PathoPhenoDB, linking human pathogens to their phenotypes in support of infectious disease research

Kafkas, Şenay; Abdelhakim, Marwa; Hashish, Yasmeen; Kulmanov, Maxat; Abdellatif, Marwa; Schofield, Paul N.; Hoehndorf, Robert

doi:10.1038/s41597-019-0090-x

Cited by 14 publications

(11 citation statements)

References 37 publications

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“…As shown in Fig 5 and Table S3, the LSTM-PHV presented higher performances than Zhou’s model (a SVM with commonly-used encoding methods) for all the datasets. The DeepViral used not only amino acid sequence contexts (as words) but also the two biological features, phenotype associations for viruses from PathoPhenoDB [18] and protein functions from the Gene Ontology (GO) database [40, 41]. We trained the LSTM-PHV and DeepViral predictors by the same datasets of TR1 and TR2 that do not include the PPIs between human and H1N1 and Ebola virus , respectively.…”

Section: Resultsmentioning

confidence: 99%

“…The DeepViral (Liu-Wei, et al, 2020) combined doc2vec/word2vec embedding methods with a convolutional neural network (CNN). DeepViral also encoded host phenotype associations from PathoPhenoDB [18] and protein functions from the Gene Ontology (GO) database (The Gene Ontology Consortium, 2017) to predict PPIs. In addition, several constructed ML models were designed for certain individual virus species, limiting their generalizability to other human host-virus systems [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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LSTM-PHV: Prediction of human-virus protein-protein interactions by LSTM with word2vec

Tsukiyama

Hasan

Fujii

et al. 2021

Preprint

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Viral infection involves a large number of protein-protein interactions (PPIs) between human and virus. The PPIs range from the initial binding of viral coat proteins to host membrane receptors to the hijacking of host transcription machinery. However, few interspecies PPIs have been identified, because experimental methods including mass spectrometry are time-consuming and expensive, and molecular dynamic simulation is limited only to the proteins whose 3D structures are solved. Sequence-based machine learning methods are expected to overcome these problems. We have first developed the LSTM model with word2vec to predict PPIs between human and virus, named LSTM-PHV, by using amino acid sequences alone. The LSTM-PHV effectively learnt the training data with a highly imbalanced ratio of positive to negative samples and achieved an AUC of 0.976 with an accuracy of 98.4% using 5-fold cross-validation. By using independent test dataset, we compared the LSTM-PHV with existing state-of-the-art PPI predictors including DeepViral. In predicting PPIs between human and unknown or new virus, the LSTM-PHV presented higher performance than the existing predictors when they were trained by multiple host protein-including datasets. LSTM-PHV learnt multiple host protein sequence contexts more efficiently than the DeepViral. Interestingly, learning of only sequence contexts as words presented remarkably high performances. Use of uniform manifold approximation and projection demonstrated that the LSTM-PHV clearly distinguished the positive PPI samples from the negative ones. We presented the LSTM-PHV online web server that is freely available at http://kurata35.bio.kyutech.ac.jp/.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LSTM-PHV: Prediction of human-virus protein-protein interactions by LSTM with word2vec

Tsukiyama

Hasan

Fujii

et al. 2021

Preprint

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“…Enabling large scale integration of biomedical knowledge with clinical patient data requires robust and accurate mappings between standardized clinical terminology concepts and ontologies, like the HPO. Existing work has demonstrated the power of the HPO to enrich clinical data including craniofacial and oral phenotypes ( 57 ), rare and Mendelian disease ( 58 , 59 ), and infectious disease ( 60 ). There have also been more generalized mapping efforts aimed at aligning different clinical terminologies to the HPO including free-text narratives ( 61 ) and structured data like diagnosis codes ( 62 , 63 ).…”

Section: Ehr Integrationmentioning

confidence: 99%

The Human Phenotype Ontology in 2021

Köhler

Gargano

Matentzoglu

et al. 2020

Nucleic Acids Research

808

688

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The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.

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“…While most of the existing text mining studies related to infectious disease focus on extracting host–pathogen interactions from text [10, 11] and archiving this data [2, 3], to the best of our knowledge, we present the first text mining system which focuses on extracting pathogen–disease associations. Our literature-extracted associations are available for download from https://github.com/bio-ontology-research-group/padimi and are included in PathoPhenoDB [12] and accessible through a public SPARQL endpoint at http://patho.phenomebrowser.net/.…”

Section: Introductionmentioning

confidence: 99%

Ontology based mining of pathogen–disease associations from literature

Kafkas

Hoehndorf

2019

J Biomed Semant

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Background Infectious diseases claim millions of lives especially in the developing countries each year. Identification of causative pathogens accurately and rapidly plays a key role in the success of treatment. To support infectious disease research and mechanisms of infection, there is a need for an open resource on pathogen–disease associations that can be utilized in computational studies. A large number of pathogen–disease associations is available from the literature in unstructured form and we need automated methods to extract the data. Results We developed a text mining system designed for extracting pathogen–disease relations from literature. Our approach utilizes background knowledge from an ontology and statistical methods for extracting associations between pathogens and diseases. In total, we extracted a total of 3420 pathogen–disease associations from literature. We integrated our literature-derived associations into a database which links pathogens to their phenotypes for supporting infectious disease research. Conclusions To the best of our knowledge, we present the first study focusing on extracting pathogen–disease associations from publications. We believe the text mined data can be utilized as a valuable resource for infectious disease research. All the data is publicly available from https://github.com/bio-ontology-research-group/padimi and through a public SPARQL endpoint from http://patho.phenomebrowser.net/.

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PathoPhenoDB, linking human pathogens to their phenotypes in support of infectious disease research

Cited by 14 publications

References 37 publications

LSTM-PHV: Prediction of human-virus protein-protein interactions by LSTM with word2vec

LSTM-PHV: Prediction of human-virus protein-protein interactions by LSTM with word2vec

The Human Phenotype Ontology in 2021

Ontology based mining of pathogen–disease associations from literature

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