PPInterFinder—a mining tool for extracting causal relations on human proteins from literature

Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar

doi:10.1093/database/bas052

Cited by 64 publications

(39 citation statements)

References 31 publications

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“…As a result, some of the teams have improved or are improving their systems, and others have engaged new communities. For example, in PPInterFinder, the ‘relation keywords’ list was refined to decrease the false positives and new patterns were added to PPI extraction methodology (22). PubTator (18) is planning to extend the bioconcepts being covered, as well as processing full-length articles, and T-HOD is extending the disease coverage (27).…”

Section: Resultsmentioning

confidence: 99%

An overview of the BioCreative 2012 Workshop Track III: interactive text mining task

et al. 2013

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In many databases, biocuration primarily involves literature curation, which usually involves retrieving relevant articles, extracting information that will translate into annotations and identifying new incoming literature. As the volume of biological literature increases, the use of text mining to assist in biocuration becomes increasingly relevant. A number of groups have developed tools for text mining from a computer science/linguistics perspective, and there are many initiatives to curate some aspect of biology from the literature. Some biocuration efforts already make use of a text mining tool, but there have not been many broad-based systematic efforts to study which aspects of a text mining tool contribute to its usefulness for a curation task. Here, we report on an effort to bring together text mining tool developers and database biocurators to test the utility and usability of tools. Six text mining systems presenting diverse biocuration tasks participated in a formal evaluation, and appropriate biocurators were recruited for testing. The performance results from this evaluation indicate that some of the systems were able to improve efficiency of curation by speeding up the curation task significantly (∼1.7- to 2.5-fold) over manual curation. In addition, some of the systems were able to improve annotation accuracy when compared with the performance on the manually curated set. In terms of inter-annotator agreement, the factors that contributed to significant differences for some of the systems included the expertise of the biocurator on the given curation task, the inherent difficulty of the curation and attention to annotation guidelines. After the task, annotators were asked to complete a survey to help identify strengths and weaknesses of the various systems. The analysis of this survey highlights how important task completion is to the biocurators’ overall experience of a system, regardless of the system’s high score on design, learnability and usability. In addition, strategies to refine the annotation guidelines and systems documentation, to adapt the tools to the needs and query types the end user might have and to evaluate performance in terms of efficiency, user interface, result export and traditional evaluation metrics have been analyzed during this task. This analysis will help to plan for a more intense study in BioCreative IV.

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Section: Resultsmentioning

confidence: 99%

An overview of the BioCreative 2012 Workshop Track III: interactive text mining task

et al. 2013

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“…This phrase is a straightforward indication that two genes have a fundamental role in protein phosphorylation. Other phrase extractors have been used to identify drug-disease treatments [13], pharmcogenomic events [14] and protein-protein interactions [15,16]. These extractors provide a simple and effective way to extract sentences; however, they depend on extensive knowledge about the text to be properly constructed.…”

Section: Rule Based Extractorsmentioning

confidence: 99%

Expanding a Database-derived Biomedical Knowledge Graph via Multi-relation Extraction from Biomedical Abstracts

Nicholson

Himmelstein

Greene

2019

Preprint

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Knowledge graphs support multiple research efforts by providing contextual information for biomedical entities, constructing networks, and supporting the interpretation of high-throughput analyses. These databases are populated via some form of manual curation, which is difficult to scale in the context of an increasing publication rate. Data programming is a paradigm that circumvents this arduous manual process by combining databases with simple rules and heuristics written as label functions, which are programs designed to automatically annotate textual data. Unfortunately, writing a useful label function requires substantial error analysis and is a nontrivial task that takes multiple days per function. This makes populating a knowledge graph with multiple nodes and edge types practically infeasible. We sought to accelerate the label function creation process by evaluating the extent to which label functions could be re-used across multiple edge types. We used a subset of an existing knowledge graph centered on disease, compound, and gene entities to evaluate label function re-use. We determined the best label function combination by comparing a baseline database-only model with the same model but added edge-specific or edge-mismatch label functions. We confirmed that adding additional edge-specific rather than edge-mismatch label functions often improves text annotation and shows that this approach can incorporate novel edges into our source knowledge graph. We expect that continued development of this strategy has the potential to swiftly populate knowledge graphs with new discoveries, ensuring that these resources include cutting-edge results.

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“…Concept extraction and relation/event extraction are the two major components of information extraction [41], [42]. While concept extraction automatically identifies the biomedical concepts present in the articles, relation/event extraction is used to predict the relationship or biological event (e.g., phosphorylation) between the concepts [43], [44].…”

Section: E Biomedical Text Mining Tasksmentioning

confidence: 99%

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

Abed¹,

Yuan²,

Li³

2017

ijacsa

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Abstract-The rapid growth of biomedical informatics has drawn increasing popularity and attention. The reason behind this are the advances in genomic, new molecular, biomedical approaches and various applications like protein identification, patient medical records, genome sequencing, medical imaging and a huge set of biomedical research data are being generated day to day. The increase of biomedical data consists of both structured and unstructured data. Subsequently, in a traditional database system (structured data), managing and extracting useful information from unstructured-biomedical data is a tedious job. Hence, mechanisms, tools, processes, and methods are necessary to apply on unstructured biomedical data (text) to get the useful business data. The fast development of these accumulations makes it progressively troublesome for people to get to the required information in an advantageous and viable way. Text mining can help us mine information and knowledge from a mountain of text, and is now widely applied in biomedical research. Text mining is not a new technology, but it has recently received spotlight attention due to the emergence of Big Data. The applications of text mining are diverse and span to multiple disciplines, ranging from biomedicine to legal, business intelligence and security. In this survey paper, the researcher identifies and discusses biomedical data (text) mining issues, and recommends a possible technique to cope with possible future growth.

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PPInterFinder—a mining tool for extracting causal relations on human proteins from literature

Cited by 64 publications

References 31 publications

An overview of the BioCreative 2012 Workshop Track III: interactive text mining task

An overview of the BioCreative 2012 Workshop Track III: interactive text mining task

Expanding a Database-derived Biomedical Knowledge Graph via Multi-relation Extraction from Biomedical Abstracts

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

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