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This thesis consists of two projects in which various machine learning approaches and statistical analysis for the integration of biomedical data analysis were explored, developed and tested. Integration of different biomedical data sources allows us to get a better understating of human body from a bigger picture. If we can get a more complete view of the data, we not only get a more complete view of the molecule basis of phenotype, but also possibly can identify abnormality in diseases which were not found when using only one type of biomedical data. The objective of the first project is to find biological pathways which are related to Duechenne Muscular Dystrophy(DMD) and Lamin A/C(LMNA) using the integration of multi-omics data. We proposed a novel method which allows us to integrate proteins, mRNAs and miRNAs to find disease related pathways. The goal of the second project is to develop a personalized recommendation system which recommend cancer treatments to patients. Compared to the traditional way of using only users' rating to impute missing values, we proposed a method to incorporate users' profile to help enhance the accuracy of the prediction. / Master of Science

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Indiana University-Purdue University Indianapolis (IUPUI) / New neurons are generated throughout adulthood in two regions of the brain, the dentate gyrus of the hippocampus, which is important for memory formation and cognitive functions, and the sub-ventricular zone of the olfactory bulb, which is important for the sense of smell, and are incorporated into hippocampal network circuitry. Disruption of this process has been postulated to contribute to neurodegenerative disorders including Alzheimer’s disease [1]. AD is the most common form of adult-onset dementia and the number of patients with AD escalates dramatically each year. The generation of new neurons in the dentate gyrus declines with age and in AD. Many of the molecular players in AD are also modulators of adult neurogenesis, but the genetic mechanisms influencing adult neurogenesis in AD are unclear. The overall goal of this project is to identify candidate genes and pathways that play a role in neurogenesis in the adult brain and to test the hypotheses that 1) hippocampal neurogenesis-related genes and pathways are significantly perturbed in AD and 2) neurogenesis-related pathways are significantly associated with hippocampal volume and other AD-related biomarker endophenotypes including brain deposition of amyloid-β and tau pathology. First, potential modulators of adult neurogenesis and their roles in neurodegenerative diseases were evaluated. Candidate genes that control the turnover process of neural stem cells/precursors to new functional neurons during adult neurogenesis were manually curated using a pathway-based systems biology approach. Second, a targeted neurogenesis pathway-based gene analysis was performed resulting in the identification of ADORA2A as associated with hippocampal volume and memory performance in mild cognitive impairment and AD. Third, a genome-wide gene-set enrichment analysis was conducted to discover associations between hippocampal volume and AD related endophenotypes and neurogenesis-related pathways. Within the discovered neurogenesis enriched pathways, a gene-based association analysis identified TESC and ACVR1 as significantly associated with hippocampal volume and APOE and PVLR2 as significantly associated with tau and amyloid beta levels in cerebrospinal fluid. This project identifies new genetic contributions to hippocampal neurogenesis with translational implications for novel therapeutic targets related to learning and memory and neuroprotection in AD.

Current approaches integrating large scale data and information from a variety of sources to reveal molecular basis of cellular events do not adequately benefit from pathway information. Here, we portray a network structure based pathway enrichment system that fuses and exploits model and data: signalling pathways are taken as the biological models while microarray and ChIP-seq data are the sample input data sources among many other alternatives. Our model- and data-driven hybrid system allows to quantitatively assessing the biological activity of a cyclic pathway and simultaneous enrichment of the significant paths leading to the ultimate cellular response. Signal Transduction Score Flow (SiTSFlow) algorithm is the fundamental constituent of proposed network structure based pathway enrichment system. SiTSFlow algorithm converts each pathway into a cascaded graph and then gene scores are mapped onto the protein nodes. Gene scores are transferred to en route of the pathway to form a final activity score describing behaviour of a specific process in the pathway while enriching of the gene node scores. Because of cyclic pathways, the algorithm runs in an iterative manner and it terminates when the node scores converge. The converged final activity score provides a quantitative measure to assess the biological significance of a process under the given experimental conditions. The conversion of cyclic pathways into cascaded graphs is performed by using a linear time multiple source Breadth First Search Algorithm. Furthermore, proposed network structure based pathway enrichment system works in linear time in terms of nodes and edges of given pathways. In order to explore various biological responses of several processes in a global signalling network, the selected small pathways have been unified based on their common gene and process nodes. The merge algorithm for pathways also runs in linear time in terms of nodes and edges of given pathways. In the experiments, SiTSFlow algorithm proved the convergence behaviour of activity scores for several cyclic pathways and for a global signalling network. The biological results obtained by assessing of experimental data by described network structure based pathway enrichment system were in correlation with the expected cellular behaviour under the given experimental conditions.

Cell functions are steered by complex interactions of gene products, like forming a temporary or stable complex, altering gene expression or catalyzing a reaction. Mapping these interactions is the key in understanding biological processes and therefore is the focus of numerous experiments and studies. Small-scale experiments deliver high quality data but lack coverage whereas high-throughput techniques cover thousands of interactions but can be error-prone. Unfortunately all of these approaches can only focus on one type of interaction at the time. This makes experimental mapping of the genome-wide network a cost and time intensive procedure. However, to overcome these problems, different computational approaches have been suggested that integrate multiple data sets and/or different evidence types. This widens the stringent definition of an interaction and introduces a more general term - functional association.  FunCoup is a database for genome-wide functional association networks of Homo sapiens and 16 model organisms. FunCoup distinguishes between five different functional associations: co-membership in a protein complex, physical interaction, participation in the same signaling cascade, participation in the same metabolic process and for prokaryotic species, co-occurrence in the same operon. For each class, FunCoup applies naive Bayesian integration of ten different evidence types of data, to predict novel interactions. It further uses orthologs to transfer interaction evidence between species. This considerably increases coverage, and allows inference of comprehensive networks even for not well studied organisms.  BinoX is a novel method for pathway analysis and determining the relation between gene sets, using functional association networks. Traditionally, pathway annotation has been done using gene overlap only, but these methods only get a small part of the whole picture. Placing the gene sets in context of a network provides additional evidence for pathway analysis, revealing a global picture based on the whole genome. PathwAX is a web server based on the BinoX algorithm. A user can input a gene set and get online network crosstalk based pathway annotation. PathwAX uses the FunCoup networks and 280 pre-defined pathways. Most runs take just a few seconds and the results are summarized in an interactive chart the user can manipulate to gain further insights of the gene set's pathway associations. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.</p>

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Williams-Beuren syndrome (WBS) is a neurodevelopmental disorder caused by the common deletion of 26-28 contiguous genes in the 7q11.23 region, which poses difficulties to the establishment of genotype-phenotype correlations. The use of mouse models would broader the knowledge of the syndrome, the role of deleted genes, affected pathways and possible treatments. In this thesis project, several mouse models, tissues and cells have been used to define the phenotypes at different levels, the deregulated genes and pathways and to discover modifying elements and novel treatments for the cardiovascular phenotype. In addition, a new binding motif has been described for Gtf2i, a deleted gene encoding a transcription factor with a major role in WB, providing new target genes from deregulated pathways. The obtained results reveal the essential role of mouse models for the study of Williams-Beuren syndrome and provide new treatments options and affected pathways and genes which could be future treatment targets. / La síndrome de Williams-Beuren és una malaltia del neurodesenvolupament causada per una deleció comú d’entre 26 i 28 gens contigus a la regió 7q11.23, dificultant l’establiment de relacions genotip-fenotip. L’ús de models de ratolí pot augmentar el coneixement sobre la malaltia, el paper dels gens delecionats, les vies moleculars afectades i els futurs tractaments. En aquesta tesi s’han usat diversos models de ratolí, les seves cèl·lules i teixits per tal de descriure i definir fenotips, gens i vies moleculars desregulades i per descobrir elements modificadors i nous tractaments. Per últim, s’ha definit un nou motiu d’unió per Gtf2i, uns dels gens delecionats que codifica per un factor de transcripció amb un rol central en la síndrome, proporcionats possible nous gens diana de vies moleculars desregulades. Els resultats obtinguts revelen el paper essencial dels models de ratolí per a l’estudi de la síndrome de Williams-Beuren, proporcionen noves opcions terapèutiques i defineixen nous gens i vies moleculars afectades que podrien suposar noves dianes terapèutiques.