305,330 research outputs found
A data fusion approach for learning transcriptional Bayesian networks in chronic leukemia
No full textLa crescente disponibilità di dati omici ha determinato un importante cambiamento nel paradigma della ricerca scientifica, passando da uno studio “contesto specifico” focalizzato su un singolo aspetto biologico, ad un studio su larga scala guidato dai dati. L’analisi simultanea di diversi livelli omici potrebbe aiutare a chiarire la relazione tra caratteristiche o perturbazioni del sistema molecolare non rilevate in precedenza con un fenotipo specifico, specialmente nel caso di malattie complesse, come il cancro. A tal fine, un approccio computazionale integrativo in grado di gestire l'eterogeneità dei dati e la complessità biologica può consentire un'indagine approfondita di programmi di espressione genica disregolati responsabili dei meccanismi di insorgenza e di progressione della malattia. La ricostruzione dei pattern regolatori dei fattori determinanti della trascrizione (fattori di trascrizione, TF), che presiedono allo schema di espressione genica, potrebbe anche aiutare a ottenere informazioni sulle firme molecolari che guidano i fenotipi della malattia, offrendo così nuove ipotesi di ricerca.
In questa tesi è stato sviluppato un approccio di “data fusion”, incentrato sull'integrazione a più livelli di dati omici per la modellizzazione di background trascrizionali su larga scala. La sua strategia di ricerca combina efficacemente un approccio network-centrico per ricostruire l'interattoma trascrizionale con la modellizzazione offerta dalla teoria Bayesiana, ed è in grado di indagare probabilisticamente, su scala genomica, le regolazioni trascrizionali e le sottostanti firme molecolari.
Questo lavoro di ricerca fa parte del progetto "Rete Ematologica Lombarda (REL) cluster biotecnologico per l'implementazione dell'analisi genomica e lo sviluppo di trattamenti innovativi nelle neoplasie ematologiche", che mira a stabilire un centro di riferimento per lo studio delle neoplasie ematologiche, con particolare attenzione alle neoplasie mieloidi.
La metodologia proposta è stata infatti applicata ad un tipo di patologia mieloide, la leucemia mieloide cronica (LMC), di cui è noto l’evento genetico causale, ma l’alterato ruolo trascrizionale alla base della progressione della malattia non è stato ancora approfondito a livello genomico.The increasing availability of omics data has caused an important paradigmatic shift in scientific research from case-based studies towards large scale data-driven research. The simultaneous interrogation of different omics levels, could help to elucidate the interrelation of previously-undetected system features or perturbations with a specific phenotype, especially in complex diseases, such as cancer. To this aim, an integrative computational approach able to deal with data heterogeneity and biological complexity may allow a deep investigation of dysregulated gene expression programs responsible of disease onset and progression mechanisms. The reconstruction of transcriptional determinants (transcription factors, TFs) regulatory patterns, which preside over the gene expression scheme could also help to gain insights into molecular signatures driving disease phenotypes, offering new research hypotheses.
In this thesis, I have developed a data fusion approach focused on “multi-layered” omics data integration for modeling large-scale transcriptional background. Its framework efficiently combines a network-centric approach to reconstruct the transcriptional interactome to probabilistically inspect, on a genome-wide scale, the transcriptional regulations and the underlying regulative signatures.
This work is part of the project “Rete Ematologica Lombarda (REL) biotechnology cluster for the implementation of genomic analysis and the development of innovative treatments in hematological malignancies”, which aims at establishing a reference center for the study of hematological malignancies, with focus on myeloid neoplasms.
The proposed methodology has been applied to the case of a myeloid disorder, the Chronic Myeloid Leukemia (CML), whose causative genetic event is known but its emerging transcriptional altered role in disease progression has not yet been deeply investigated at a genomic level
A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks
No full textBackground: Reverse engineering of transcriptional regulatory networks (TRN) from genomics data has always represented a computational challenge in System Biology. The major issue is modeling the complex crosstalk among transcription factors (TFs) and their target genes, with a method able to handle both the high number of interacting variables and the noise in the available heterogeneous experimental sources of information. Results: In this work, we propose a data fusion approach that exploits the integration of complementary omics-data as prior knowledge within a Bayesian framework, in order to learn and model large-scale transcriptional networks. We develop a hybrid structure-learning algorithm able to jointly combine TFs ChIP-Sequencing data and gene expression compendia to reconstruct TRNs in a genome-wide perspective. Applying our method to high-throughput data, we verified its ability to deal with the complexity of a genomic TRN, providing a snapshot of the synergistic TFs regulatory activity. Given the noisy nature of data-driven prior knowledge, which potentially contains incorrect information, we also tested the method's robustness to false priors on a benchmark dataset, comparing the proposed approach to other regulatory network reconstruction algorithms. We demonstrated the effectiveness of our framework by evaluating structural commonalities of our learned genomic network with other existing networks inferred by different DNA binding information-based methods. Conclusions: This Bayesian omics-data fusion based methodology allows to gain a genome-wide picture of the transcriptional interplay, helping to unravel key hierarchical transcriptional interactions, which could be subsequently investigated, and it represents a promising learning approach suitable for multi-layered genomic data integration, given its robustness to noisy sources and its tailored framework for handling high dimensional data
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Data fusion approach for learning transcriptional Bayesian networks
No full textThe complexity of gene expression regulation relies on the synergic nature underlying the molecular interplay among its principal actors, transcription factors (TFs). Exerting a spatiotemporal control on their target genes, they define transcriptional programs across the genome, which are strongly perturbed in a disease context. In order to gain a more comprehensive picture of these complex dynamics, a data fusion approach, aimed at performing the integration of heterogeneous -omics data is fundamental. Bayesian Networks provide a natural framework for integrating different sources of data and knowledge through the priors’ use. In this work, we developed an hybrid structure-learning algorithm with the aim of exploiting TF ChIP-seq and gene expression (GE) data to investigate disease-specific transcriptional regulations in a genome-wide perspective. TF ChIP seq profiles were firstly used for structure learning and then integrated in the model as a prior probability. GE panels were employed to learn the model parameters, trying to find the best heuristic transcriptional network. We applied our approach to a specific pathological case, the chronic myeloid leukemia (CML), a myeloproliferative disorder, whose transcriptional mechanisms have not yet been deeply elucidated. The proposed data-driven method allows to investigate transcriptional signatures, highlighting in the obtained probabilistic network a three-layered hierarchy, as a different TFs influence on gene expression cellular programs
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Dissecting the role of lncRNAs in promoting Natural Killer cytotoxicity in Non-Small Cell Lung Cancer models
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Author, publisher and bookseller : a tripartite synergy in Nigerian book industry
No full textThis work is about the roles of Author, Publisher and Bookseller in Book development in
Nigeria. The paper started by delving into the history of Book Publishing in Nigeria after
which it proceeded by defining who an author, a publisher, and a bookseller is and
expatiated on the indispensable roles of these key actors in Nigerian Book Industry and in
the emerging Information Society. Furthermore, the various constraints to book
development were identified while the paper advised on how the Book Industry can be
further promoted in Nigeria. However, the paper concluded and made recommendations
on how the Book sector can help in enhancing scholarship in the country
Artificial intelligence and machine learning: just a hype or a new opportunity for pharmacometrics?
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