FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2012361142> ?p ?o ?g. }

• W2012361142 endingPage "464" @default.
• W2012361142 startingPage "457" @default.
• W2012361142 abstract "The differential diagnostic process attempts to identify candidate diseases that best explain a set of clinical features. This process can be complicated by the fact that the features can have varying degrees of specificity, as well as by the presence of features unrelated to the disease itself. Depending on the experience of the physician and the availability of laboratory tests, clinical abnormalities may be described in greater or lesser detail. We have adapted semantic similarity metrics to measure phenotypic similarity between queries and hereditary diseases annotated with the use of the Human Phenotype Ontology (HPO) and have developed a statistical model to assign p values to the resulting similarity scores, which can be used to rank the candidate diseases. We show that our approach outperforms simpler term-matching approaches that do not take the semantic interrelationships between terms into account. The advantage of our approach was greater for queries containing phenotypic noise or imprecise clinical descriptions. The semantic network defined by the HPO can be used to refine the differential diagnosis by suggesting clinical features that, if present, best differentiate among the candidate diagnoses. Thus, semantic similarity searches in ontologies represent a useful way of harnessing the semantic structure of human phenotypic abnormalities to help with the differential diagnosis. We have implemented our methods in a freely available web application for the field of human Mendelian disorders. The differential diagnostic process attempts to identify candidate diseases that best explain a set of clinical features. This process can be complicated by the fact that the features can have varying degrees of specificity, as well as by the presence of features unrelated to the disease itself. Depending on the experience of the physician and the availability of laboratory tests, clinical abnormalities may be described in greater or lesser detail. We have adapted semantic similarity metrics to measure phenotypic similarity between queries and hereditary diseases annotated with the use of the Human Phenotype Ontology (HPO) and have developed a statistical model to assign p values to the resulting similarity scores, which can be used to rank the candidate diseases. We show that our approach outperforms simpler term-matching approaches that do not take the semantic interrelationships between terms into account. The advantage of our approach was greater for queries containing phenotypic noise or imprecise clinical descriptions. The semantic network defined by the HPO can be used to refine the differential diagnosis by suggesting clinical features that, if present, best differentiate among the candidate diagnoses. Thus, semantic similarity searches in ontologies represent a useful way of harnessing the semantic structure of human phenotypic abnormalities to help with the differential diagnosis. We have implemented our methods in a freely available web application for the field of human Mendelian disorders. Making the correct diagnosis is arguably the most important role of the physician. Clinical diagnostics is often challenging, especially in the field of medical genetics, where the differential diagnosis is complicated by the shear numbers of Mendelian and chromosomal disorders, each of which may be characterized by numerous clinical features that are often shared by many diseases. In addition, pleiotropy and variable expression of individual disorders mean that individual patients with a given disease may have different, partially overlapping combinations of clinical signs and symptoms. A timely and correct genetic diagnosis is important for avoiding unnecessary diagnostic procedures, identifying appropriate therapeutic measures and clinical management strategies, and providing adequate genetic counseling. However, an etiological diagnosis can be made in only about half or fewer of the children presenting with dysmorphic signs with or without mental retardation.1Curry C.J. Stevenson R.E. Aughton D. Byrne J. Carey J.C. Cassidy S. Cunniff C. Graham J.M. Jones M.C. Kaback M.M. et al.Evaluation of mental retardation: recommendations of a consensus conference: American College of Medical Genetics.Am. J. Med. Genet. 1997; 72: 468-477Crossref PubMed Scopus (347) Google Scholar, 2Battaglia A. Bianchini E. Carey J.C. Diagnostic yield of the comprehensive assessment of developmental delay/mental retardation in an institute of child neuropsychiatry.Am. J. Med. Genet. 1999; 82: 60-66Crossref PubMed Scopus (110) Google Scholar, 3van Karnebeek C.D.M. Jansweijer M.C.E. Leenders A.G.E. Offringa M. Hennekam R.C.M. Diagnostic investigations in individuals with mental retardation: a systematic literature review of their usefulness.Eur. J. Hum. Genet. 2005; 13: 6-25Crossref PubMed Scopus (205) Google Scholar, 4Rauch A. Hoyer J. Guth S. Zweier C. Kraus C. Becker C. Zenker M. Hüffmeier U. Thiel C. Rüschendorf F. et al.Diagnostic yield of various genetic approaches in patients with unexplained developmental delay or mental retardation.Am. J. Med. Genet. A. 2006; 140: 2063-2074Crossref PubMed Scopus (290) Google Scholar, 5Srour M. Mazer B. Shevell M.I. Analysis of clinical features predicting etiologic yield in the assessment of global developmental delay.Pediatrics. 2006; 118: 139-145Crossref PubMed Scopus (59) Google Scholar Because of these difficulties, a number of genetic databases have been developed, including POSSUM6Bankier A. Keith C.G. POSSUM: the microcomputer laser-videodisk syndrome information system.Ophthalmic Paediatr. Genet. 1989; 10: 51-52Crossref PubMed Scopus (14) Google Scholar and the London Dysmorphology Database (LDDB),7Fryns J.-P. de Ravel T.J.L. London Dysmorphology Database, London Neurogenetics Database and Dysmorphology Photo Library on CD-ROM [Version 3] 2001 R. M. Winter, M. Baraitser, Oxford University Press.Hum. Genet. 2002; 111: 113Crossref PubMed Scopus (14) Google Scholar as well as the search routines available with the Online Mendelian Inheritance in Man (OMIM) website8Hamosh A. Scott A.F. Amberger J.S. Bocchini C.A. McKusick V.A. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders.Nucleic Acids Res. 2005; 33: D514-D517Crossref PubMed Scopus (1726) Google Scholar and Orphanet.9Aymé S. [Orphanet, an information site on rare diseases].Soins. 2003; 672: 46-47PubMed Google Scholar Users enter one or more features and are presented with a list of candidate diagnoses that are characterized by some or all of the features. However, these systems do not provide explicit rankings or measures of plausibility for the potentially long lists of search results. None of the systems explicitly use semantic relationships between clinical features in order to weight search results. In this paper, we present a method for clinical diagnostics based on a newly developed ontological search routine that uses the semantic structure of the Human Phenotype Ontology (HPO)10Robinson P.N. Köhler S. Bauer S. Seelow D. Horn D. Mundlos S. The Human Phenotype Ontology: A tool for annotating and analyzing human hereditary disease.Am. J. Hum. Genet. 2008; 83: 610-615Abstract Full Text Full Text PDF PubMed Scopus (228) Google Scholar to weight clinical features on the basis of specificity and to identify those clinical features that best distinguish among the top candidate differential diagnoses. We have developed a statistical model to assign a p value to the score obtained by searching on n terms, corresponding to the probability of obtaining a given similarity score or better by choosing the same number of query terms at random. Intuitively, if the highest-scoring candidate diagnosis has a significant p value, this would indicate to the clinician that this syndrome is a likely differential diagnosis and should be considered further. If, on the other hand, the highest-scoring candidate does not have a significant p value, this could indicate that the combination of phenotypic abnormalities entered by the physician is not specific enough to allow a diagnosis, or that the combination of features pertains to a clinical entity that is not represented in the database being queried. An ontology is a computational representation of a domain of knowledge based upon a controlled, standardized vocabulary for describing entities and the semantic relationships between them. Many ontologies are structured as a directed acyclic graph (DAG), whereby the nodes of the DAG, which are also called terms of the ontology, correspond to concepts of the domain. After the success of Gene Ontology,11Ashburner M. Ball C.A. Blake J.A. Botstein D. Butler H. Cherry J.M. Davis A.P. Dolinski K. Dwight S.S. Eppig J.T. et al.Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium.Nat. Genet. 2000; 25: 25-29Crossref PubMed Scopus (23524) Google Scholar ontologies have been developed for many fields in biomedical science.12Smith B. Ashburner M. Rosse C. Bard J. Bug W. Ceusters W. Goldberg L.J. Eilbeck K. Ireland A. Mungall C.J. et al.The OBO foundry: coordinated evolution of ontologies to support biomedical data integration.Nat. Biotechnol. 2007; 25: 1251-1255Crossref PubMed Scopus (1670) Google Scholar We developed the HPO in order to provide a standardized vocabulary of phenotypic abnormalities encountered in human disease.10Robinson P.N. Köhler S. Bauer S. Seelow D. Horn D. Mundlos S. The Human Phenotype Ontology: A tool for annotating and analyzing human hereditary disease.Am. J. Hum. Genet. 2008; 83: 610-615Abstract Full Text Full Text PDF PubMed Scopus (228) Google Scholar For the comparisons described here, the version of the HPO from May 6, 2009 was used. This version is available as version 1.58 from the National Center of Bioontologies (NCBO) Bioportal website, where the HPO can be found via ontology ID 1125. In this version, the HPO contains nearly 9000 terms. Each term in the HPO describes a phenotypic abnormality, such as atrial septal defect. These terms are related to parent terms by “is a” relationships, meaning that they represent a subclass of a more general parent term. In contrast to strict hierarchies, the data structures used to represent ontologies (e.g., DAGs) allow a term to have multiple parent terms. In the HPO, multiple parentage allows the different aspects of phenotypic abnormalities to be represented. The phenotypic feature atrial septal defect, for instance, has the parent terms abnormality of the cardiac septa and abnormality of the cardiac atria, both describing a cardiac abnormality (Figure 1). Annotation is the process of assigning ontology terms (concepts) for the description of objects. In the case of the HPO, ontology terms corresponding to phenotypic abnormalities are used for annotation of diseases. Currently, almost 50,000 annotations to 4813 diseases listed in OMIM are provided. The true path rule applies to all terms in the HPO. That is, if a disease is annotated to the term atrial septal defect, it is implicitly annotated to all ancestors of this term (for instance, Ellis-van Creveld syndrome is annotated to atrial septal defect, and it is therefore implicitly annotated to all the ancestors of that term, such as cardiac malformation) (Figure 1). The importance of a clinical finding for the differential diagnosis depends on its specificity. In ontologies, specificity is reflected by the information content (IC) of a term. The frequency of a term is defined as the proportion of objects that are annotated by the term or any of its descendent terms. The IC is then defined as the negative natural logarithm of the frequency.13Cover T.M. Thomas J.A. Elements of information theory. John Wiley and Sons, Inc., 1991Crossref Google Scholar Thus, the IC of terms tends to grow as we move from the root of an ontology to more specific descendent terms. In our implementation, the IC of a phenotypic feature t is defined on the basis of its frequency within our annotation database. For instance, atrioventricular block is used to annotate three diseases among a total of 4813 diseases, so that its IC is calculated as −log(3/4813) = 7.38. The more general term abnormality of the musculoskeletal system pertains to 2352 diseases, so its IC is −log(2352/4813) = 0.72. The similarity between two terms can be calculated as the IC of their most informative common ancestor (MICA).14Resnik, P. (1995). Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of the 14th International Joint Conference on Artificial Intelligence. 448–453.Google Scholar For instance, in Figure 1, the similarity between the terms abnormality of the cardiac septa and abnormality of the cardiac atria is calculated as the IC of the term cardiac malformation. We can use above-mentioned term-similarity measures to calculate a similarity score on the basis of the query terms entered by the physician and the terms used to annotate the diseases in a database. Several similarity measures have been proposed14Resnik, P. (1995). Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of the 14th International Joint Conference on Artificial Intelligence. 448–453.Google Scholar, 15Jiang, J.J., and Conrath, D.W. (1997). Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of International Conference on Research in Computational Linguistics, pp. 19–33.Google Scholar, 16Lin, D. (1998). An information-theoretic definition of similarity. In ICML ‘98: Proceedings of the Fifteenth International Conference on Machine Learning. (San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.), pp. 296–304.Google Scholar and have been applied to the biomedical domain.17Lord P. Stevens R.D. Brass A. Goble C.A. Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation.Bioinformatics. 2003; 19: 1275-1283Crossref PubMed Scopus (614) Google Scholar, 18Névéol A. Zeng K. Bodenreider O. Besides precision & recall: exploring alternative approaches to evaluating an automatic indexing tool for medline.AMIA Annual Symposium Proceedings. 2006; : 589-593PubMed Google Scholar, 19Couto F.M. Silva M.J. Coutinho P.M. Measuring semantic similarity between Gene Ontology terms.Data Knowl. Eng. 2007; 61: 137-152Crossref Scopus (160) Google Scholar, 20Yu H. Jansen R. Stolovitzky G. Gerstein M. Total ancestry measure: quantifying the similarity in tree-like classification, with genomic applications.Bioinformatics. 2007; 23: 2163-2173Crossref PubMed Scopus (40) Google Scholar In our case, for each of the query terms, the “best match” among the terms annotated to the disease is found and the average over all query terms is calculated. This is defined as the similarity:sim(Q→D)=avg[∑t1∈Qmaxt2∈DIC(MICA(t1,t2))].(Equation 1) Figure 2 provides an overview of the approach. This measure will return a high score if a good match is found for each term in the query. In the following text, we will refer to this method as the Ontological Similarity Search (OSS). Note that Equation 1 does not take into account the fact that there could be a number of terms annotated to the syndrome in addition to those used for the maximum match. For instance, this would be the case if a specific query is compared to two syndromes, both of which are annotated by terms that exactly match the query but one of which is annotated by a number of additional terms. With the one-sided formula (Equation 1) used, both syndromes would receive the same score. It is also possible to define a symmetric version of Equation 1 in which the similarity of the query to the disease is averaged with the similarity of the disease to the query:simsymmetric(D,Q)=12sim(Q→D)+12sim(D→Q).(Equation 2) We also implemented a simple feature vector (FV) method, in which the exact overlap between Q and D is calculated. This method is meant to be similar to text-matching methods used by POSSUM6Bankier A. Keith C.G. POSSUM: the microcomputer laser-videodisk syndrome information system.Ophthalmic Paediatr. Genet. 1989; 10: 51-52Crossref PubMed Scopus (14) Google Scholar and the London Dysmorphology Database,7Fryns J.-P. de Ravel T.J.L. London Dysmorphology Database, London Neurogenetics Database and Dysmorphology Photo Library on CD-ROM [Version 3] 2001 R. M. Winter, M. Baraitser, Oxford University Press.Hum. Genet. 2002; 111: 113Crossref PubMed Scopus (14) Google Scholar as well as the search routines available with the OMIM website8Hamosh A. Scott A.F. Amberger J.S. Bocchini C.A. McKusick V.A. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders.Nucleic Acids Res. 2005; 33: D514-D517Crossref PubMed Scopus (1726) Google Scholar and Orphanet.9Aymé S. [Orphanet, an information site on rare diseases].Soins. 2003; 672: 46-47PubMed Google Scholar However, we note that we did not attempt to perform an explicit comparison with these databases because of the different clinical vocabularies used by each of these databases and the fact that they do not provide a ranking for the results of searches. The raw similarity score depends on a number of factors, including the number and specificity of the terms both of the query and of the diseases represented in the database. It is thus not possible to say what score constitutes a “good match” for a general query. We have therefore developed a statistical model based on the distribution of similarity scores that is obtained by randomly choosing combinations of HPO terms. Intuitively, random combinations of clinical features are unlikely to be observed in real diseases, so that the scores obtained by entering a combination of terms that characterize a given disease are higher. If a given score is only rarely obtained by chance, then we consider it to be statistically significant. We estimated a p value for each search result that indicates the probability of obtaining the same or higher similarity scores by a randomly generated query set of the same size. The p values are estimated by Monte Carlo random sampling and corrected for multiple testing by the method of Benjamini and Hochberg.21Benjamini Y. Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing.J. Roy. Statist. Soc. Ser. B. Methodological. 1995; 57: 289-300Google Scholar For each query, similarity scores are calculated for each disease in the database, and the best differential diagnoses are returned to the user, ranked by p value. We will refer to this method as Ontological Similarity Search with p values (OSS-PV). Our similarity score is based on an average over all of the scores for the individual query terms (see Equations (Equation 1), (Equation 2)). Thus, the probability of observing a certain (or higher) score S in a similarity search with two query terms is different than that in a similarity search with six query terms. That means we need to compute the p value for every number of query terms q that we allow for the search. Unfortunately, the exhaustive computation of all possible choices is infeasible, because the number of combinations grows exponentially with q. Instead, we take a Monte Carlo approach and approximate the complete probability distribution with 100,000 random searches on the HPO for every OMIM entry. The simulation is repeated for searches with q = 1…10 query terms, for each of the similarity measures to be tested. We stored on disk all possible scores for every OMIM entry (rounded to four decimal places) and the associated p value. For 11 or more terms, we used the precalculated distribution for ten terms. It is difficult to validate a diagnostic algorithm by using real patients for a number of reasons, mainly because it is difficult to get phenotypic information about hundreds or thousands of patients (which would be needed for statistical validation) that has been collected with the use of a standardized procedure and a standardized vocabulary. We therefore took an informatic approach, in which we generated clinical data for “simulated patients” on the basis of the frequency of clinical features among persons diagnosed with a certain disease. We identified 44 complex dysmorphology syndromes for which adequate frequency data were available (see Tables S1–S45, available online), and we used this information to generate the simulated patients. We assumed that the occurrence of individual clinical features is independent. Although this assumption is not correct, sufficient data are currently not available for modeling of the interdependencies of clinical features. For instance, in order to generate patients for a disease with the features A, B, and C, in which A occurs in 50%, B occurs in 70%, and C occurs in 10% of patients, we use a random number generator to generate three random numbers uniformly distributed between 0 and 100. If the first number is less than 50, we assign the feature A to the simulated patient, and otherwise we do not. If the second number is less than 70, we assign B to the patient, and if the third number is less than 10, we assign C to the patient. We then repeat this procedure 100 times in order to generate 100 patients with different combinations of clinical features. Because some of the diseases have gender-specific features, we first decided whether the patient was male or female and adjusted the simulation accordingly. In clinical practice, patients can not only have signs and symptoms that are related to some underlying disorder but may also have unrelated clinical problems. We refer to this as “noise.” In oder to simulate noise, we added again half as many noise terms to the terms selected from the underlying disease. That means that if the patient had nine features, we added four randomly selected terms. We ensured that the noise terms were not ancestors or descendents of the terms annotated to the disease or of each other. Another difficultly with clinical databases is that physicians may not choose the same phrase to describe some clinical anomaly as that which is used in the database. This may be because the physician is unaware of the correct terminology or because detailed laboratory or clinical investigations have yet to be performed and a clinical anomaly can only be described on a general level. We refer to this as “imprecision.” When the imprecision mode was turned on, every feature of the patient was randomly replaced by one of its ancestors, except the root of the ontology (organ abnormality). When both “noise” and “imprecision” were applied, we first performed the imprecision step (which may lead to a reduced number of features of the patient, for instance, if two query terms are mapped to the same ancestor term) and afterwards applied the noise-step. We have implemented the algorithms described above in a web application called the Phenomizer (Figure S1), and we will now demonstrate how ontological search algorithms can be used to assist the diagnostic workflow. Imagine that a nine-year-old boy is presented for workup of developmental retardation and is additionally found to have arachnodactyly, pectus excavatum, and scoliosis. Initial analysis with the Phenomizer with the use of the corresponding terms yields a list of differential diagnoses with p values starting at 0.1. This lack of significance reflects the fact that the clinical findings are not specific enough, per se, to allow a diagnosis. The physician can now use the Phenomizer to generate a list of clinical features that are most specific for individual diagnoses in a set of selected syndromes and can use this list to guide the further workup. For instance, one of the features returned, when all syndromes with p values less than 0.5 are selected, is arterial tortuosity, generalized, which could prompt further investigations such as magnetic resonance imaging of the vasculature. In this case, adding this feature to the list of features leads to a significant p value for Loeys-Dietz syndrome 1A.22Loeys B.L. Schwarze U. Holm T. Callewaert B.L. Thomas G.H. Pannu H. Backer J.F.D. Oswald G.L. Symoens S. Manouvrier S. et al.Aneurysm syndromes caused by mutations in the TGF-beta receptor.N. Engl. J. Med. 2006; 355: 788-798Crossref PubMed Scopus (1135) Google Scholar The clinical features returned by the Phenomizer can prompt more exact clinical examination (e.g., fine, brittle hair) or technical examinations (e.g., radiography to search for codfish vertebrae). In many cases, adding one of these terms to the patient features has the effect of making one or a few of the diagnoses significant (Table 1), which may help physicians plan the further workup by referring to an appropriate specialist or performing genetic mutation analysis.Table 1The Semantic Structure of the HPO Can be Used for Identifying Features that Best Discriminate among Differential DiagnosesAdditional FeatureBest Differential DiagnosisNumber of Differential Diagnoses with p < 0.05Arterial tortuosity, generalizedLoeys-Dietz syndrome 1A1Codfish vertebraeMRXS142Broad femoral metaphysesCATSHL syndrome1Arnold-Chiari type I malformationShprintzen-Goldberg syndrome1Fine, brittle hairHomocystinuria1The semantic structure of the HPO can be used for identifying features that best discriminate among differential diagnoses. For instance, searching on the terms developmental retardation, arachnodactyly, pectus excavatum, and scoliosis initially returns a list of differential diagnoses starting with p values at 0.1. The Phenomizer provides a list of HPO terms that would best distinguish between selected differential diagnoses. This can suggest possibilities for further examinations that would help to narrow down the differential diagnosis. If such a feature is found, users can add the corresponding term to the list of patient features and recalculate the statistical significance of the resulting similarity scores. This table shows exemplary results of adding individual terms to the search. For each term, the best diagnosis is shown together with the total number of differential diagnoses with a p value of 0.05 or less. In the case of ties, only one, arbitrarily chosen diagnosis is shown. Abbreviations are as follows: MRXS14, mental retardation, X-linked, syndromic 14 (MIM 300676); CATSHL, camptodactyly, tall stature, scoliosis, and hearing loss (MIM 610474). Open table in a new tab The semantic structure of the HPO can be used for identifying features that best discriminate among differential diagnoses. For instance, searching on the terms developmental retardation, arachnodactyly, pectus excavatum, and scoliosis initially returns a list of differential diagnoses starting with p values at 0.1. The Phenomizer provides a list of HPO terms that would best distinguish between selected differential diagnoses. This can suggest possibilities for further examinations that would help to narrow down the differential diagnosis. If such a feature is found, users can add the corresponding term to the list of patient features and recalculate the statistical significance of the resulting similarity scores. This table shows exemplary results of adding individual terms to the search. For each term, the best diagnosis is shown together with the total number of differential diagnoses with a p value of 0.05 or less. In the case of ties, only one, arbitrarily chosen diagnosis is shown. Abbreviations are as follows: MRXS14, mental retardation, X-linked, syndromic 14 (MIM 300676); CATSHL, camptodactyly, tall stature, scoliosis, and hearing loss (MIM 610474). It is difficult to compare the performance of our method to that of other systems such as POSSUM or LDDB because these systems use different vocabularies to describe clinical features and do not provide p values or rankings for candidate diagnoses. Nonetheless, we developed a testing scheme to compare the Phenomizer to simpler matching schemes, which simply count the number of clinical features from a query set that are present in a disease (FV). We note that the FV method does not take the semantic inheritance structure of the ontology into account. It essentially compares two vectors of zeros and ones with one field for each of the clinical features being compared, whereby the vector has a “1” if a feature is present and a “0” if it is not present. The dot product of a query vector (with the features observed by the physician) and the disease vector (with all of the features characterizing a disease) then yields a count of common features. The disease with the highest count is taken to be the best differential diagnosis. We also simulated the effects of adding “noise” (i.e., additional clinical features not related to the underlying diagnosis) to the query and of using “imprecise” terms (i.e., replacing query terms with randomly chosen ancestors of the terms; for instance, replacing the term atrial septal defect with abnormality of the cardiac septa) (Figure 1). Additional information about the procedures can be found in the Material and Methods section. We then collected comprehensive clinical information on 44 complex dysmorphology syndromes from the literature, including information on what proportion of patients have any given clinical feature, and used this information to generate 100 virtual patients with each syndrome, whereby the probability of any virtual patient having a given clinical feature is taken to be the proportion of patients from the literature with the feature (see Tables S1–S45). We ranked the complete database of 4813 OMIM diseases by calculating the similarity of the simulated patient to every OMIM disease and recorded the rank of the correct diagnosis returned by the Phenomizer. In the case of ties, the average rank was returned (e.g., if three syndromes each received the best score, all three were assigned rank 2). When the ranking was done by p value and two or more diseases had the same p value, the score is used for ranking, such that ties are only possible if the p value and the score are identical. The results of this simulation procedure are shown in Figure 3. It can be seen that both ontological methods (OSS and OSS-PV) have a modest advantage over the feature-vector (FV) method in an ideal situation with no noise or imprecision. The performance of the FV method deteriorates somewhat when phenotypic noise is added. The effect of imprecision simulates the situation when the physician enters a term to describe a clinical feature that is more general than the term used in the database. It can be seen that the performance of the FV method greatly suffers in this situation, whereas that of the ontological methods, which intuitively use the semantic network encoded in the ontology to recognize that the imprecise term has a meaning similar to that of the term used in the database, shows only a minimal decrease in performance. The OSS-PV, which bases the ranking on the p value of attaining a given score for each disease in the database (OSS-PV), was superior to the results of ranking on the basis of the raw similarity scores (OSS). This reflects the fact that the distribution of similarity scores is not the same for all diseases in the database (results not shown) and suggests that search methods that take the local score distributions into account are superior. In sum, we have shown that ontological approaches (OSS, OSS-PV) are especially robust in the presence of noise and are not overly dependent on the exact search terms being used. Clearly, OSS-PV significantly outperforms all other methods (p < 2.2 × 10−16; Mann-Whitney test). There are a number of different ways of performing an ontological similarity search. The results presented above are based on a one-sided search using a similarity measure based on the information content of the most informative common ancestor (Equation 1), whereby the “best match” is sought for each query term among all terms used to annotate a disease. We also performed the analysis by using the symmetric version of the similarity score (Equation 2). The corresponding OSS-PV also significantly outperformed the feature-vector method in this setting (p < 1.3 × 10−3; Mann-Whitney test). We have also tested a number of different similarity measures that use different algorithms for calculating the similarity between terms in an ontology.15Jiang, J.J., and Conrath, D.W. (1997). Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of International Conference on Research in Computational Linguistics, pp. 19–33.Google Scholar, 16Lin, D. (1998). An information-theoretic definition of similarity. In ICML ‘98: Proceedings of the Fifteenth International Conference on Machine Learning. (San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.), pp. 296–304.Google Scholar, 17Lord P. Stevens R.D. Brass A. Goble C.A. Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation.Bioinformatics. 2003; 19: 1275-1283Crossref PubMed Scopus (614) Google Scholar, 19Couto F.M. Silva M.J. Coutinho P.M. Measuring semantic similarity between Gene Ontology terms.Data Knowl. Eng. 2007; 61: 137-152Crossref Scopus (160) Google Scholar, 23Mistry M. Pavlidis P. Gene Ontology term overlap as a measure of gene functional similarity.BMC Bioinformatics. 2008; 9: 327Crossref PubMed Scopus (141) Google Scholar The results of simulations using these algorithms were inferior to those using the information content of the most informative common ancestor as defined with the use of Equations (Equation 1), (Equation 2) (data not shown). Computer-based decision support programs for physicians have been in use since the 1960s, and numerous algorithms have been evaluated, including mainly naive Bayes classifiers, rule-based systems, artificial neural networks, and expert Bayesian networks.24Warner H. Iliad: moving medical decision-making into new frontiers.Methods Inf. Med. 1989; 28: 370-372PubMed Google Scholar, 25Trace D. Evens M. Naeymi-Rad F. Carmony L. Medical information management: the MEDAS approach.Symposium on Computer Applications in Medical Care. 1990; : 635-639Google Scholar, 26Miller R. Masarie F.E. Myers J.D. Quick medical reference (QMR) for diagnostic assistance.MD comput. 1986; 3: 34-48PubMed Google Scholar, 27Barnett G. Cimino J. Hupp J. Hoffer E. DXplain. an evolving diagnostic decision-support system.JAMA. 1987; 258: 67-74Crossref PubMed Scopus (224) Google Scholar, 28Schurink C.A.M. Lucas P.J.F. Hoepelman I.M. Bonten M.J.M. Computer-assisted decision support for the diagnosis and treatment of infectious diseases in intensive care units.Lancet Infect. Dis. 2005; 5: 305-312Abstract Full Text Full Text PDF PubMed Scopus (70) Google Scholar The field of medical genetics poses special challenges because of the large number of distinct syndromes and phenotypic features that need to be considered and the fact that pathognomonic signs are rare and in many cases combinations of more- or less-specific clinical features are needed for a diagnosis.29Jones K.L. Smith D.W. Smith's Recognizable Patterns of Human Malformation. Saunders WB, 2005Google Scholar Previous computer-based systems for medical genetics diagnostics have relied mainly on identifying lists of syndromes characterized by at least a certain number of query features, and have not provided a means of determining whether any given match is significant in a statistical sense. The procedure that we have described in this paper takes advantage of semantic similarity in an ontology to rank candidate diseases (the differential diagnosis) according to their semantic similarity with the query terms and to provide a p value that indicates whether the similarity scores of best-matching candidate diseases are significantly better than would be expected by chance. In addition, the semantic network induced by the list of differential diagnoses is exploited to indicate to the user those clinical features that if present best distinguish among the top differential diagnoses, which may either suggest to the physician sensible follow-up examinations or induce him or her to reexamine the patient for subtle phenotypic features not sought after during the initial examination. To evaluate our diagnostic algorithm, we developed a testing scenario based on “simulated patients” presenting with clinical features of one of 44 complex dysmorphology syndromes. The features were chosen to be present or not according to the frequencies of their occurrence as reported in the genetics literature. The results of the simulation demonstrated that the ontological approaches, especially OSS-PV, performed better than diagnostic algorithms on the basis of exact matching of items in a phenotypic feature vector. The advantage was the greatest in the presence of phenotypic “noise” and “imprecision” in the description of clinical abnormalities, which we contend is typical in the clinical setting. Presumably, the superior performance of ontological algorithms reflects the advantage of exploiting the semantic structure of the HPO. There are limitations to the simulation strategy that we used for the analysis, including the fact that the occurrence of the various phenotypic abnormalities that characterize a disease is not independent. However, not enough data are available for the inclusion of correlations between phenotypic features in the simulation. We have implemented our method as a freely available web application called the Phenomizer. The Phenomizer is not intended to be an expert system (software that attempts to reproduce the performance of a human expert) but rather a system for experts, who can use the Phenomizer to help guide the differential diagnostic process in human genetics. By providing a statistical measure of the significance of the proposed candidate diagnoses, the Phenomizer can provide some indication of whether the clinical features entered by the physician are in themselves highly suggestive of a given diagnosis or, on the other hand, whether no diagnosis in the database significantly matches the query terms. Finally, although we have implemented our methods for the domain of medical genetics, similar approaches could be used for any field of medicine for which an ontology and annotations have been developed. The authors would like to acknowledge the monumental work of the late Professor Victor McKusick and colleagues at OMIM, without which our own work on the HPO would have been impossible. This work was supported by the Deutsche Forschungsgemeinschaft (DFG RO 2005/4-1, SFB 760) and the Berlin-Brandenburg Center for Regenerative Therapies (BCRT) (Bundesministerium für Bildung und Forschung, project no. 0313911). Download .pdf (.37 MB) Help with pdf files Document S1. One Figure and 45 Tables The URLs for data presented herein are as follows:The Human Phenotype Ontology (HPO), http://www.human-phenotype-ontology.orgNational Center of Bioontologies (NCBO) Bioportal website, http://bioportal.bioontology.org/Online Mendelian Inheritance in Man (OMIM), http://www.ncbi.nlm.nih.gov/omim/The Phenomizer, http://compbio.charite.de/phenomizer" @default.
• W2012361142 created "2016-06-24" @default.
• W2012361142 creator A5007219500 @default.
• W2012361142 creator A5013713722 @default.
• W2012361142 creator A5021272146 @default.
• W2012361142 creator A5023743496 @default.
• W2012361142 creator A5033225381 @default.
• W2012361142 creator A5047602412 @default.
• W2012361142 creator A5049735533 @default.
• W2012361142 creator A5080725960 @default.
• W2012361142 creator A5081693588 @default.
• W2012361142 creator A5087919339 @default.
• W2012361142 date "2009-10-01" @default.
• W2012361142 modified "2023-10-14" @default.
• W2012361142 title "Clinical Diagnostics in Human Genetics with Semantic Similarity Searches in Ontologies" @default.
• W2012361142 cites W127096774 @default.
• W2012361142 cites W2054351373 @default.
• W2012361142 cites W2054472010 @default.
• W2012361142 cites W2060948468 @default.
• W2012361142 cites W2078003073 @default.
• W2012361142 cites W2103017472 @default.
• W2012361142 cites W2104532643 @default.
• W2012361142 cites W2107407649 @default.
• W2012361142 cites W2110003745 @default.
• W2012361142 cites W2113142309 @default.
• W2012361142 cites W2119412782 @default.
• W2012361142 cites W2127563440 @default.
• W2012361142 cites W2132642742 @default.
• W2012361142 cites W2133014144 @default.
• W2012361142 cites W2136410628 @default.
• W2012361142 cites W2147438501 @default.
• W2012361142 cites W2159348493 @default.
• W2012361142 cites W2160568168 @default.
• W2012361142 cites W4230779446 @default.
• W2012361142 cites W4299392555 @default.
• W2012361142 doi "https://doi.org/10.1016/j.ajhg.2009.09.003" @default.
• W2012361142 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2756558" @default.
• W2012361142 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/19800049" @default.
• W2012361142 hasPublicationYear "2009" @default.
• W2012361142 type Work @default.
• W2012361142 sameAs 2012361142 @default.
• W2012361142 citedByCount "432" @default.
• W2012361142 countsByYear W20123611422012 @default.
• W2012361142 countsByYear W20123611422013 @default.
• W2012361142 countsByYear W20123611422014 @default.
• W2012361142 countsByYear W20123611422015 @default.
• W2012361142 countsByYear W20123611422016 @default.
• W2012361142 countsByYear W20123611422017 @default.
• W2012361142 countsByYear W20123611422018 @default.
• W2012361142 countsByYear W20123611422019 @default.
• W2012361142 countsByYear W20123611422020 @default.
• W2012361142 countsByYear W20123611422021 @default.
• W2012361142 countsByYear W20123611422022 @default.
• W2012361142 countsByYear W20123611422023 @default.
• W2012361142 crossrefType "journal-article" @default.
• W2012361142 hasAuthorship W2012361142A5007219500 @default.
• W2012361142 hasAuthorship W2012361142A5013713722 @default.
• W2012361142 hasAuthorship W2012361142A5021272146 @default.
• W2012361142 hasAuthorship W2012361142A5023743496 @default.
• W2012361142 hasAuthorship W2012361142A5033225381 @default.
• W2012361142 hasAuthorship W2012361142A5047602412 @default.
• W2012361142 hasAuthorship W2012361142A5049735533 @default.
• W2012361142 hasAuthorship W2012361142A5080725960 @default.
• W2012361142 hasAuthorship W2012361142A5081693588 @default.
• W2012361142 hasAuthorship W2012361142A5087919339 @default.
• W2012361142 hasBestOaLocation W20123611421 @default.
• W2012361142 hasConcept C103278499 @default.
• W2012361142 hasConcept C115961682 @default.
• W2012361142 hasConcept C130318100 @default.
• W2012361142 hasConcept C154945302 @default.
• W2012361142 hasConcept C204321447 @default.
• W2012361142 hasConcept C23123220 @default.
• W2012361142 hasConcept C41008148 @default.
• W2012361142 hasConcept C54355233 @default.
• W2012361142 hasConcept C70721500 @default.
• W2012361142 hasConcept C86803240 @default.
• W2012361142 hasConceptScore W2012361142C103278499 @default.
• W2012361142 hasConceptScore W2012361142C115961682 @default.
• W2012361142 hasConceptScore W2012361142C130318100 @default.
• W2012361142 hasConceptScore W2012361142C154945302 @default.
• W2012361142 hasConceptScore W2012361142C204321447 @default.
• W2012361142 hasConceptScore W2012361142C23123220 @default.
• W2012361142 hasConceptScore W2012361142C41008148 @default.
• W2012361142 hasConceptScore W2012361142C54355233 @default.
• W2012361142 hasConceptScore W2012361142C70721500 @default.
• W2012361142 hasConceptScore W2012361142C86803240 @default.
• W2012361142 hasIssue "4" @default.
• W2012361142 hasLocation W20123611421 @default.
• W2012361142 hasLocation W20123611422 @default.
• W2012361142 hasLocation W20123611423 @default.
• W2012361142 hasLocation W20123611424 @default.
• W2012361142 hasOpenAccess W2012361142 @default.
• W2012361142 hasPrimaryLocation W20123611421 @default.
• W2012361142 hasRelatedWork W2147243590 @default.
• W2012361142 hasRelatedWork W2153652827 @default.
• W2012361142 hasRelatedWork W2186284405 @default.
• W2012361142 hasRelatedWork W2252005665 @default.
• W2012361142 hasRelatedWork W2380556669 @default.

• next