NET-GE: a novel NETwork-based Gene Enrichment for detecting biological processes associated to Mendelian diseases

Enrichment analysis is a widely applied procedure for shedding light on the molecular mechanisms and functions at the basis of phenotypes, for enlarging the dataset of possibly related genes/proteins and for helping interpretation and prioritization of newly determined variations. Several standard and Network-based enrichment methods are available. Both approaches rely on the annotations that characterize the genes/proteins included in the input set; network based ones also include in different ways physical and functional relationships among different genes or proteins that can be extracted from the available biological networks of interactions

Computing Network of Diseases and Pharmacological Entities through the Integration of Distributed Literature Mining and Ontology Mapping

The proliferation of -omics (such as, Genomics, Proteomics) and -ology (such as, System Biology, Cell Biology, Pharmacology) have spawned new frontiers of research in drug discovery and personalized medicine. A vast amount (21 million) of published research results are archived in the PubMed and are continually growing in size. To improve the accessibility and utility of such a large number of literatures, it is critical to develop a suit of semantic sensitive technology that is capable of discovering knowledge and can also infer possible new relationships based on statistical co-occurrences of meaningful terms or concepts. In this context, this thesis presents a unified framework to mine a large number of literatures through the integration of latent semantic analysis (LSA) and ontology mapping. In particular, a parameter optimized, robust, scalable, and distributed LSA (DiLSA) technique was designed and implemented on a carefully selected 7.4 million PubMed records related to pharmacology. The DiLSA model was integrated with MeSH to make the model effective and efficient for a specific domain. An optimized multi-gram dictionary was customized by mapping the MeSH to build the DiLSA model. A fully integrated web-based application, called PharmNet, was developed to bridge the gap between biological knowledge and clinical practices. Preliminary analysis using the PharmNet shows an improved performance over global LSA model. A limited expert evaluation was performed to validate the retrieved results and network with biological literatures. A thorough performance evaluation and validation of results is in progress

BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation

We present BioGraph, a data integration and data mining platform for the exploration and discovery of biomedical information. The platform offers prioritizations of putative disease genes, supported by functional hypotheses. We show that BioGraph can retrospectively confirm recently discovered disease genes and identify potential susceptibility genes, outperforming existing technologies, without requiring prior domain knowledge. Additionally, BioGraph allows for generic biomedical applications beyond gene discovery. BioGraph is accessible at http://www.biograph.be

Knowledge Discovery Through Large-Scale Literature-Mining of Biological Text-Data

The aim of this study is to develop scalable and efficient literature-mining framework for knowledge discovery in the field of medical and biological sciences. Using this scalable framework, customized disease-disease interaction network can be constructed. Features of the proposed network that differentiate it from existing networks are its 1) flexibility in the level of abstraction, 2) broad coverage, and 3) domain specificity. Empirical results for two neurological diseases have shown the utility of the proposed framework. The second goal of this study is to design and implement a bottom-up information retrieval approach to facilitate literature-mining in the specialized field of medical genetics. Experimental results are being corroborated at the moment

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

Enhanced label-free discovery proteomics through improved data analysis and knowledge enrichment

Mass spectrometry (MS)-based proteomics has evolved into an important tool applied in fundamental biological research as well as biomedicine and medical research. The rapid developments of technology have required the establishment of data processing algorithms, protocols and workflows. The successful application of such software tools allows for the maturation of instrumental raw data into biological and medical knowledge. However, as the choice of algorithms is vast, the selection of suitable processing tools for various data types and research questions is not trivial. In this thesis, MS data processing related to the label-free technology is systematically considered. Essential questions, such as normalization, choice of preprocessing software, missing values and imputation, are reviewed in-depth. Considerations related to preprocessing of the raw data are complemented with exploration of methods for analyzing the processed data into practical knowledge. In particular, longitudinal differential expression is reviewed in detail, and a novel approach well-suited for noisy longitudinal high-througput data with missing values is suggested. Knowledge enrichment through integrated functional enrichment and network analysis is introduced for intuitive and information-rich delivery of the results. Effective visualization of such integrated networks enables the fast screening of results for the most promising candidates (e.g. clusters of co-expressing proteins with disease-related functions) for further validation and research. Finally, conclusions related to the prepreprocessing of the raw data are combined with considerations regarding longitudinal differential expression and integrated knowledge enrichment into guidelines for a potential label-free discovery proteomics workflow. Such proposed data processing workflow with practical suggestions for each distinct step, can act as a basis for transforming the label-free raw MS data into applicable knowledge.Massaspektrometriaan (MS) pohjautuva proteomiikka on kehittynyt tehokkaaksi työkaluksi, jota hyödynnetään niin biologisessa kuin lääketieteellisessäkin tutkimuksessa. Alan nopea kehitys on synnyttänyt erikoistuneita algoritmeja, protokollia ja ohjelmistoja datan käsittelyä varten. Näiden ohjelmistotyökalujen oikeaoppinen käyttö lopulta mahdollistaa datan tehokkaan esikäsittelyn, analysoinnin ja jatkojalostuksen biologiseksi tai lääketieteelliseksi ymmärrykseksi. Mahdollisten vaihtoehtojen suuresta määrästä johtuen sopivan ohjelmistotyökalun valinta ei usein kuitenkaan ole yksiselitteistä ja ongelmatonta. Tässä väitöskirjassa tarkastellaan leimaamattomaan proteomiikkaan liittyviä laskennallisia työkaluja. Väitöskirjassa käydään läpi keskeisiä kysymyksiä datan normalisoinnista sopivan esikäsittelyohjelmiston valintaan ja puuttuvien arvojen käsittelyyn. Datan esikäsittelyn lisäksi tarkastellaan datan tilastollista jatkoanalysointia sekä erityisesti erilaisen ekspression havaitsemista pitkittäistutkimuksissa. Väitöskirjassa esitellään uusi, kohinaiselle ja puuttuvia arvoja sisältävälle suurikapasiteetti-pitkittäismittausdatalle soveltuva menetelmä erilaisen ekspression havaitsemiseksi. Uuden tilastollisen menetelmän lisäksi väitöskirjassa tarkastellaan havaittujen tilastollisten löydösten rikastusta käytännön ymmärrykseksi integroitujen rikastumis- ja verkkoanalyysien kautta. Tällaisten funktionaalisten verkkojen tehokas visualisointi mahdollistaa keskeisten tulosten nopean tulkinnan ja kiinnostavimpien löydösten valinnan jatkotutkimuksia varten. Lopuksi datan esikäsittelyyn ja pitkittäistutkimusten tilastollisen jatkokäsittelyyn liittyvät johtopäätökset yhdistetään tiedollisen rikastamisen kanssa. Näihin pohdintoihin perustuen esitellään mahdollinen työnkulku leimaamattoman MS proteomiikkadatan käsittelylle raakadatasta hyödynnettäviksi löydöksiksi sekä edelleen käytännön biologiseksi ja lääketieteelliseksi ymmärrykseksi

Application of knowledge discovery and data mining methods in livestock genomics for hypothesis generation and identification of biomarker candidates influencing meat quality traits in pigs

Recent advancements in genomics and genome profiling technologies have lead to an increase in the amount of data available in livestock genomics. Yet, most of the studies done in livestock genomics have been following a reductionist approach and very few studies have either followed data mining or knowledge discovery concepts or made use of the wealth of information available in the public domain to gain new knowledge. The goals of this thesis were: (i) the adoption of existing analysis strategies or the development of novel approaches in livestock genomics for integrative data analysis following the principles of data mining and knowledge discovery and (ii) demonstrating the application of such approaches in livestockgenomics for hypothesis generation and biomarker discovery. A pig meat quality trait termed androstenone measurement in backfat was selected as the target phenotype for the experiments. Two experiments were performed as a part of this thesis. The first one followed a knowledge driven approach merging high-throughput expression data with metabolic interaction network. Based on the results from this experiment, several novel biomarker candidates and a hypothesis regarding different mechanisms regulating androstenone synthesis in porcine testis samples with divergent androstenone measurements in back fat were proposed. The model proposed that the elevated levels of androstenone synthesis in sample population could be due to the combined effect of cAMP/PKA signaling, elevated levels of fatty acid metabolism and anti lipid peroxidation activity of members of glutathione metabolic pathway. The second experiment followed a data driven approach and integrated gene expression data from multiple porcine populations to identify similarities in gene expression patterns related to hepatic androstenone metabolism. The results indicated that one of the low androstenone phenotype specific co-expression cluster was functionally enriched in pathways related to androgen and androstenone metabolism and that the members of this cluster exhibited weak co-expression in high androstenone phenotype. Based on the results from this experiment, this co-expression cluster was proposed as a signature cluster for hepatic androstenone metabolism in boars with low androstenone content in back fat. The results from these experiments indicate that integrative analysis approaches following data mining and knowledge discovery concepts can be used for the generation of new knowledge from existing data in livestock genomics. But, limited data availability in livestock genomics is a hindrance to the extensive use such analysis methods in livestock genomics field for gaining new knowledge. In conclusion, this study was aimed at demonstrating the capabilities of data mining and knowledge discovery methods and integrative analysis approaches to generate new knowledge in livestock genomics using existing datasets. The results from the experiments hint the possibilities of further exploring such methods for knowledge generation in this field. Although the application of such methods is limited in livestock genomics due to data availability issues at present, the increase in data availability due to evolving high throughput technologies and decrease in data generation costs would aid in the wide spread use of such methods in livestock genomics in the coming future