Identifying Human Phenotype Terms by Combining Machine Learning and Validation Rules

Lobo, Manuel Stapleton Garcia de Vasconcelos; Lamurias, Andre; Couto, Francisco M.

doi:10.1155/2017/8565739

Cited by 27 publications

(19 citation statements)

References 15 publications

(18 reference statements)

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“…However, the performance of the classifier can be further improved by including more refined semantic similarity methods. Additionally, text mining techniques may also be employed to find the functionality of genes in literature that are lacking semantic scores [32]. Moreover, to attain reliable ASD genes predictions, future studies should be focused on combining protein-protein interactions with semantic similarity scores.…”

Section: Discussionmentioning

confidence: 99%

Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology

et al. 2018

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Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learning methods are widely used to identify these markers, but their performance is highly dependent upon the size and quality of available data. In this study, we demonstrated that machine learning classifiers trained on gene functional similarities, using Gene Ontology (GO), can improve the identification of genes involved in complex diseases. For this purpose, we developed a supervised machine learning methodology to predict complex disease genes. The proposed pipeline was assessed using Autism Spectrum Disorder (ASD) candidate genes. A quantitative measure of gene functional similarities was obtained by employing different semantic similarity measures. To infer the hidden functional similarities between ASD genes, various types of machine learning classifiers were built on quantitative semantic similarity matrices of ASD and non-ASD genes. The classifiers trained and tested on ASD and non-ASD gene functional similarities outperformed previously reported ASD classifiers. For example, a Random Forest (RF) classifier achieved an AUC of 0. 80 for predicting new ASD genes, which was higher than the reported classifier (0.73). Additionally, this classifier was able to predict 73 novel ASD candidate genes that were enriched for core ASD phenotypes, such as autism and obsessive-compulsive behavior. In addition, predicted genes were also enriched for ASD co-occurring conditions, including Attention Deficit Hyperactivity Disorder (ADHD). We also developed a KNIME workflow with the proposed methodology which allows users to configure and execute it without requiring machine learning and programming skills. Machine learning is an effective and reliable technique to decipher ASD mechanism by identifying novel disease genes, but this study further demonstrated that their performance can be improved by incorporating a quantitative measure of gene functional similarities. Source code and the workflow of the proposed methodology are available at https://github.com/Muh-Asif/ASD-genes-prediction.

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Section: Discussionmentioning

confidence: 99%

Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology

et al. 2018

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“…cTAKES is a more general medical knowledge extraction system primarily designed for SNOMED-CT, while BioLarK and the OBO annotator are concept recognizers primarily tailored for the HPO. Another method, called IHP (Identifying Human Phenotypes) [15], was recently introduced for identifying HPO terms in unstructured text using machine learning for named entity recognition and a rule-based approach for further extending them. However, this method is not directly comparable, as it only reports the text spans that are a phenotype and does not classify or rank matching HPO terms.…”

Section: Resultsmentioning

confidence: 99%

“…Examples of popular tools for general purpose are the NCBO (National Center for Biomedical Ontology) annotator [10], OBO (Open Biological and Biomedical Ontologies) annotator [11], MetaMap [12], and Apache cTAKES (Clinical Text Analysis and Knowledge Extraction System) [13]. Other tools focusing on more specific domains have also been developed, such as BioLark [14] for automatic recognition of terms from the HPO and a tool by Lobo et al [15], which combines a machine learning approach with manual validation rules to detect HPO terms. Another example is the phenotype search tool provided by PhenoTips [16], which uses Apache Solr indexed on the HPO and has an extensive set of rule-based techniques to rank matching phenotypes for a query.…”

Section: Introductionmentioning

confidence: 99%

Identifying Clinical Terms in Medical Text Using Ontology-Guided Machine Learning

et al. 2019

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Background Automatic recognition of medical concepts in unstructured text is an important component of many clinical and research applications, and its accuracy has a large impact on electronic health record analysis. The mining of medical concepts is complicated by the broad use of synonyms and nonstandard terms in medical documents. Objective We present a machine learning model for concept recognition in large unstructured text, which optimizes the use of ontological structures and can identify previously unobserved synonyms for concepts in the ontology. Methods We present a neural dictionary model that can be used to predict if a phrase is synonymous to a concept in a reference ontology. Our model, called the Neural Concept Recognizer (NCR), uses a convolutional neural network to encode input phrases and then rank medical concepts based on the similarity in that space. It uses the hierarchical structure provided by the biomedical ontology as an implicit prior embedding to better learn embedding of various terms. We trained our model on two biomedical ontologies—the Human Phenotype Ontology (HPO) and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT). Results We tested our model trained on HPO by using two different data sets: 288 annotated PubMed abstracts and 39 clinical reports. We achieved 1.7%-3% higher F1-scores than those for our strongest manually engineered rule-based baselines ( P =.003). We also tested our model trained on the SNOMED-CT by using 2000 Intensive Care Unit discharge summaries from MIMIC (Multiparameter Intelligent Monitoring in Intensive Care) and achieved 0.9%-1.3% higher F1-scores than those of our baseline. The results of our experiments show high accuracy of our model as well as the value of using the taxonomy structure of the ontology in concept recognition. Conclusion Most popular medical concept recognizers rely on rule-based models, which cannot generalize well to unseen synonyms. In addition, most machine learning methods typically require large corpora of annotated text that cover all classes of concepts, which can be extremely difficult to obtain for biomedical ontologies. Without relying on large-scale labeled training data or requiring any custom training, our model can be efficiently generalized to new synonyms and performs as well or better than state-of-the-art methods custom built for specific ontologies.

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“…Another rule-based synonym expansion approach to extending the Gene Ontology showed improved performance in named entity recognition (NER) tasks [9]. A combined machine-learning and rule-based approach to learning new HP synonyms from manually annotated PubMed abstracts improved performance of an annotation task over a gold standard text corpus [10].…”

Section: Introductionmentioning

confidence: 99%

Improved characterisation of clinical text through ontology-based vocabulary expansion

Slater

Bradlow

Ball

et al. 2020

Preprint

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Background: Biomedical ontologies contain a wealth of metadata that constitutes a fundamental infrastructural resource for text mining. For several reasons, redundancies exist in the ontology ecosystem, which lead to the same concepts being described by several terms in the same or similar contexts across several ontologies. While these terms describe the same concepts, they contain different sets of complementary metadata. Linking these definitions to make use of their combined metadata could lead to improved performance in ontology-based information retrieval, extraction, and analysis tasks. Results: We develop and present an algorithm that expands the set of labels associated with an ontology class using a combination of strict lexical matching and cross-ontology reasoner-enabled equivalency queries. Across all disease terms in the Disease Ontology, the approach found 51,362 additional labels, more than tripling the number defined by the ontology itself. Manual validation by a clinical expert on a random sampling of expanded synonyms over the Human Phenotype Ontology yielded a precision of 0.912. Furthermore, we found that annotating patient visits in MIMIC-III with an extended set of Disease Ontology labels led to semantic similarity score derived from those labels being a significantly better predictor of matching first diagnosis, with a mean average precision of 0.88 for the unexpanded set of annotations, and 0.913 for the expanded set. Conclusions: Inter-ontology synonym expansion can lead to a vast increase in the scale of vocabulary available for text mining applications. While the accuracy of the extended vocabulary is not perfect, it nevertheless led to a significantly improved ontology-based characterisation of patients from text in one setting. Furthermore, where run-on error is not acceptable, the technique can be used to provide candidate synonyms which can be checked by a domain expert.

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Identifying Human Phenotype Terms by Combining Machine Learning and Validation Rules

Cited by 27 publications

References 15 publications

Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology

Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology

Identifying Clinical Terms in Medical Text Using Ontology-Guided Machine Learning

Improved characterisation of clinical text through ontology-based vocabulary expansion

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