Learning by Fusing Heterogeneous Data

It has become increasingly common in science and technology to gather data about systems at different levels of granularity or from different perspectives. This often gives rise to data that are represented in totally different input spaces. A basic premise behind the study of learning from heterogeneous data is that in many such cases, there exists some correspondence among certain input dimensions of different input spaces. In our work we found that a key bottleneck that prevents us from better understanding and truly fusing heterogeneous data at large scales is identifying the kind of knowledge that can be transferred between related data views, entities and tasks. We develop interesting and accurate data fusion methods for predictive modeling, which reduce or entirely eliminate some of the basic feature engineering steps that were needed in the past when inferring prediction models from disparate data. In addition, our work has a wide range of applications of which we focus on those from molecular and systems biology: it can help us predict gene functions, forecast pharmacological actions of small chemicals, prioritize genes for further studies, mine disease associations, detect drug toxicity and regress cancer patient survival data. Another important aspect of our research is the study of latent factor models. We aim to design latent models with factorized parameters that simultaneously tackle multiple types of data heterogeneity, where data diversity spans across heterogeneous input spaces, multiple types of features, and a variety of related prediction tasks. Our algorithms are capable of retaining the relational structure of a data system during model inference, which turns out to be vital for good performance of data fusion in certain applications. Our recent work included the study of network inference from many potentially nonidentical data distributions and its application to cancer genomic data. We also model the epistasis, an important concept from genetics, and propose algorithms to efficiently find the ordering of genes in cellular pathways. A central topic of our Thesis is also the analysis of large data compendia as predictions about certain phenomena, such as associations between diseases and involvement of genes in a certain phenotype, are only possible when dealing with lots of data. Among others, we analyze 30 heterogeneous data sets to assess drug toxicity and over 40 human gene association data collections, the largest number of data sets considered by a collective latent factor model up to date. We also make interesting observations about deciding which data should be considered for fusion and develop a generic approach that can estimate the sensitivities between different data sets

Mining real-world networks in systems biology and economics

Recent advances in biotechnology have yielded an explosion of data describing biological systems, creating rich opportunities for new insights into cellular inner-workings and therapeutic discoveries. To keep up with this rapid growth and increase in data complexity, we need novel static, integrative, and dynamic methodologies to continue mining these networked systems. In this thesis we introduce new static, integrative, and dynamic computational frameworks for network analysis, and combine existing ones in new ways, to elucidate the biotechnological biases and functional principles governing molecular interactions and their implications in disease. We focus on mining new knowledge from the yeast and human interactomes, since these are currently the most complete data in biology. We perform three lines of experimental work: 1) the macro-scale study, where we model the yeast and human interactomes and show that their interactome data are growing in structurally and functionally principled ways, characterised by a non-random dual topological nature; 2) the micro-scale study, where we zoom into the specifics of wiring patterns around individual genes and uncover a unique core sub-structure within the human interactome, which contains driver genes dubbed to be the main triggers for disease onset; and 3) the data integration study, where we introduce a new computational framework for fusing multiple types of molecular interaction data and use it to construct the first unified model of the cell’s functional organisation and cross-communication lines. Similarly, a new field of systems economics has gained recent attention, with more financial and economic network data emerging at an increasing pace. Hence, we introduce a new computational methodology for tracking network dynamics and use it to quantify the micro- and macro-scale topological changes in the world trade network over the past 50 years, and to demonstrate the fundamental relationship between topological perturbations and indicators of countries’ political and economic stabilities.Open Acces

Integrative bioinformatics and graph-based methods for predicting adverse effects of developmental drugs

Adverse drug effects are complex phenomena that involve the interplay between drug molecules and their protein targets at various levels of biological organisation, from molecular to organismal. Many factors are known to contribute toward the safety profile of a drug, including the chemical properties of the drug molecule itself, the biological properties of drug targets and other proteins that are involved in pharmacodynamics and pharmacokinetics aspects of drug action, and the characteristics of the intended patient population. A multitude of scattered publicly available resources exist that cover these important aspects of drug activity. These include manually curated biological databases, high-throughput experimental results from gene expression and human genetics resources as well as drug labels and registered clinical trial records. This thesis proposes an integrated analysis of these disparate sources of information to help bridge the gap between the molecular and the clinical aspects of drug action. For example, to address the commonly held assumption that narrowly expressed proteins make safer drug targets, an integrative data-driven analysis was conducted to systematically investigate the relationship between the tissue expression profile of drug targets and the organs affected by clinically observed adverse drug reactions. Similarly, human genetics data were used extensively throughout the thesis to compare adverse symptoms induced by drug molecules with the phenotypes associated with the genes encoding their target proteins. One of the main outcomes of this thesis was the generation of a large knowledge graph, which incorporates diverse molecular and phenotypic data in a structured network format. To leverage the integrated information, two graph-based machine learning methods were developed to predict a wide range of adverse drug effects caused by approved and developmental therapies

Leveraging Client Processing for Location Privacy in Mobile Local Search

Usage of mobile services is growing rapidly. Most Internet-based services targeted for PC based browsers now have mobile counterparts. These mobile counterparts often are enhanced when they use user\u27s location as one of the inputs. Even some PC-based services such as point of interest Search, Mapping, Airline tickets, and software download mirrors now use user\u27s location in order to enhance their services. Location-based services are exactly these, that take the user\u27s location as an input and enhance the experience based on that. With increased use of these services comes the increased risk to location privacy. The location is considered an attribute that user\u27s hold as important to their privacy. Compromise of one\u27s location, in other words, loss of location privacy can have several detrimental effects on the user ranging from trivial annoyance to unreasonable persecution. More and more companies in the Internet economy rely exclusively on the huge data sets they collect about users. The more detailed and accurate the data a company has about its users, the more valuable the company is considered. No wonder that these companies are often the same companies that offer these services for free. This gives them an opportunity to collect more accurate location information. Research community in the location privacy protection area had to reciprocate by modeling an adversary that could be the service provider itself. To further drive this point, we show that a well-equipped service provider can infer user\u27s location even if the location information is not directly available by using other information he collects about the user. There is no dearth of proposals of several protocols and algorithms that protect location privacy. A lot of these earlier proposals require a trusted third party to play as an intermediary between the service provider and the user. These protocols use anonymization and/or obfuscation techniques to protect user\u27s identity and/or location. This requirement of trusted third parties comes with its own complications and risks and makes these proposals impractical in real life scenarios. Thus it is preferable that protocols do not require a trusted third party. We look at existing proposals in the area of private information retrieval. We present a brief survey of several proposals in the literature and implement two representative algorithms. We run experiments using different sizes of databases to ascertain their practicability and performance features. We show that private information retrieval based protocols still have long ways to go before they become practical enough for local search applications. We propose location privacy preserving mechanisms that take advantage of the processing power of modern mobile devices and provide configurable levels of location privacy. We propose these techniques both in the single query scenario and multiple query scenario. In single query scenario, the user issues a query to the server and obtains the answer. In the multiple query scenario, the user keeps sending queries as she moves about in the area of interest. We show that the multiple query scenario increases the accuracy of adversary\u27s determination of user\u27s location, and hence improvements are needed to cope with this situation. So, we propose an extension of the single query scenario that addresses this riskier multiple query scenario, still maintaining the practicability and acceptable performance when implemented on a modern mobile device. Later we propose a technique based on differential privacy that is inspired by differential privacy in statistical databases. All three mechanisms proposed by us are implemented in realistic hardware or simulators, run against simulated but real life data and their characteristics ascertained to show that they are practical and ready for adaptation. This dissertation study the privacy issues for location-based services in mobile environment and proposes a set of new techniques that eliminate the need for a trusted third party by implementing efficient algorithms on modern mobile hardware

Computational and chemical approaches to drug repurposing

Drug repurposing, which entails discovering novel therapeutic applications for already existing drugs, provides numerous benefits compared to conventional drug discovery methods. This strategy can be pursued through two primary approaches: computational and chemical. Computational methods involve the utilization of data mining and bioinformatics techniques to identify potential drug candidates, while chemical approaches involve experimental screens oriented to finding new potential treatments based on existing drugs. Both computational and chemical methods have proven successful in uncovering novel therapeutic uses for established drugs. During my PhD, I participated in several experimental drug repurposing screens based on high-throughput phenotypic approaches. Finally, attracted by the potential of computational drug repurposing pipelines, I decided to contribute and generate a web platform focused on the use of transcriptional signatures to identify potential new treatments for human disease. A summary of these studies follows: In Study I, we utilized the tetracycline repressor (tetR)-regulated mechanism to create a human osteosarcoma cell line (U2OS) with the ability to express TAR DNA-binding protein 43 (TDP-43) upon induction. TDP-43 is a protein known for its association with several neurodegenerative diseases. We implemented a chemical screening with this system as part of our efforts to repurpose approved drugs. While the screening was unsuccessful to identify modulators of TDP-43 toxicity, it revealed compounds capable of inhibiting the doxycyclinedependent TDP-43 expression. Furthermore, a complementary CRISPR/Cas9 screening using the same cell system identified additional regulators of doxycycline-dependent TDP43 expression. This investigation identifies new chemical and genetic modulators of the tetR system and highlights potential limitations of using this system for chemical or genetic screenings in mammalian cells. In Study II, our objective was to reposition compounds that could potentially reduce the toxic effects of a fragment of the Huntingtin (HTT) protein containing a 94 amino acid long glutamine stretch (Htt-Q94), a feature of Huntington's disease (HD). To achieve this, we carried out a high-throughput chemical screening using a varied collection of 1,214 drugs, largely sourced from a drug repurposing library. Through our screening process, we singled out clofazimine, an FDA-approved anti-leprosy drug, as a potential therapeutic candidate. Its effectiveness was validated across several in vitro models as well as a zebrafish model of polyglutamine (polyQ) toxicity. Employing a combination of computational analysis of transcriptional signatures, molecular modeling, and biochemical assays, we deduced that clofazimine is an agonist for the peroxisome proliferator-activated receptor gamma (PPARγ), a receptor previously suggested to be a viable therapeutic target for HD due to its role in promoting mitochondrial biogenesis. Notably, clofazimine was successful in alleviating the mitochondrial dysfunction triggered by the expression of Htt-Q94. These findings lend substantial support to the potential of clofazimine as a viable candidate for drug repurposing in the treatment of polyQ diseases. In Study III, we explored the molecular mechanism of a previously identified repurposing example, the use of diethyldithiocarbamate-copper complex (CuET), a disulfiram metabolite, for cancer treatment. We found CuET effectively inhibits cancer cell growth by targeting the NPL4 adapter of the p97VCP segregase, leading to translational arrest and stress in tumor cells. CuET also activates ribosomal biogenesis and autophagy in cancer cells, and its cytotoxicity can be enhanced by inhibiting these pathways. Thus, CuET shows promise as a cancer treatment, especially in combination therapies. In Study IV, we capitalized on the Molecular Signatures Database (MSigDB), one of the largest signature repositories, and drug transcriptomic profiles from the Connectivity Map (CMap) to construct a comprehensive and interactive drug-repurposing database called the Drug Repurposing Encyclopedia (DRE). Housing over 39.7 million pre-computed drugsignature associations across 20 species, the DRE allows users to conduct real-time drugrepurposing analysis. This can involve comparing user-supplied gene signatures with existing ones in the DRE, carrying out drug-gene set enrichment analyses (drug-GSEA) using submitted drug transcriptomic profiles, or conducting similarity analyses across all database signatures using user-provided gene sets. Overall, the DRE is an exhaustive database aimed at promoting drug repurposing based on transcriptional signatures, offering deep-dive comparisons across molecular signatures and species. Drug repurposing presents a valuable strategy for discovering fresh therapeutic applications for existing drugs, offering numerous benefits compared to conventional drug discovery methods. The studies conducted in this thesis underscore the potential of drug repurposing and highlight the complementary roles of computational and chemical approaches. These studies enhance our understanding of the mechanistic properties of repurposed drugs, such as clofazimine and disulfiram, and reveal novel mechanisms for targeting specific disease pathways. Additionally, the development of the DRE platform provides a comprehensive tool to support researchers in conducting drug-repositioning analyses, further facilitating the advancement of drug repurposing studies

Study of the intestinal microbiota in the HIV infection and the effect of a nutritional intervention

Introducción Desde la aplicación de la Terapia Antirretroviral de Gran Actividad (TARGA) la supervivencia de los sujetos infectados con el virus de la inmunodeficiencia humana (VIH) se ha incrementado considerablemente; sin embargo, su expectativa de vida es todavía 10 años menor a la de la media de la población. Esta reducción es debida a enfermedades no relacionadas con el síndrome de inmunodeficiencia adquirida (SIDA). Entre las principales causantes de este descenso se encuentra el aumento de la incidencia de problemas cardiovasculares. Recientemente, se ha descrito que la activación inmune persistente y la inflamación crónica son dos factores implicados en la morbimortalidad de estos pacientes. Un acontecimiento central en la fisiopatología del VIH es la destrucción, en fases muy tempranas de la infección, de los linfocitos Th17 del tejido linfoide asociado a mucosa (TLAM), órgano en el que residen alrededor del 90% del acervo de linfocitos totales. Junto al daño del TLAM tiene lugar apoptosis de las células epiteliales y la pérdida de la integridad de la mucosa que conduce a una translocación bacteriana anormal que se cree responsable de la inmunoactivación sistémica observada en los sujetos con VIH. Fisiológicamente las células Th17 juegan un papel crucial en la defensa frente a la translocación bacteriana, ya que estimulan la proliferación de células epiteliales y la expresión de defensinas antibacterianas, al mismo tiempo que promueven la quimiotaxis de neutrófilos hacia el TLAM para eliminar los productos bacterianos. Por tanto, la pérdida masiva de linfocitos Th17 determina probablemente el aumento de translocación bacteriana observado en la infección por VIH. Recientemente, por otro lado, se ha observado que el cambio en la composición de la microbiota intestinal asociada a la infección por el VIH está relacionada con la pérdida del TLAM, lo que podría, a su vez, ser una de las principales causas de la inflamación sistémica. Objetivos El objetivo principal de la presente tesis es caracterizar la comunidad microbioma intestinal disbiótica tanto en su composición como en su función, así como estudiar su efecto en la immunoactivación sistémica. Como objetivo secundario, abordaremos el estudio del efecto de una intervención nutricional dirigida a modificar la composición bacteriana intestinal hacia una comunidad menos inflamatoria. Materiales y métodos Para estudiar el efecto de la infección del VIH sobre la microbiota intestinal humana, se reclutó una corte de sujetos HIV+ sin TARGA, sujetos HIV+ con respuesta positiva al TARGA, sujetos HIV+ con respuesta negativa al TARGA y sujetos sanos como controles. Adicionalmente, se realizó una intervención nutricional de seis semanas de 5 g de galacto-oligosacáridos de cadena corta (Purimune®), 10 g de fructooligosacáridos de cadena larga (Orafti-HP® y Actilight®) y 5 g de glutamina (Nutrición Médica®) y placebo (20 g de maltodextrina). De cada integrante de la cohorte se recogieron muestras fecales y sanguíneas, antes y después de la intervención nutricional, con el fin de poder caracterizar la microbiota intestinal, así como medir marcadores de activación inmune, inflamación sistémica y translocación bacteriana. La microbiota intestinal se caracterizó recurriendo a técnicas de metagenómica, metatranscriptómica y meta-metabolómica. Para cada una de las muestras sanguíneas se obtuvieron distintos marcadores de activación inmune sistémica innata, adaptativa, translocación bacteriana, así como un análisis completo de la química sanguínea de cada uno de los participantes de la cohorte. La medición de dichos marcadores se llevó a cabo en colaboración con el Hospital Universitario Virgen del Rocío (Sevilla) y el Departamento de Enfermedades Infecciosas del Hospital Universitario Ramón y Cajal (Madrid) De las muestras fecales se extrajo el ADN y el ARN bacterianos para posteriormente poder secuenciarlo por medio de la combinación de las tecnologías de pirosecuenciación (Roche GS FLX y química de Titanium) y secuenciación de cadenas pareadas de Illumnina (Miseq química V3). Del ADN bacteriano se amplificó el gen ribosómico 16S con el fin de poder llevar a cabo luego la caracterización taxonómica de la comunidad bacteriana correspondiente. La secuenciación masiva fue realizada en el área de Genómica y Salud de la Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO) En colaboración con el Centro de Metabolómica y Bioanálisis (CEMBIO de la, Universidad CEU San Pablo (Madrid, España), obtuvimos el perfil metabólico de las comunidades bacterianas residentes del intestino humano. Los meta-metabolomas se determinaron por medio de la extracción de los metabolitos totales para, posteriormente, separarlos mediante la aplicación de una cromatografía líquida de alta eficacia acoplada a un espectrómetro de masas ESI-QTOF. Los metabolitos fueron identificados por medio del servidor web MELTIN, las bases de datos Lipidomics Gateway database y KEGG. Del mismo modo, en colaboración con el Departamento de Tecnología Alimentaria del Centro Agrotécnico, Universidad de Lleida (España), obtuvimos las mediciones de los ácidos grasos de cadena corta (AGCC). La anotación funcional de las secuencias de metagenomas se realizó mediante el ensamblaje de los metagenomas, recurriendo al programa Ray-Meta. La predicción de los marcos de lectura abiertos (MLA) la determinamos recurriendo al programa MetaGeneMark. De forma similar, los metatranscriptomas se ensamblaron recurriendo al programa Trinity y sus MLA se predijeron por el programa TransDecoder.LongOrfs. A partir de los MLA de los metagenomas y de los metatranscriptomas creamos una base de datos no redundante de MLA aplicando el programa USEARCH. Esta base de datos se comparó, recurriendo al programa rapsearch2 con la base de datos funcionales KEGG y la base de datos de resistencias a antibióticos CARD. La cuantificación de cada MLA se llevó a cabo de forma separada para metagenomas y metatranscriptomas, recurriendo en el primer caso al programa soap.coverage, y el RSEM para el segundo. La anotación taxonómica de las secuencias se realizó utilizando el mapeo de las secuencias de los metagenomas y metatranscriptomas contra una base de datos no redundante de genomas de referencia de especies residentes del microbioma humano. Para el caso de las secuencias de los amplicones del gen ribosómico 16S recurrimos al paquete de programas de análisis ecológico Qiime. La identificación de los biomarcadores relativos a las condiciones HIV+ o HIV-, así como los subgrupos correspondientes de los HIV+, se identificaron por medio del programa LEfSe, de forma indistinta para las anotaciones taxonómicas y funcionales. Recurrimos a Qiime y el programa de análisis estadístico R (v.3.3) para llevar a cabo análisis de diversidad alfa y beta. Así mismo, el programa de análisis estadístico R se utilizó para realizar los análisis de correlación, regresión lineal, pruebas de rango, los modelos generalizados lineales, las redes bayesianas, las redes ecológicas y funcionales, así como el resto de los análisis estadísticos de la presente tesis. Resultados y discusión La alteración de la mucosa intestinal provocada por el VIH genera una disbiosis que se detecta a partir de la comparación de los datos provenientes de la metagenómica, la metatranscriptómica y la meta-metabolómica en las diferentes cohortes del estudio. Del mismo modo se observa una correlación con las variables clínicas de activación inmune e inflamación sistémica. La disbiosis se caracteriza por el incremento de bacterias Gram-negativas capaces de resistir el estrés oxidativo generado por la inflamación intestinal. Varios compuestos metabólicos de esta comunidad bacteriana, tales como los lipopolisacáridos de membrana, son potentes activadores de la respuesta inmune e inflamatoria. Por otro lado, varias especies bacterianas conocidas por tener un papel antinflamatorio o ser importantes productoras de AGCC ven disminuida su abundancia en el microbioma asociado a los pacientes infectados por el VIH. La pérdida de dichas especies se ve reflejada en una reducción de la concentración de los AGCC en el intestino. Los AGCC, en especial el ácido butírico, poseen efectos beneficiosos sobre la salud del hospedador. Estos ayudan a la producción de mucina, la integridad de las uniones ocluyentes, la diferenciación de las células T reguladoras y la regeneración de la barrera epitelial. La disbiosis asociada al VIH no se ve aminorada por el uso de TARGA y la incorporación del prebiótico mostró un efecto moderado en la mayoría de los participantes. Sin embargo, dicho efecto fue más notorio en los individuos del grupo HIV+ sin TARGA. La administración del prebiótico mostró un incremento en la abundancia de especies productoras de ácido butírico junto con una mayor producción de este ácido graso. Este incremento correlacionaba con una reducción en la translocación bacteriana y la reducción de los marcadores de inflamación sistémica. El análisis de redes reveló que la comunidad bacteriana asociada al VIH muestra propiedades de "red biológica". Esto implica que esta comunidad disbiótica es capaz de conformar una comunidad estable que está asociada al deterioro de la salud del paciente. Adicionalmente, las especies y genes que se han enriquecido o perdido en la comunidad bacteriana intestinal asociada al VIH son componentes fundamentales que mantienen la estructura de las redes ecológicas y metabólicas correspondiente. Así, las especies que están sobrerrepresentadas en la condición del VIH influyen fuertemente en el resto de la comunidad de bacterias. Por otro lado, la infección del VIH causa cambios dramáticos en la estructura metabólica del microbioma intestinal perdiendo y ganando importantes enzimas metabólicas. Aunque se deben realizar estudios longitudinales adicionales, así como incrementar el tamaño muestral de participantes, en la presente tesis mostramos en forma holística el papel fundamental que el microbioma intestinal tiene en la patogénesis de la infección por el VIH. Más importante aún, proponemos, por un lado, que la microbiota puede ser objeto de intervención clínica en pacientes infectados con el VIH y, por otro, sugerimos posibles candidatos bacterianos probióticos capaces de dar respuestas viables en las correspondientes intervenciones.The use of ART in HIV+ subjects has increased considerably the life expectancy restoring the CD4+ T-cell counts and maintaining at low levels the viral load. However, their life expectancy is 10 years lower than the average population. This reduction is given due to unrelated AIDS illness, such as cardiovascular diseases and atherosclerosis that are caused by persistent immune activation and chronic inflammation. A possible explanation for this phenomenon is a constant bacterial translocation from the intestinal lumen to the systemic circulation given a prior disruption of the GALT. Moreover, the loss of the lymphoid tissue leads to a microbial imbalance that could be related to the systemic immune activation. In the present thesis, we describe in a holistic way the fundamental role of the microbiome in the pathogenesis of HIV infection. Here we present the results of a cross-sectional study of a cohort of three different HIV-infected groups of subjects (with a different response to the ART) and controls target to understand the alterations of the gut-microbiome given the HIV infection. The microbiome was characterized implementing different “omic” technologies and the impact on the host health was determined based on measuring clinical data related to the immune response and the bacterial translocation. Finally, a pilot study based on dietary supplementation with prebiotics and glutamine was carried out with the aim of ameliorating the HIV-associated dysbiosis. The HIV infection causes a disruption of the GALT leading the dysbiosis of the microbial community that cannot be restored by the ART. Moreover, the infection time would affect the diversity of the microbiota and the ecosystem stability. This dysbiotic community is enriched in Gram-negative species which are adapted to the inflammatory environment of the gut produced by HIV infection and produces pro-inflammatory metabolites which trigger the systemic immune activation and inflammation. Moreover, the HIV-dysbiosis is depleted for SCFA producer species and in the expression of genes related to anti-inflammatory metabolic pathways such as butanoate metabolism, propanoate metabolism or fatty acid metabolism. The prebiotic has an effect on a community whose original configuration is receptive to the nutritional intervention; this is related to the time exposure to HIV infection. The prebiotic intervention increases the butyrate levels by means of the increase of SCFA-producer species such as Faecalibacterium sp. The increment of the levels of the butyrate is related to the decrease of the bacterial translocation and systemic inflammation. Finally, we show that the dysbiotic-community is able to establish a stable-community which is associated with the deterioration of the patient's health. More importantly, we suggest that the microbiota may be a new target for clinical interventions in patients infected with HIV and proposed putative candidates for been viable targets for such interventions

Topology Reconstruction of Dynamical Networks via Constrained Lyapunov Equations

The network structure (or topology) of a dynamical network is often unavailable or uncertain. Hence, we consider the problem of network reconstruction. Network reconstruction aims at inferring the topology of a dynamical network using measurements obtained from the network. In this technical note we define the notion of solvability of the network reconstruction problem. Subsequently, we provide necessary and sufficient conditions under which the network reconstruction problem is solvable. Finally, using constrained Lyapunov equations, we establish novel network reconstruction algorithms, applicable to general dynamical networks. We also provide specialized algorithms for specific network dynamics, such as the well-known consensus and adjacency dynamics.Comment: 8 page