Learning to Recognize Phenotype Candidates in the Auto-Immune Literature Using SVM Re-Ranking

Collier, Nigel; Tran, Mai-Vu; Le, Hoang-Quynh; Ha, Quang-Thuy; Oellrich, Anika; Rebholz‐Schuhmann, Dietrich

doi:10.1371/journal.pone.0072965

Cited by 10 publications

(5 citation statements)

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“…The principal modules are now briefly discussed. Data sampling: As described in Data Sampling section; Data cleansing: split and tokenize the sentences using the GENIA tagger ( 15 ) trained on the GENIA Medline abstract corpus; Parsing: phrase structure parsing takes place using the BLLIP/Charniak-Johnson parser (available from https://github.com/BLLIP/bllip-parser ); ( 16 ) trained on the GENIA corpus as labelled data and PubMed; Named entity recognition: biomedical entities were tagged using thePM NER tagger ( 17 ) and the GENIA tagger. This allows us to include semantic labels about anatomical entities, disorders, genes, proteins and other entities that might not be matched in the external vocabularies of the NCBO Annotator.…”

Section: System and Methodsmentioning

confidence: 99%

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PhenoMiner: from text to a database of phenotypes associated with OMIM diseases

et al. 2015

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Analysis of scientific and clinical phenotypes reported in the experimental literature has been curated manually to build high-quality databases such as the Online Mendelian Inheritance in Man (OMIM). However, the identification and harmonization of phenotype descriptions struggles with the diversity of human expressivity. We introduce a novel automated extraction approach called PhenoMiner that exploits full parsing and conceptual analysis. Apriori association mining is then used to identify relationships to human diseases. We applied PhenoMiner to the BMC open access collection and identified 13 636 phenotype candidates. We identified 28 155 phenotype-disorder hypotheses covering 4898 phenotypes and 1659 Mendelian disorders. Analysis showed: (i) the semantic distribution of the extracted terms against linked ontologies; (ii) a comparison of term overlap with the Human Phenotype Ontology (HP); (iii) moderate support for phenotype-disorder pairs in both OMIM and the literature; (iv) strong associations of phenotype-disorder pairs to known disease-genes pairs using PhenoDigm. The full list of PhenoMiner phenotypes (S1), phenotype-disorder associations (S2), association-filtered linked data (S3) and user database documentation (S5) is available as supplementary data and can be downloaded at http://github.com/nhcollier/PhenoMiner under a Creative Commons Attribution 4.0 license.Database URL: phenominer.mml.cam.ac.uk

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Section: System and Methodsmentioning

confidence: 99%

“…Named entity recognition: biomedical entities were tagged using thePM NER tagger ( 17 ) and the GENIA tagger. This allows us to include semantic labels about anatomical entities, disorders, genes, proteins and other entities that might not be matched in the external vocabularies of the NCBO Annotator.…”

Section: System and Methodsmentioning

confidence: 99%

PhenoMiner: from text to a database of phenotypes associated with OMIM diseases

et al. 2015

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“…Using the scientific literature as a source, several groups have been active in developing approaches explicitly for phenotypes. These include the Bio-LarK system, which has been applied to skeletal dysplasia [ 39 ] and the PhenoMiner system, which has been applied to the cardiovascular and autoimmune systems [ 60 ]. Work by Khordad et al.…”

Section: State-of-the-art Phenome Researchmentioning

confidence: 99%

The digital revolution in phenotyping

Oellrich¹,

Collier²,

Groza³

et al. 2015

Brief Bioinform

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Phenotypes have gained increased notoriety in the clinical and biological domain owing to their application in numerous areas such as the discovery of disease genes and drug targets, phylogenetics and pharmacogenomics. Phenotypes, defined as observable characteristics of organisms, can be seen as one of the bridges that lead to a translation of experimental findings into clinical applications and thereby support ‘bench to bedside’ efforts. However, to build this translational bridge, a common and universal understanding of phenotypes is required that goes beyond domain-specific definitions. To achieve this ambitious goal, a digital revolution is ongoing that enables the encoding of data in computer-readable formats and the data storage in specialized repositories, ready for integration, enabling translational research. While phenome research is an ongoing endeavor, the true potential hidden in the currently available data still needs to be unlocked, offering exciting opportunities for the forthcoming years. Here, we provide insights into the state-of-the-art in digital phenotyping, by means of representing, acquiring and analyzing phenotype data. In addition, we provide visions of this field for future research work that could enable better applications of phenotype data.

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“…This method is widely used in life engineering [3][4][5], the Internet [6], civil engineering [7], geo-science [8,9], mechanical engineering [10][11][12] and medical science [13]. SVM is well-suited for addressing pattern recognition problems with small samples, nonlinearity, and high dimension, among others.…”

Section: Introductionmentioning

confidence: 99%

A new multi-class classification method based on minimum enclosing balls

et al. 2015

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With respect to classification problems, the Minimum enclosing ball (MEB) method was recently studied by some scholars as a new support vector machine. As a nascent technology, however, MEB reports poor adaptability for different types of samples, especially multi-class samples. In this paper, we propose a new multi-class classification method based on MEB. This method is derived from each class sample center and radius with the Gaussian kernel width factor parameter σ, which is labelled as σ-MEB. σ is a variable parameter according to the different sample characteristics. When this parameter is considered, the multi-class classifier is easy to adapt and is robust in diverse datasets. The quadratic programming problem was transformed into its dual form with Lagrange multipliers using this method. Finally, we applied sequential minimal optimization method and Karush-Kuhn-Tucker conditions to accelerate the training process. Numerical experiment results indicate that for given different types of samples, the proposed method is more accurate than the methods with which it is compared. Moreover, the proposed method reports values in the upper quantile with respect to adaptation performance.

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Learning to Recognize Phenotype Candidates in the Auto-Immune Literature Using SVM Re-Ranking

Cited by 10 publications

References 46 publications

PhenoMiner: from text to a database of phenotypes associated with OMIM diseases

PhenoMiner: from text to a database of phenotypes associated with OMIM diseases

The digital revolution in phenotyping

A new multi-class classification method based on minimum enclosing balls

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