1
|
Abdulkadhar S, Bhasuran B, Natarajan J. Multiscale Laplacian graph kernel combined with lexico-syntactic patterns for biomedical event extraction from literature. Knowl Inf Syst 2020. [DOI: 10.1007/s10115-020-01514-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
2
|
Li M, Meng X, Zheng R, Wu FX, Li Y, Pan Y, Wang J. Identification of Protein Complexes by Using a Spatial and Temporal Active Protein Interaction Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:817-827. [PMID: 28885159 DOI: 10.1109/tcbb.2017.2749571] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The rapid development of proteomics and high-throughput technologies has produced a large amount of Protein-Protein Interaction (PPI) data, which makes it possible for considering dynamic properties of protein interaction networks (PINs) instead of static properties. Identification of protein complexes from dynamic PINs becomes a vital scientific problem for understanding cellular life in the post genome era. Up to now, plenty of models or methods have been proposed for the construction of dynamic PINs to identify protein complexes. However, most of the constructed dynamic PINs just focus on the temporal dynamic information and thus overlook the spatial dynamic information of the complex biological systems. To address the limitation of the existing dynamic PIN analysis approaches, in this paper, we propose a new model-based scheme for the construction of the Spatial and Temporal Active Protein Interaction Network (ST-APIN) by integrating time-course gene expression data and subcellular location information. To evaluate the efficiency of ST-APIN, the commonly used classical clustering algorithm MCL is adopted to identify protein complexes from ST-APIN and the other three dynamic PINs, NF-APIN, DPIN, and TC-PIN. The experimental results show that, the performance of MCL on ST-APIN outperforms those on the other three dynamic PINs in terms of matching with known complexes, sensitivity, specificity, and f-measure. Furthermore, we evaluate the identified protein complexes by Gene Ontology (GO) function enrichment analysis. The validation shows that the identified protein complexes from ST-APIN are more biologically significant. This study provides a general paradigm for constructing the ST-APINs, which is essential for further understanding of molecular systems and the biomedical mechanism of complex diseases.
Collapse
|
3
|
Lai PT, Lu WL, Kuo TR, Chung CR, Han JC, Tsai RTH, Horng JT. Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study. JMIR Med Inform 2019; 7:e14502. [PMID: 31769759 PMCID: PMC6913619 DOI: 10.2196/14502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 07/26/2019] [Accepted: 08/11/2019] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Research on disease-disease association (DDA), like comorbidity and complication, provides important insights into disease treatment and drug discovery, and a large body of the literature has been published in the field. However, using current search tools, it is not easy for researchers to retrieve information on the latest DDA findings. First, comorbidity and complication keywords pull up large numbers of PubMed studies. Second, disease is not highlighted in search results. Finally, DDA is not identified, as currently no disease-disease association extraction (DDAE) dataset or tools are available. OBJECTIVE As there are no available DDAE datasets or tools, this study aimed to develop (1) a DDAE dataset and (2) a neural network model for extracting DDA from the literature. METHODS In this study, we formulated DDAE as a supervised machine learning classification problem. To develop the system, we first built a DDAE dataset. We then employed two machine learning models, support vector machine and convolutional neural network, to extract DDA. Furthermore, we evaluated the effect of using the output layer as features of the support vector machine-based model. Finally, we implemented large margin context-aware convolutional neural network architecture to integrate context features and convolutional neural networks through the large margin function. RESULTS Our DDAE dataset consisted of 521 PubMed abstracts. Experiment results showed that the support vector machine-based approach achieved an F1 measure of 80.32%, which is higher than the convolutional neural network-based approach (73.32%). Using the output layer of convolutional neural network as a feature for the support vector machine does not further improve the performance of support vector machine. However, our large margin context-aware-convolutional neural network achieved the highest F1 measure of 84.18% and demonstrated that combining the hinge loss function of support vector machine with a convolutional neural network into a single neural network architecture outperforms other approaches. CONCLUSIONS To facilitate the development of text-mining research for DDAE, we developed the first publicly available DDAE dataset consisting of disease mentions, Medical Subject Heading IDs, and relation annotations. We developed different conventional machine learning models and neural network architectures and evaluated their effects on our DDAE dataset. To further improve DDAE performance, we propose an large margin context-aware-convolutional neural network model for DDAE that outperforms other approaches.
Collapse
Affiliation(s)
- Po-Ting Lai
- Department of Computer Science National Tsing Hua University, Hsinchu, Province of China Taiwan
| | - Wei-Liang Lu
- Department of Computer Science & Information Engineering, National Central University, Taoyuan, Province of China Taiwan
| | - Ting-Rung Kuo
- Department of Computer Science & Information Engineering, National Central University, Taoyuan, Province of China Taiwan
| | - Chia-Ru Chung
- Department of Computer Science & Information Engineering, National Central University, Taoyuan, Province of China Taiwan
| | - Jen-Chieh Han
- Department of Computer Science & Information Engineering, National Central University, Taoyuan, Province of China Taiwan
| | - Richard Tzong-Han Tsai
- Department of Computer Science & Information Engineering, National Central University, Taoyuan, Province of China Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science & Information Engineering, National Central University, Taoyuan, Province of China Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Province of China Taiwan
| |
Collapse
|
4
|
A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text. ALGORITHMS 2019. [DOI: 10.3390/a12090182] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial part of modern semantic webs because they can convert a web of documents into a web of things. Although ontology learning generally occupies a large space in computer science, Arabic ontology learning, in particular, is underdeveloped due to the Arabic language’s nature as well as the profundity required in this domain. The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. The model proposed in this work contributes to Arabic ontology learning in two ways. First, a text mining algorithm is proposed for extracting concepts and their semantic relations from text documents. The algorithm calculates the concept frequency weights using the term frequency weights. Then, it calculates the weights of concept similarity using the information of the ontology structure, involving (1) the concept’s path distance, (2) the concept’s distribution layer, and (3) the mutual parent concept’s distribution layer. Then, feature mapping is performed by assigning the concepts’ similarities to the concept features. Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. The operator of the G-WOA is a hybrid operator integrating GA’s mutation, crossover, and selection processes with the WOA’s processes (encircling prey, attacking of bubble-net, and searching for prey) to fulfill the balance between both exploitation and exploration, and to find the solutions that exhibit the highest fitness. For evaluating the performance of the ontology learning approach, extensive comparisons are conducted using different Arabic corpora and bio-inspired optimization algorithms. Furthermore, two publicly available non-Arabic corpora are used to compare the efficiency of the proposed approach with those of other languages. The results reveal that the proposed genetic-whale optimization algorithm outperforms the other compared algorithms across all the Arabic corpora in terms of precision, recall, and F-score measures. Moreover, the proposed approach outperforms the state-of-the-art methods of ontology learning from Arabic and non-Arabic texts in terms of these three measures.
Collapse
|
5
|
Niu Y, Wu H, Wang Y. Protein-Protein Interaction Identification Using a Similarity-Constrained Graph Model. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:607-616. [PMID: 29989990 DOI: 10.1109/tcbb.2017.2777448] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Protein-protein interaction (PPI) identification is an important task in text mining. Most PPI detection systems make predictions solely based on evidence within a single sentence and often suffer from the heavy burden of manual annotation. This paper approaches PPI detection task from a different paradigm by investigating the context of protein pairs collected from a large corpus and their relations. First, crucial cues in the context are exploited to make initial predictions. Then, relational similarity between protein pairs is calculated. Finally, evidence from the two views is integrated in the framework of minimum cuts algorithm. Experimental results show that the graph model achieves better performance than standard supervised approaches. Using 20 percent data as the training set, our algorithm achieves higher accuracy than support vector machine (SVM) using 80 percent data as training data. Moreover, the semi-supervised settings reveal promising directions for PPI identification exploiting unlabeled data.
Collapse
|
6
|
Yadav S, Ekbal A, Saha S, Kumar A, Bhattacharyya P. Feature assisted stacked attentive shortest dependency path based Bi-LSTM model for protein–protein interaction. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2018.11.020] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
7
|
Arguello Casteleiro M, Demetriou G, Read W, Fernandez Prieto MJ, Maroto N, Maseda Fernandez D, Nenadic G, Klein J, Keane J, Stevens R. Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature. J Biomed Semantics 2018; 9:13. [PMID: 29650041 PMCID: PMC5896136 DOI: 10.1186/s13326-018-0181-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 03/06/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Automatic identification of term variants or acceptable alternative free-text terms for gene and protein names from the millions of biomedical publications is a challenging task. Ontologies, such as the Cardiovascular Disease Ontology (CVDO), capture domain knowledge in a computational form and can provide context for gene/protein names as written in the literature. This study investigates: 1) if word embeddings from Deep Learning algorithms can provide a list of term variants for a given gene/protein of interest; and 2) if biological knowledge from the CVDO can improve such a list without modifying the word embeddings created. METHODS We have manually annotated 105 gene/protein names from 25 PubMed titles/abstracts and mapped them to 79 unique UniProtKB entries corresponding to gene and protein classes from the CVDO. Using more than 14 M PubMed articles (titles and available abstracts), word embeddings were generated with CBOW and Skip-gram. We setup two experiments for a synonym detection task, each with four raters, and 3672 pairs of terms (target term and candidate term) from the word embeddings created. For Experiment I, the target terms for 64 UniProtKB entries were those that appear in the titles/abstracts; Experiment II involves 63 UniProtKB entries and the target terms are a combination of terms from PubMed titles/abstracts with terms (i.e. increased context) from the CVDO protein class expressions and labels. RESULTS In Experiment I, Skip-gram finds term variants (full and/or partial) for 89% of the 64 UniProtKB entries, while CBOW finds term variants for 67%. In Experiment II (with the aid of the CVDO), Skip-gram finds term variants for 95% of the 63 UniProtKB entries, while CBOW finds term variants for 78%. Combining the results of both experiments, Skip-gram finds term variants for 97% of the 79 UniProtKB entries, while CBOW finds term variants for 81%. CONCLUSIONS This study shows performance improvements for both CBOW and Skip-gram on a gene/protein synonym detection task by adding knowledge formalised in the CVDO and without modifying the word embeddings created. Hence, the CVDO supplies context that is effective in inducing term variability for both CBOW and Skip-gram while reducing ambiguity. Skip-gram outperforms CBOW and finds more pertinent term variants for gene/protein names annotated from the scientific literature.
Collapse
Affiliation(s)
| | - George Demetriou
- School of Computer Science, University of Manchester, Manchester, UK
| | - Warren Read
- School of Computer Science, University of Manchester, Manchester, UK
| | | | - Nava Maroto
- Departamento de Lingüística Aplicada a la Ciencia y a la Tecnología, Universidad Politécnica de Madrid, Madrid, Spain
| | | | - Goran Nenadic
- School of Computer Science, University of Manchester, Manchester, UK.,Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Julie Klein
- Institut National de la Santé et de la Recherche Medicale (INSERM) U1048, Toulouse, France.,Universite Toulouse III Paul Sabatier, route de Narbonne, Toulouse, France
| | - John Keane
- School of Computer Science, University of Manchester, Manchester, UK.,Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Robert Stevens
- School of Computer Science, University of Manchester, Manchester, UK.
| |
Collapse
|
8
|
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
|
9
|
Dongliang X, Jingchang P, Bailing W. Multiple kernels learning-based biological entity relationship extraction method. J Biomed Semantics 2017; 8:38. [PMID: 29297359 PMCID: PMC5763518 DOI: 10.1186/s13326-017-0138-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background Automatic extracting protein entity interaction information from biomedical literature can help to build protein relation network and design new drugs. There are more than 20 million literature abstracts included in MEDLINE, which is the most authoritative textual database in the field of biomedicine, and follow an exponential growth over time. This frantic expansion of the biomedical literature can often be difficult to absorb or manually analyze. Thus efficient and automated search engines are necessary to efficiently explore the biomedical literature using text mining techniques. Results The P, R, and F value of tag graph method in Aimed corpus are 50.82, 69.76, and 58.61%, respectively. The P, R, and F value of tag graph kernel method in other four evaluation corpuses are 2–5% higher than that of all-paths graph kernel. And The P, R and F value of feature kernel and tag graph kernel fuse methods is 53.43, 71.62 and 61.30%, respectively. The P, R and F value of feature kernel and tag graph kernel fuse methods is 55.47, 70.29 and 60.37%, respectively. It indicated that the performance of the two kinds of kernel fusion methods is better than that of simple kernel. Conclusion In comparison with the all-paths graph kernel method, the tag graph kernel method is superior in terms of overall performance. Experiments show that the performance of the multi-kernels method is better than that of the three separate single-kernel method and the dual-mutually fused kernel method used hereof in five corpus sets.
Collapse
Affiliation(s)
- Xu Dongliang
- School of Mechanical, Electrical and Information Engineering, ShanDong University, WenHua West Road, WeiHai, 264209, China
| | - Pan Jingchang
- School of Mechanical, Electrical and Information Engineering, ShanDong University, WenHua West Road, WeiHai, 264209, China.
| | - Wang Bailing
- School of Computer Science and Technology, Harbin Institute of Technology, WenHua West Road, WeiHai, 264209, China
| |
Collapse
|
10
|
Multichannel Convolutional Neural Network for Biological Relation Extraction. BIOMED RESEARCH INTERNATIONAL 2016; 2016:1850404. [PMID: 28053977 PMCID: PMC5174749 DOI: 10.1155/2016/1850404] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Accepted: 11/09/2016] [Indexed: 11/18/2022]
Abstract
The plethora of biomedical relations which are embedded in medical logs (records) demands researchers' attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are susceptible to the issues of "vocabulary gap" and data sparseness and the unattainable automation process in feature extraction. To address aforementioned issues, in this work, we propose a multichannel convolutional neural network (MCCNN) for automated biomedical relation extraction. The proposed model has the following two contributions: (1) it enables the fusion of multiple (e.g., five) versions in word embeddings; (2) the need for manual feature engineering can be obviated by automated feature learning with convolutional neural network (CNN). We evaluated our model on two biomedical relation extraction tasks: drug-drug interaction (DDI) extraction and protein-protein interaction (PPI) extraction. For DDI task, our system achieved an overall f-score of 70.2% compared to the standard linear SVM based system (e.g., 67.0%) on DDIExtraction 2013 challenge dataset. And for PPI task, we evaluated our system on Aimed and BioInfer PPI corpus; our system exceeded the state-of-art ensemble SVM system by 2.7% and 5.6% on f-scores.
Collapse
|
11
|
Choi SP. Extraction of protein–protein interactions (PPIs) from the literature by deep convolutional neural networks with various feature embeddings. J Inf Sci 2016. [DOI: 10.1177/0165551516673485] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The automatic extraction of protein–protein interactions (PPIs) reported in scientific publications are of great significance for biomedical researchers in that they could efficiently grasp the recent research results about biochemical events and molecular processes for conducting their original studies. This article introduces a deep convolutional neural network (DCNN) equipped with various feature embeddings to battle the limitations of the existing machine learning-based PPI extraction methods. The proposed model learns and optimises word embeddings based on the publicly available word vectors and also exploits position embeddings to identify the locations of the target protein names in sentences. Furthermore, it can employ various linguistic feature embeddings to improve the PPI extraction. The intensive experiments using AIMed data set known as the most difficult collection not only show the superiority of the suggested model but also indicate important implications in optimising the network parameters and hyperparameters.
Collapse
Affiliation(s)
- Sung-Pil Choi
- Department of Library and Information Science, Kyonggi University, South Korea
| |
Collapse
|
12
|
Chang YC, Chu CH, Su YC, Chen CC, Hsu WL. PIPE: a protein-protein interaction passage extraction module for BioCreative challenge. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw101. [PMID: 27524807 PMCID: PMC4983456 DOI: 10.1093/database/baw101] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 06/02/2016] [Indexed: 11/13/2022]
Abstract
Identifying the interactions between proteins mentioned in biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this article, we propose PIPE, an interaction pattern generation module used in the Collaborative Biocurator Assistant Task at BioCreative V (http://www.biocreative.org/) to capture frequent protein-protein interaction (PPI) patterns within text. We also present an interaction pattern tree (IPT) kernel method that integrates the PPI patterns with convolution tree kernel (CTK) to extract PPIs. Methods were evaluated on LLL, IEPA, HPRD50, AIMed and BioInfer corpora using cross-validation, cross-learning and cross-corpus evaluation. Empirical evaluations demonstrate that our method is effective and outperforms several well-known PPI extraction methods. Database URL:
Collapse
Affiliation(s)
- Yung-Chun Chang
- Institute of Information Science, Academia Sinica, Taipei, Taiwan Department of Information Management, National Taiwan University, Taipei, Taiwan
| | - Chun-Han Chu
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Yu-Chen Su
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Chien Chin Chen
- Department of Information Management, National Taiwan University, Taipei, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| |
Collapse
|
13
|
Affiliation(s)
- Sun Kim
- Seoul National University, Seoul, South Korea
| |
Collapse
|