Protein–protein interaction predictions using text mining methods

Papanikolaou, Nikolas; Pavlopoulos, Georgios A.; Θεοδοσίου, Θεοδόσιος; Iliopoulos, Ioannis

doi:10.1016/j.ymeth.2014.10.026

Cited by 82 publications

(52 citation statements)

References 120 publications

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“…These repositories of protein complexes and interactions are varied in size and species-specificity, and they contain information from experimental and computational sources with or without manual validation (Table 1). For these reasons, it is advised to choose high-quality positive datasets from multiple (times or methods) independent assays (usually high-throughput methods that consider the coverage and biases of different assays) [1] or from text mining of published literature with careful evaluation [2]. The gold standard datasets are always focused on reference datasets that source from model organisms (Figure 2) with advanced accuracy and coverage.…”

Section: Defining Gold Standard Datasetsmentioning

confidence: 99%

“…Sketching a map of protein–protein interactions (PPI) is a significant topic of system biology and an important step towards understanding protein functions and cellular behaviors [1]. Different experimental techniques (in vivo or in vitro) have made significant efforts to study the constant nature of protein interaction sites and screen a large number of protein interaction partners (Figure 1), such as two-hybrid (Y2H) screens, Tandem affinity purification mass spectroscopy (TAP-MS), protein microarrays, mating-based split-ubiquitin system (mbSUS), pulldown assays, dual polarization interferometry (DPI), NMR-based method for mapping the structural interactions (STINT-NMR), bioluminescence resonance energy transfer (BRET), fluorescence resonance energy transfer (FRET), atomic force microscopy (AFM), surface plasmon resonance (SPR), protein complex immune precipitation (Co-IP) [2,3,4,5], and so on. Among these experimental techniques, some high-throughput methods such as Y2H, TAP-MS, protein chips, etc.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Protein–Protein Interactions by Evidence Combining Methods

Chang

Zhou

Qamar

et al. 2016

IJMS

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Most cellular functions involve proteins’ features based on their physical interactions with other partner proteins. Sketching a map of protein–protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

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Section: Defining Gold Standard Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Protein–Protein Interactions by Evidence Combining Methods

Chang

Zhou

Qamar

et al. 2016

IJMS

View full text Add to dashboard Cite

show abstract

“…However, they are completely different in purpose and scope. The purpose of such PPI document classification is two-fold in that it is to extract PPI relationships using an existing relation extraction method [15]- [17] and to construct a PPI network to find candidate proteins that affect the target protein directly or indirectly. However, a protein function is how a protein works in an organism, and requires much broader information including the PPI.…”

Section: Related Workmentioning

confidence: 99%

Classifying Biomedical Literature Providing Protein Function Evidence

Lim¹,

Lee²

2015

ETRI J

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Because protein is a primary element responsible for biological or biochemical roles in living bodies, protein function is the core and basis information for biomedical studies. However, recent advances in bio technologies have created an explosive increase in the amount of published literature; therefore, biomedical researchers have a hard time finding needed protein function information. In this paper, a classification system for biomedical literature providing protein function evidence is proposed. Note that, despite our best efforts, we have been unable to find previous studies on the proposed issue. To classify papers based on protein function evidence, we should consider whether the main claim of a paper is to assert a protein function. We, therefore, propose two novel features — protein and assertion. Our experimental results show a classification performance with 71.89% precision, 90.0% recall, and a 79.94% F‐measure. In addition, to verify the usefulness of the proposed classification system, two case study applications are investigated — information retrieval for protein function and automatic summarization for protein function text. It is shown that the proposed classification system can be successfully applied to these applications.

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“…TEES (Björne and Salakoski, 2013) is a SVM based text mining system for the extraction of events and relations from natural language texts, it obtains good performance on a few tasks in BioNLP-ST 2013 (Nédellec et al, 2013). As a major type of biomedical events, a series of methods concentrate on protein-protein interactions (PPI) Papanikolaou et al, 2015). Kernel-based methods are widely used for relation extraction task and obtain good results by leveraging lexical and syntactic information (Airola et al, 2008;Miwa et al, 2009;Li et al, 2015b).…”

Section: Introductionmentioning

confidence: 99%

LitWay, Discriminative Extraction for Different Bio-Events

Rao²,

Zhang

2016

Proceedings of the 4th BioNLP Shared Task Workshop

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Even a simple biological phenomenon may introduce a complex network of molecular interactions. Scientific literature is one of the trustful resources delivering knowledge of these networks. We propose LitWay, a system for extracting semantic relations from texts. LitWay utilizes a hybrid method that combines both a rule-based method and a machine learning-based method. It is tested on the SeeDev task of BioNLP-ST 2016, achieves the state-of-the-art performance with the F-score of 43.2%, ranking first of all participating teams. To further reveal the linguistic characteristics of each event, we test the system solely with syntactic rules or machine learning, and different combinations of two methods. We find that it is difficult for one method to achieve good performance for all semantic relation types due to the complication of bio-events in the literatures.

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Protein–protein interaction predictions using text mining methods

Cited by 82 publications

References 120 publications

Prediction of Protein–Protein Interactions by Evidence Combining Methods

Prediction of Protein–Protein Interactions by Evidence Combining Methods

Classifying Biomedical Literature Providing Protein Function Evidence

LitWay, Discriminative Extraction for Different Bio-Events

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