
7
|
Lou P, Jimeno Yepes A, Zhang Z, Zheng Q, Zhang X, Li C. BioNorm: deep learning-based event normalization for the curation of reaction databases. Bioinformatics 2020; 36:611-620. [PMID: 31350561 DOI: 10.1093/bioinformatics/btz571] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Revised: 06/27/2019] [Accepted: 07/19/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION A biochemical reaction, bio-event, depicts the relationships between participating entities. Current text mining research has been focusing on identifying bio-events from scientific literature. However, rare efforts have been dedicated to normalize bio-events extracted from scientific literature with the entries in the curated reaction databases, which could disambiguate the events and further support interconnecting events into biologically meaningful and complete networks. RESULTS In this paper, we propose BioNorm, a novel method of normalizing bio-events extracted from scientific literature to entries in the bio-molecular reaction database, e.g. IntAct. BioNorm considers event normalization as a paraphrase identification problem. It represents an entry as a natural language statement by combining multiple types of information contained in it. Then, it predicts the semantic similarity between the natural language statement and the statements mentioning events in scientific literature using a long short-term memory recurrent neural network (LSTM). An event will be normalized to the entry if the two statements are paraphrase. To the best of our knowledge, this is the first attempt of event normalization in the biomedical text mining. The experiments have been conducted using the molecular interaction data from IntAct. The results demonstrate that the method could achieve F-score of 0.87 in normalizing event-containing statements. AVAILABILITY AND IMPLEMENTATION The source code is available at the gitlab repository https://gitlab.com/BioAI/leen and BioASQvec Plus is available on figshare https://figshare.com/s/45896c31d10c3f6d857a.
Collapse
Affiliation(s)
- Peiliang Lou
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.,Key Laboratory of Intelligent Networks and Network Security (Xi'an Jiaotong University), Ministry of Education, Xi'an, Shaanxi 710049, China
| | | | - Zai Zhang
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Qinghua Zheng
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.,National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Xiangrong Zhang
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an, 710071, China
| | - Chen Li
- Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.,National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| |
Collapse
|
8
|
Li Q, Yang Z, Zhao Z, Luo L, Li Z, Wang L, Zhang Y, Lin H, Wang J, Zhang Y. HMNPPID-human malignant neoplasm protein-protein interaction database. Hum Genomics 2019; 13:44. [PMID: 31639057 PMCID: PMC6805303 DOI: 10.1186/s40246-019-0223-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Protein-protein interaction (PPI) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Especially, the PPIs associated with human malignant neoplasms can unveil the biology behind these neoplasms. However, such PPI database is not currently available. RESULTS In this work, a database of protein-protein interactions associated with 171 kinds of human malignant neoplasms named HMNPPID is constructed. In addition, a visualization program, named VisualPPI, is provided to facilitate the analysis of the PPI network for a specific neoplasm. CONCLUSIONS HMNPPID can hopefully become an important resource for the research on PPIs of human malignant neoplasms since it provides readily available data for healthcare professionals. Thus, they do not need to dig into a large amount of biomedical literatures any more, which may accelerate the researches on the PPIs of malignant neoplasms.
Collapse
Affiliation(s)
- Qingqing Li
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Zhihao Yang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China.
| | - Zhehuan Zhao
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Ling Luo
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Zhiheng Li
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Lei Wang
- Beijing Institute of Health Administration and Medical Information, Beijing, 100850, China.
| | - Yin Zhang
- Beijing Institute of Health Administration and Medical Information, Beijing, 100850, China
| | - Hongfei Lin
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Jian Wang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Yijia Zhang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| |
Collapse
|
9
|
Caufield JH, Ping P. New advances in extracting and learning from protein-protein interactions within unstructured biomedical text data. Emerg Top Life Sci 2019; 3:357-369. [PMID: 33523203 DOI: 10.1042/etls20190003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 07/11/2019] [Accepted: 07/16/2019] [Indexed: 12/14/2022]
Abstract
Protein-protein interactions, or PPIs, constitute a basic unit of our understanding of protein function. Though substantial effort has been made to organize PPI knowledge into structured databases, maintenance of these resources requires careful manual curation. Even then, many PPIs remain uncurated within unstructured text data. Extracting PPIs from experimental research supports assembly of PPI networks and highlights relationships crucial to elucidating protein functions. Isolating specific protein-protein relationships from numerous documents is technically demanding by both manual and automated means. Recent advances in the design of these methods have leveraged emerging computational developments and have demonstrated impressive results on test datasets. In this review, we discuss recent developments in PPI extraction from unstructured biomedical text. We explore the historical context of these developments, recent strategies for integrating and comparing PPI data, and their application to advancing the understanding of protein function. Finally, we describe the challenges facing the application of PPI mining to the text concerning protein families, using the multifunctional 14-3-3 protein family as an example.
Collapse
Affiliation(s)
- J Harry Caufield
- The NIH BD2K Center of Excellence in Biomedical Computing, University of California at Los Angeles, Los Angeles, CA 90095, U.S.A
- Department of Physiology, University of California at Los Angeles, Los Angeles, CA 90095, U.S.A
| | - Peipei Ping
- The NIH BD2K Center of Excellence in Biomedical Computing, University of California at Los Angeles, Los Angeles, CA 90095, U.S.A
- Department of Physiology, University of California at Los Angeles, Los Angeles, CA 90095, U.S.A
- Department of Medicine/Cardiology, University of California at Los Angeles, Los Angeles, CA 90095, U.S.A
- Department of Bioinformatics, University of California at Los Angeles, Los Angeles, CA 90095, U.S.A
- Scalable Analytics Institute (ScAi), University of California at Los Angeles, Los Angeles, CA 90095, U.S.A
| |
Collapse
|
10
|
Lung PY, He Z, Zhao T, Yu D, Zhang J. Extracting chemical-protein interactions from literature using sentence structure analysis and feature engineering. Database (Oxford) 2019; 2019:5280305. [PMID: 30624652 PMCID: PMC6323317 DOI: 10.1093/database/bay138] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Revised: 12/04/2018] [Accepted: 12/06/2018] [Indexed: 12/14/2022]
Abstract
Information about the interactions between chemical compounds and proteins is indispensable for understanding the regulation of biological processes and the development of therapeutic drugs. Manually extracting such information from biomedical literature is very time and resource consuming. In this study, we propose a computational method to automatically extract chemical-protein interactions (CPIs) from a given text. Our method extracts CPI pairs and CPI triplets from sentences, where a CPI pair consists of a chemical compound and a protein name, and a CPI triplet consists of a CPI pair along with an interaction word describing their relationship. We extracted a diverse set of features from sentences that were used to build multiple machine learning models. Our models contain both simple features, which can be directly computed from sentences, and more sophisticated features derived using sentence structure analysis techniques. For example, one set of features was extracted based on the shortest paths between the CPI pairs or among the CPI triplets in the dependency graphs obtained from sentence parsing. We designed a three-stage approach to predict the multiple categories of CPIs. Our method performed the best among systems that use non-deep learning methods and outperformed several deep-learning-based systems in the track 5 of the BioCreative VI challenge. The features we designed in this study are informative and can be applied to other machine learning methods including deep learning.
Collapse
Affiliation(s)
- Pei-Yau Lung
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Zhe He
- School of Information, Florida State University, Tallahassee, FL, USA
| | - Tingting Zhao
- Department of Geography, Florida State University, Tallahassee, FL, USA
| | - Disa Yu
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Jinfeng Zhang
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| |
Collapse
|
11
|
Yu K, Lung PY, Zhao T, Zhao P, Tseng YY, Zhang J. Automatic extraction of protein-protein interactions using grammatical relationship graph. BMC Med Inform Decis Mak 2018; 18:42. [PMID: 30066644 PMCID: PMC6069288 DOI: 10.1186/s12911-018-0628-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Background Relationships between bio-entities (genes, proteins, diseases, etc.) constitute a significant part of our knowledge. Most of this information is documented as unstructured text in different forms, such as books, articles and on-line pages. Automatic extraction of such information and storing it in structured form could help researchers more easily access such information and also make it possible to incorporate it in advanced integrative analysis. In this study, we developed a novel approach to extract bio-entity relationships information using Nature Language Processing (NLP) and a graph-theoretic algorithm. Methods Our method, called GRGT (Grammatical Relationship Graph for Triplets), not only extracts the pairs of terms that have certain relationships, but also extracts the type of relationship (the word describing the relationships). In addition, the directionality of the relationship can also be extracted. Our method is based on the assumption that a triplet exists for a pair of interactions. A triplet is defined as two terms (entities) and an interaction word describing the relationship of the two terms in a sentence. We first use a sentence parsing tool to obtain the sentence structure represented as a dependency graph where words are nodes and edges are typed dependencies. The shortest paths among the pairs of words in the triplet are then extracted, which form the basis for our information extraction method. Flexible pattern matching scheme was then used to match a triplet graph with unknown relationship to those triplet graphs with labels (True or False) in the database. Results We applied the method on three benchmark datasets to extract the protein-protein-interactions (PPIs), and obtained better precision than the top performing methods in literature. Conclusions We have developed a method to extract the protein-protein interactions from biomedical literature. PPIs extracted by our method have higher precision among other methods, suggesting that our method can be used to effectively extract PPIs and deposit them into databases. Beyond extracting PPIs, our method could be easily extended to extracting relationship information between other bio-entities.
Collapse
Affiliation(s)
- Kaixian Yu
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA. .,Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA.
| | - Pei-Yau Lung
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA
| | - Tingting Zhao
- Department of Geography, Florida State University, Tallahassee, FL, 32306, USA
| | - Peixiang Zhao
- Department of Computer Science, Florida State University, Tallahassee, FL, 32306, USA
| | - Yan-Yuan Tseng
- Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, Detroit, MI, 48201, USA
| | - Jinfeng Zhang
- Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA.
| |
Collapse
|
12
|
Zhang Y, Lin H, Yang Z, Wang J, Zhang S, Sun Y, Yang L. A hybrid model based on neural networks for biomedical relation extraction. J Biomed Inform 2018; 81:83-92. [DOI: 10.1016/j.jbi.2018.03.011] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 03/15/2018] [Accepted: 03/16/2018] [Indexed: 11/27/2022]
|
13
|
Warikoo N, Chang YC, Hsu WL. LPTK: a linguistic pattern-aware dependency tree kernel approach for the BioCreative VI CHEMPROT task. Database (Oxford) 2018; 2018:5139652. [PMID: 30346607 PMCID: PMC6196310 DOI: 10.1093/database/bay108] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 08/30/2018] [Accepted: 09/24/2018] [Indexed: 11/14/2022]
Abstract
Identifying the interactions between chemical compounds and genes from biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this paper, we describe Linguistic Pattern-Aware Dependency Tree Kernel, a linguistic interaction pattern learning method developed for CHEMPROT task-BioCreative VI, to capture chemical-protein interaction (CPI) patterns within biomedical literatures. We also introduce a framework to integrate these linguistic patterns with smooth partial tree kernel to extract the CPIs. This new method of feature representation models aspects of linguistic probability in geometric representation, which not only optimizes the sufficiency of feature dimension for classification, but also defines features as interpretable contexts rather than long vectors of numbers. In order to test the robustness and efficiency of our system in identifying different kinds of biological interactions, we evaluated our framework on three separate data sets, i.e. CHEMPROT corpus, Chemical-Disease Relation corpus and Protein-Protein Interaction corpus. Corresponding experiment results demonstrate that our method is effective and outperforms several compared systems for each data set.
Collapse
Affiliation(s)
- Neha Warikoo
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Yung-Chun Chang
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| |
Collapse
|
14
|
Murugesan G, Abdulkadhar S, Natarajan J. Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature. PLoS One 2017; 12:e0187379. [PMID: 29099838 PMCID: PMC5669485 DOI: 10.1371/journal.pone.0187379] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 10/18/2017] [Indexed: 11/24/2022] Open
Abstract
Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems.
Collapse
Affiliation(s)
- Gurusamy Murugesan
- Data Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, Tamilnadu, India
| | - Sabenabanu Abdulkadhar
- Data Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, Tamilnadu, India
| | - Jeyakumar Natarajan
- Data Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, Tamilnadu, India
- * E-mail:
| |
Collapse
|