The molecular mechanisms underlying tissue regeneration and wound healing are still poorly understood despite their importance. In this paper we develop a bioinformatics approach, combining biology and network theory to drive experiments for better understanding the genetic underpinnings of wound healing mechanisms and for selecting potential drug targets. We start by selecting literature-relevant genes in murine wound healing, and inferring from them a Protein-Protein Interaction (PPI) network. Then, we analyze the network to rank wound healing-related genes according to their topological properties. Lastly, we perform a procedure for in-silico simulation of a treatment action in a biological pathway. The findings obtained by applying the developed pipeline, including gene expression analysis, confirms how a network-based bioinformatics method is able to prioritize candidate genes for in vitro analysis, thus speeding up the understanding of molecular mechanisms and supporting the discovery of potential drug targets.

abstract: Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive. / Dissertation/Thesis / Ph.D. Computer Science 2011

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Answers to the most complex biological questions are rarely determined solely from the experimental evidence. It requires subsequent analysis of many data sources that are often heterogeneous. Most biological data repositories focus on providing only one particular type of data, such as sequences, molecular interactions, protein structure, or gene expression. In many cases, it is required for researchers to visit several different databases to answer one scientific question. It is essential to develop strategies to integrate disparate biological data sources that are efficient and seamless to facilitate the discovery of novel associations and validate existing hypotheses. This thesis presents the design and development of different integration strategies of biological and clinical systems. The BioSPIDA system is a data warehousing solution that integrates many NCBI databases and other biological sources on protein sequences, protein domains, and biological pathways. It utilizes a universal parser facilitating integration without developing separate source code for each data site. This enables users to execute fine-grained queries that can filter genes by their protein interactions, gene expressions, functional annotation, and protein domain representation. Relational databases can powerfully return and generate quickly filtered results to research questions, but they are not the most suitable solution in all cases. Clinical patients and genes are typically annotated by concepts in hierarchical ontologies and performance of relational databases are weakened considerably when traversing and representing graph structures. This thesis illustrates when relational databases are most suitable as well as comparing the performance benchmarks of semantic web technologies and graph databases when comparing ontological concepts. Several approaches of analyzing integrated data will be discussed to demonstrate the advantages over dependencies on remote data centers. Intensive Care Patients are prioritized by their length of stay and their severity class is estimated by their diagnosis to help minimize wait time and preferentially treat patients by their condition. In a separate study, semantic clustering of patients is conducted by integrating a clinical database and a medical ontology to help identify multi-morbidity patterns. In the biological area, gene pathways, protein interaction networks, and functional annotation are integrated to help predict and prioritize candidate disease genes. This thesis will present the results that were able to be generated from each project through utilizing a local repository of genes, functional annotations, protein interactions, clinical patients, and medical ontologies.

Doenças complexas são caracterizadas por serem poligênicas e multifatoriais, o que representa um desafio em relação à busca de genes relacionados a elas. Com o advento das tecnologias de sequenciamento em larga escala do genoma e das medições de expressão gênica (transcritoma), bem como o conhecimento de interações proteína-proteína, doenças complexas têm sido sistematicamente investigadas. Particularmente, baseando-se no paradigma Network Medicine, as redes de interação proteína-proteína (PPI -- Protein-Protein Interaction) têm sido utilizadas para priorizar genes relacionados às doenças complexas segundo suas características topológicas. Entretanto, as redes PPI são afetadas pelo viés da literatura, em que as proteínas mais estudadas tendem a ter mais conexões, degradando a qualidade dos resultados. Adicionalmente, métodos que utilizam somente redes PPI fornecem apenas resultados estáticos e não-específicos, uma vez que as topologias destas redes não são específicas de uma determinada doença. Neste trabalho, desenvolvemos uma metodologia para priorizar genes e vias biológicas relacionados à uma dada doença complexa, através de uma abordagem integrativa de dados de redes PPI, transcritômica e genômica, visando aumentar a replicabilidade dos diferentes estudos e a descoberta de novos genes associados à doença. Após a integração das redes PPI com dados de expressão gênica, aplicamos as hipóteses da Network Medicine à rede resultante para conectar genes sementes (relacionados à doença, definidos a partir de estudos de associação) através de caminhos mínimos que possuam maior co-expressão entre seus genes. Dados de expressão em duas condições (controle e doença) são usados separadamente para obter duas redes, em que cada nó (gene) dessas redes é pontuado segundo fatores topológicos e de co-expressão. Baseado nesta pontuação, desenvolvemos dois escores de ranqueamento: um que prioriza genes com maior alteração entre suas pontuações em cada condição, e outro que privilegia genes com a maior soma destas pontuações. A aplicação do método a três estudos envolvendo dados de expressão de esquizofrenia recuperou com sucesso genes diferencialmente co-expressos em duas condições, e ao mesmo tempo evitou o viés da literatura. Além disso, houve uma melhoria substancial na replicação dos resultados pelo método aplicado aos três estudos, que por métodos convencionais não alcançavam replicabilidade satisfatória. / Complex diseases are characterized as being poligenic and multifactorial, so this poses a challenge regarding the search for genes related to them. With the advent of high-throughput technologies for genome sequencing and gene expression measurements (transcriptome), as well as the knowledge of protein-protein interactions, complex diseases have been sistematically investigated. Particularly, Protein-Protein Interaction (PPI) networks have been used to prioritize genes related to complex diseases according to its topological features. However, PPI networks are affected by ascertainment bias, in which the most studied proteins tend to have more connections, degrading the quality of the results. Additionally, methods using only PPI networks can provide just static and non-specific results, since the topologies of these networks are not specific of a given disease. In this work, we developed a methodology to prioritize genes and biological pathways related to a given complex disease, through an approach that integrates data from PPI networks, transcriptomics and genomics, aiming to increase replicability of different studies and to discover new genes associated to the disease. The methodology integrates PPI network and gene expression data, and then applies the Network Medicine Hypotheses to the resulting network in order to connect seed genes (obtained from association studies) through shortest paths possessing larger coexpression among their genes. Gene expression data in two conditions (control and disease) are used to obtain two networks, where each node (gene) in these networks is rated according to topological and coexpression aspects. Based on this rating, we developed two ranking scores: one that prioritizes genes with the largest alteration between their ratings in each condition, and another that favors genes with the greatest sum of these scores. The application of this method to three studies involving schizophrenia expression data successfully recovered differentially co-expressed gene in two conditions, while avoiding the ascertainment bias. Furthermore, when applied to the three studies, the method achieved a substantial improvement in replication of results, while other conventional methods did not reach a satisfactory replicability.

Orientador: Prof. Dr. David Corrêa Martins Júnior / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Ciência da Computação, 2017.

Doenças complexas são caracterizadas por serem poligênicas e multifatoriais, o que representa um desafio em relação à busca de genes relacionados a elas. Com o advento das tecnologias de sequenciamento em larga escala do genoma e das medições de expressão gênica (transcritoma), bem como o conhecimento de interações proteína-proteína, doenças complexas têm sido sistematicamente investigadas. Particularmente, baseando-se no paradigma Network Medicine, as redes de interação proteína-proteína (PPI -- Protein-Protein Interaction) têm sido utilizadas para priorizar genes relacionados às doenças complexas segundo suas características topológicas. Entretanto, as redes PPI são afetadas pelo viés da literatura, em que as proteínas mais estudadas tendem a ter mais conexões, degradando a qualidade dos resultados. Adicionalmente, métodos que utilizam somente redes PPI fornecem apenas resultados estáticos e não-específicos, uma vez que as topologias destas redes não são específicas de uma determinada doença. Neste trabalho, desenvolvemos uma metodologia para priorizar genes e vias biológicas relacionados à uma dada doença complexa, através de uma abordagem integrativa de dados de redes PPI, transcritômica e genômica, visando aumentar a replicabilidade dos diferentes estudos e a descoberta de novos genes associados à doença. Após a integração das redes PPI com dados de expressão gênica, aplicamos as hipóteses da Network Medicine à rede resultante para conectar genes sementes (relacionados à doença, definidos a partir de estudos de associação) através de caminhos mínimos que possuam maior co-expressão entre seus genes. Dados de expressão em duas condições (controle e doença) são usados separadamente para obter duas redes, em que cada nó (gene) dessas redes é pontuado segundo fatores topológicos e de co-expressão. Baseado nesta pontuação, desenvolvemos dois escores de ranqueamento: um que prioriza genes com maior alteração entre suas pontuações em cada condição, e outro que privilegia genes com a maior soma destas pontuações. A aplicação do método a três estudos envolvendo dados de expressão de esquizofrenia recuperou com sucesso genes diferencialmente co-expressos em duas condições, e ao mesmo tempo evitou o viés da literatura. Além disso, houve uma melhoria substancial na replicação dos resultados pelo método aplicado aos três estudos, que por métodos convencionais não alcançavam replicabilidade satisfatória. / Complex diseases are characterized as being poligenic and multifactorial, so this poses a challenge regarding the search for genes related to them. With the advent of high-throughput technologies for genome sequencing and gene expression measurements (transcriptome), as well as the knowledge of protein-protein interactions, complex diseases have been sistematically investigated. Particularly, Protein-Protein Interaction (PPI) networks have been used to prioritize genes related to complex diseases according to its topological features. However, PPI networks are affected by ascertainment bias, in which the most studied proteins tend to have more connections, degrading the quality of the results. Additionally, methods using only PPI networks can provide just static and non-specific results, since the topologies of these networks are not specific of a given disease. In this work, we developed a methodology to prioritize genes and biological pathways related to a given complex disease, through an approach that integrates data from PPI networks, transcriptomics and genomics, aiming to increase replicability of different studies and to discover new genes associated to the disease. The methodology integrates PPI network and gene expression data, and then applies the Network Medicine Hypotheses to the resulting network in order to connect seed genes (obtained from association studies) through shortest paths possessing larger coexpression among their genes. Gene expression data in two conditions (control and disease) are used to obtain two networks, where each node (gene) in these networks is rated according to topological and coexpression aspects. Based on this rating, we developed two ranking scores: one that prioritizes genes with the largest alteration between their ratings in each condition, and another that favors genes with the greatest sum of these scores. The application of this method to three studies involving schizophrenia expression data successfully recovered differentially co-expressed gene in two conditions, while avoiding the ascertainment bias. Furthermore, when applied to the three studies, the method achieved a substantial improvement in replication of results, while other conventional methods did not reach a satisfactory replicability.

Gene prioritization involves ranking genes by possible relevance to a disease of interest. This is important in order to narrow down the set of genes to be investigated biologically, and over the years, several computational approaches have been proposed for automat-ically prioritizing genes using some form of gene-related data, mostly using statistical or machine learning methods. Recently, Agarwal and Sengupta (2009) proposed the use of learning-to-rank methods, which have been used extensively in information retrieval and related fields, to learn a ranking of genes from a given data source, and used this approach to successfully identify novel genes related to leukemia and colon cancer using only gene expression data. In this work, we explore the possibility of combining such learning-to-rank methods with rank aggregation techniques to learn a ranking of genes from multiple heterogeneous data sources, such as gene expression data, gene ontology data, protein-protein interaction data, etc. Rank aggregation methods have their origins in voting theory, and have been used successfully in meta-search applications to aggregate webpage rankings from different search engines. Here we use graph-based learning-to-rank methods to learn a ranking of genes from each individual data source represented as a graph, and then apply rank aggregation methods to aggregate these rankings into a single ranking over the genes. The thesis describes our approach, reports experiments with various data sets, and presents our findings and initial conclusions.

Mapping of the human genome has been instrumental in understanding diseasescaused by changes in single genes. However, disease mechanisms involvingmultiple genes have proven to be much more elusive. Their complexityemerges from interactions of intracellular molecules and makes them immuneto the traditional reductionist approach. Only by modelling this complexinteraction pattern using networks is it possible to understand the emergentproperties that give rise to diseases.The overarching term used to describe both physical and indirect interactionsinvolved in the same functions is functional association. FunCoup is oneof the most comprehensive networks of functional association. It uses a naïveBayesian approach to integrate high-throughput experimental evidence of intracellularinteractions in humans and multiple model organisms. In the firstupdate, both the coverage and the quality of the interactions, were increasedand a feature for comparing interactions across species was added. The latestupdate involved a complete overhaul of all data sources, including a refinementof the training data and addition of new class and sources of interactionsas well as six new species.Disease-specific changes in genes can be identified using high-throughputgenome-wide studies of patients and healthy individuals. To understand theunderlying mechanisms that produce these changes, they can be mapped tocollections of genes with known functions, such as pathways. BinoX wasdeveloped to map altered genes to pathways using the topology of FunCoup.This approach combined with a new random model for comparison enables BinoXto outperform traditional gene-overlap-based methods and other networkbasedtechniques.Results from high-throughput experiments are challenged by noise and biases,resulting in many false positives. Statistical attempts to correct for thesechallenges have led to a reduction in coverage. Both limitations can be remediedusing prioritisation tools such as MaxLink, which ranks genes using guiltby association in the context of a functional association network. MaxLink’salgorithm was generalised to work with any disease phenotype and its statisticalfoundation was strengthened. MaxLink’s predictions were validatedexperimentally using FRET.The availability of prioritisation tools without an appropriate way to comparethem makes it difficult to select the correct tool for a problem domain.A benchmark to assess performance of prioritisation tools in terms of theirability to generalise to new data was developed. FunCoup was used for prioritisationwhile testing was done using cross-validation of terms derived fromGene Ontology. This resulted in a robust and unbiased benchmark for evaluationof current and future prioritisation tools. Surprisingly, previously superiortools based on global network structure were shown to be inferior to a localnetwork-based tool when performance was analysed on the most relevant partof the output, i.e. the top ranked genes.This thesis demonstrates how a network that models the intricate biologyof the cell can contribute with valuable insights for researchers that study diseaseswith complex genetic origins. The developed tools will help the researchcommunity to understand the underlying causes of such diseases and discovernew treatment targets. The robust way to benchmark such tools will help researchersto select the proper tool for their problem domain. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 5: Manuscript. Paper 6: Manuscript.</p>