In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations, and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural inductive matrix completion (NIMC), in disease-gene prediction. Comparing to the state-of-the-art inductive matrix completion method, using neural networks allows us to learn latent features from non-linear functions of input features. Previous methods use disease features only from mining text. Comparing to text mining, disease ontology is a more informative way of discovering correlation of dis- eases, from which we can calculate the similarities between diseases and help increase the performance of predicting disease-gene associations. We compare the proposed method with other state-of-the-art methods for pre- dicting associated genes for diseases from the Online Mendelian Inheritance in Man (OMIM) database. Results show that both new features and the proposed NIMC model can improve the chance of recovering an unknown associated gene in the top 100 predicted genes. Best results are obtained by using both the new features and the new model. Results also show the proposed method does better in predicting associated genes for newly discovered diseases.

Ontologies are widely used in various domains, including biomedical research, to structure information, represent knowledge, and analyze data. The combination of ontologies from different domains is crucial for systematic data analysis and comparison of similar domains. This process requires ontology composition, integration, and alignment, which involve creating new classes by reusing classes from different domains, aggregating types of ontologies within the same domain, and finding correspondences between ontologies within the same or similar domain. This thesis presents use cases where we applied ontology composition, integration, and alignment of phenotype ontologies, and evaluated the resulting ontologies and alignment. First, we analyzed a large aging dataset of inbred laboratory mice, using Mouse Anatomy and Mouse Pathology ontologies. Second, we integrated phenotype ontologies for human and model organism phenotypes to enable comparisons of phenotypes between and within individual species. We developed Pheno-e, an extension of PhenomeNet. We identified novel abnormal anatomical classes for fly phenotypes, allowing the annotation of fly genes that were not annotated before. We demonstrate the distinct contributions of each species' phenotypic data to detecting human diseases using Pheno-e, and show that mouse phenotypic data contributes the most to the discovery of gene--disease associations. This work could guide the selection of model organisms when building methods to find gene-disease associations. Additionally, we refined class definitions in phenotypic ontologies, specifically targeting cell cardinality phenotypes. This representation resolved incorrect inferences in the utilized ontologies, enabling accurate interpretation of phenotypic descriptions. Our findings reveal that this correction enhances gene-disease prediction for diseases associated with cardinality phenotypes. Third, we introduce a novel neural-symbolic method that combines logic fundamentals with machine learning for ontology alignment. This method begins with symbolic representation, followed by iterative neural learning for alignment and symbolic representation consistency checking and reasoning, and back to neural learning. We demonstrate that our system generates noncontroversial alignments first and these alignments are coherent with respect to OWL EL. This novel method can pave the way for more accurate and efficient ontology-based methods, which can have significant implications for various semantic web applications.

Background: The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and epidemiological studies. The development of computable phenotype algorithms to solve these tasks is a challenging problem, caused by various reasons. Firstly, the term 'phenotype' has no generally agreed definition and its meaning depends on context. Secondly, the phenotypes are most commonly specified as non-computable descriptive documents. Recent attempts have shown that ontologies are a suitable way to handle phenotypes and that they can support clinical research and decision making. The SMITH Consortium is dedicated to rapidly establish an integrative medical informatics framework to provide physicians with the best available data and knowledge and enable innovative use of healthcare data for research and treatment optimisation. In the context of a methodological use case 'phenotype pipeline' (PheP), a technology to automatically generate phenotype classifications and annotations based on electronic health records (EHR) is developed. A large series of phenotype algorithms will be implemented. This implies that for each algorithm a classification scheme and its input variables have to be defined. Furthermore, a phenotype engine is required to evaluate and execute developed algorithms. Results: In this article, we present a Core Ontology of Phenotypes (COP) and the software Phenotype Manager (PhenoMan), which implements a novel ontology-based method to model, classify and compute phenotypes from already available data. Our solution includes an enhanced iterative reasoning process combining classification tasks with mathematical calculations at runtime. The ontology as well as the reasoning method were successfully evaluated with selected phenotypes including SOFA score, socio-economic status, body surface area and WHO BMI classification based on available medical data. Conclusions: We developed a novel ontology-based method to model phenotypes of living beings with the aim of automated phenotype reasoning based on available data. This new approach can be used in clinical context, e.g., for supporting the diagnostic process, evaluating risk factors, and recruiting appropriate participants for clinical and epidemiological studies.

Depuis le début des années 90, le projet génome humain a permis l’émergence de nombreuses techniques globalisantes porteuses du suffixe –omique : génomique, transcriptomique, protéomique, épigénomique, etc.… L’étude globale de l’ensemble des phénotypes humains (« phénome ») est à l’origine de nouvelles technologies constituant la « phénomique ». L’approche phénomique permet de déterminer des liens entre des combinaisons de traits phénomiques. Nous voulons appliquer cette approche à l’étude des malformations humaines en particulier leurs combinaisons, ne formant des syndromes, des associations ou des séquences bien caractérisés que dans un petit nombre de cas. Afin d’évaluer la faisabilité de cette approche, pour une étude pilote nous avons décidé d’établir une base de données pour la description phénotypique des anomalies foetales. Nous avons effectué ces étapes : o Réalisation d’une étude rétrospective d’une série d’autopsies de foetus au CHU Sainte- Justine (Montréal, QC, Canada) entre 2001-2006 o Élaboration de trois thésaurus et d’une ontologie des anomalies développementales humaines o Construction une base de données en langage MySQL Cette base de données multicentrique accessible sur (http://www.malformations.org), nous permet de rechercher très facilement les données phénotypiques des 543 cas observés porteurs d’une anomalie donnée, de leur donner une description statistique et de générer les différents types d’hypothèses. Elle nous a également permis de sélectionner 153 cas de foetus malformés qui font l’objet d’une étude de micropuce d’hybridation génomique comparative (aCGH) à la recherche d’une anomalie génomique. / Since the early 90s, the Human Genome Project (HGP) has allowed the development of numerous worldwide techniques which carried the suffix “omic”: genomic, transcriptomic, proteomic, epigenomic, etc…. The global investigation of the sets of human phenotypes (phenome) is called phenomic. With phenomic studies we should be able to determine the links among similar phenotypic groups. We wish to apply this approach to human dysmorphology, particularly malformation combinations, which form characteristic malformation associations, malformation sequences, malformation syndromes or malformation disorders only in a minority of cases. As a graduate student research project, we decided to perform a retrospective study of the sets of pathology reports including 543 fetuses autopsied in the Department of Pathology of CHU Sainte-Justine (Montreal, QC, Canada) between 2001 and 2006. We have established an open Malformation Database (MDB) which can be accessed at http://www.malformations.org. To achieve this, we conducted the following steps: o Realization of a retrospective study of fetopathology reports for fetal malformations. o Development of an ontology along with three thesauruses of human developmental anomalies. o Implementation of these thesauruses and ontology in the MySQL system. This hypothesis-generating database allows us to easily retrieve the fetal cases (phenotypic data) with anomalies, calculate the frequencies of these anomalies, and evaluate the feasibility of the phenomic approach to human dysmorphogenesis. We were able as well to select 153 cases of malformed fetuses which will be the subject of aCGH array study for genomic research of human anomalies.

Depuis le début des années 90, le projet génome humain a permis l’émergence de nombreuses techniques globalisantes porteuses du suffixe –omique : génomique, transcriptomique, protéomique, épigénomique, etc.… L’étude globale de l’ensemble des phénotypes humains (« phénome ») est à l’origine de nouvelles technologies constituant la « phénomique ». L’approche phénomique permet de déterminer des liens entre des combinaisons de traits phénomiques. Nous voulons appliquer cette approche à l’étude des malformations humaines en particulier leurs combinaisons, ne formant des syndromes, des associations ou des séquences bien caractérisés que dans un petit nombre de cas. Afin d’évaluer la faisabilité de cette approche, pour une étude pilote nous avons décidé d’établir une base de données pour la description phénotypique des anomalies foetales. Nous avons effectué ces étapes : o Réalisation d’une étude rétrospective d’une série d’autopsies de foetus au CHU Sainte- Justine (Montréal, QC, Canada) entre 2001-2006 o Élaboration de trois thésaurus et d’une ontologie des anomalies développementales humaines o Construction une base de données en langage MySQL Cette base de données multicentrique accessible sur (http://www.malformations.org), nous permet de rechercher très facilement les données phénotypiques des 543 cas observés porteurs d’une anomalie donnée, de leur donner une description statistique et de générer les différents types d’hypothèses. Elle nous a également permis de sélectionner 153 cas de foetus malformés qui font l’objet d’une étude de micropuce d’hybridation génomique comparative (aCGH) à la recherche d’une anomalie génomique. / Since the early 90s, the Human Genome Project (HGP) has allowed the development of numerous worldwide techniques which carried the suffix “omic”: genomic, transcriptomic, proteomic, epigenomic, etc…. The global investigation of the sets of human phenotypes (phenome) is called phenomic. With phenomic studies we should be able to determine the links among similar phenotypic groups. We wish to apply this approach to human dysmorphology, particularly malformation combinations, which form characteristic malformation associations, malformation sequences, malformation syndromes or malformation disorders only in a minority of cases. As a graduate student research project, we decided to perform a retrospective study of the sets of pathology reports including 543 fetuses autopsied in the Department of Pathology of CHU Sainte-Justine (Montreal, QC, Canada) between 2001 and 2006. We have established an open Malformation Database (MDB) which can be accessed at http://www.malformations.org. To achieve this, we conducted the following steps: o Realization of a retrospective study of fetopathology reports for fetal malformations. o Development of an ontology along with three thesauruses of human developmental anomalies. o Implementation of these thesauruses and ontology in the MySQL system. This hypothesis-generating database allows us to easily retrieve the fetal cases (phenotypic data) with anomalies, calculate the frequencies of these anomalies, and evaluate the feasibility of the phenomic approach to human dysmorphogenesis. We were able as well to select 153 cases of malformed fetuses which will be the subject of aCGH array study for genomic research of human anomalies.