Literature mining and database annotation of protein phosphorylation using a rule-based system

Hu, Zhang-Zhi; Narayanaswamy, Meenakshi; Ravikumar, K. E.; Vijay‐Shanker, K.; Wu, Cathy H.

doi:10.1093/bioinformatics/bti390

Cited by 104 publications

(73 citation statements)

References 19 publications

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“…In addition, in order to increase the accuracy of identifying protein names, name substitution is used for processing sentences with complex and long protein names by following existing studies. 22 In detail, we employed semantic tokenization to replace multi-word protein names with single token. An abbreviated notation PROi (i is the index of the corresponding protein name in the dictionary) is used to replace protein names in each sentence.…”

Section: Named Entity Recognition (Ner) and Substitutionmentioning

confidence: 99%

“…Other text-mining advances include biological relationship extraction such as gene-disease interactions, 18 named entity recognition (gene/protein name, 19 organisms, 20 and diseases, 21 etc.). Current web-based text mining techniques for PTM information extraction include RLIMS-P, 22 RLIMS-P 2.0, 23 eFIP 24 and MinePhos, 25 RLIMS-P 22 is the¯rst tool for PTM information extraction. It is a rule-based system which utilizes shallow parsing technique and manually developed patterns to extract phosphorylation information (substrates, kinases and sites) from abstracts.…”

Section: Introductionmentioning

confidence: 99%

“…It is a rule-based system which utilizes shallow parsing technique and manually developed patterns to extract phosphorylation information (substrates, kinases and sites) from abstracts. However, RLIMS-P 22 only extracts phosphorylation information, and its complex and restricted patterns limit its overall performance. eFIP 24 integrates various tools to extract protein phosphorylation information from abstracts, and aids scientists to quickly¯nd phosphorylation information from abstracts with a given protein (including site and kinase).…”

Section: Introductionmentioning

confidence: 99%

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MPTM: A tool for mining protein post-translational modifications from literature

Sun

Wang

2017

J. Bioinform. Comput. Biol.

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Due to the importance of post-translational modi¯cations (PTMs) in human health and diseases, PTMs are regularly reported in the biomedical literature. However, the continuing and rapid pace of expansion of this literature brings a huge challenge for researchers and database curators. Therefore, there is a pressing need to aid them in identifying relevant PTM information more e±ciently by using a text mining system. So far, only a few web servers are available for mining information of a very limited number of PTMs, which are based on simple pattern matching or pre-de¯ned rules. In our work, in order to help researchers and database curators easily¯nd and retrieve PTM information from available text, we have developed a text mining tool called MPTM, which extracts and organizes valuable knowledge about 11 common PTMs from abstracts in PubMed by using relations extracted from dependency parse trees and a heuristic algorithm. It is the¯rst web server that provides literature mining service for hydroxylation, myristoylation and GPI-anchor. The tool is also used to¯nd new publications on PTMs from PubMed and uncovers potential PTM information by large-scale text analysis. MPTM analyzes text sentences to identify protein names including substrates and proteininteracting enzymes, and automatically associates them with the UniProtKB protein entry. To facilitate further investigation, it also retrieves PTM-related information, such as human diseases, Gene Ontology terms and organisms from the input text and related databases. In addition, an online database (MPTMDB) with extracted PTM information and a local MPTM Lite package are provided on the MPTM website. MPTM is freely available online at

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Section: Named Entity Recognition (Ner) and Substitutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MPTM: A tool for mining protein post-translational modifications from literature

Sun

Wang

2017

J. Bioinform. Comput. Biol.

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“…In particular, the NLP based approaches have been studied extensively and achieve great success in many information extraction tasks [7], [24], [25], [26], [27]. Some recent works in NLP include RLIMS-P [8], MDL-based method [28], and BioIE [29].…”

Section: Related Workmentioning

confidence: 99%

“…We believe that both these approaches have limitations, which restrict their ability to provide the kind of information biologists desire. The first approach misses a lot of useful information because of the simple representation of a document, whereas the second approach is likely to be only semi-automatic [8].…”

Section: Introductionmentioning

confidence: 99%

Graph and Topological Structure Mining on Scientific Articles

Wang

Jin

Agrawal

et al. 2007

2007 IEEE 7th International Symposium on BioInformatics and BioEngineering

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Abstract-In this paper, we investigate a new approach for literature mining. We use frequent subgraph mining, and its generalization topological structure mining, for finding interesting relationships between gene names and other key biological terms from the text of scientific articles. We show how we can find keywords of interest and represent them as nodes of the graphs. We also propose several methods for inserting edges between these nodes.Our study initially focused on comparing: 1) different methods for constructing edges, and 2) patterns found from sub-graph mining and topological structure mining. Subsequently, we analyzed several frequent topological minors reported by our experiments, and explained their scientific significance.Overall, our study shows the following. First, a simple method of constructing edges, which is based on sliding windows, seems to provide the best results. Second, we are able to find much larger number of well-known and meaningful topological patterns with high support values, as compared to sub-graphs. Overall, the frequent topological minors our algorithm found correspond well to known relationships between genes and biological terms. Thus, we believe that topological structure mining can be a very valuable tool for researchers who are not deeply familiar with the existing literature, and want to obtain a quick summary about known relationships among key scientific names or terms.

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Mining Information on Protein Function from Text

Krallinger¹,

Valencia²

2007

Bioinformatics‐From Genomes to Therapies

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Literature mining and database annotation of protein phosphorylation using a rule-based system

Cited by 104 publications

References 19 publications

MPTM: A tool for mining protein post-translational modifications from literature

MPTM: A tool for mining protein post-translational modifications from literature

Graph and Topological Structure Mining on Scientific Articles

Mining Information on Protein Function from Text

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