Pairwise gene GO-based measures for biclustering of high-dimensional expression data

Background: Biclustering algorithms search for groups of genes that share the same behavior under a subset of samples in gene expression data. Nowadays, the biological knowledge available in public repositories can be used to drive these algorithms to find biclusters composed of groups of genes functionally coherent. On the other hand, a distance among genes can be defined according to their information stored in Gene Ontology (GO). Gene pairwise GO semantic similarity measures report a value for each pair of genes which establishes their functional similarity. A scatter search-based algorithm that optimizes a merit function that integrates GO information is studied in this paper. This merit function uses a term that addresses the information through a GO measure. Results: The effect of two possible different gene pairwise GO measures on the performance of the algorithm is analyzed. Firstly, three well known yeast datasets with approximately one thousand of genes are studied. Secondly, a group of human datasets related to clinical data of cancer is also explored by the algorithm. Most of these data are high-dimensional datasets composed of a huge number of genes. The resultant biclusters reveal groups of genes linked by a same functionality when the search procedure is driven by one of the proposed GO measures. Furthermore, a qualitative biological study of a group of biclusters show their relevance from a cancer disease perspective. Conclusions: It can be concluded that the integration of biological information improves the performance of the biclustering process. The two different GO measures studied show an improvement in the results obtained for the yeast dataset. However, if datasets are composed of a huge number of genes, only one of them really improves the algorithm performance. This second case constitutes a clear option to explore interesting datasets from a clinical point of view.Ministerio de Economía y Competitividad TIN2014-55894-C2-

Multi-species integrative biclustering

We describe an algorithm, multi-species cMonkey, for the simultaneous biclustering of heterogeneous multiple-species data collections and apply the algorithm to a group of bacteria containing Bacillus subtilis, Bacillus anthracis, and Listeria monocytogenes. The algorithm reveals evolutionary insights into the surprisingly high degree of conservation of regulatory modules across these three species and allows data and insights from well-studied organisms to complement the analysis of related but less well studied organisms

User-Specific Bicluster-based Collaborative Filtering

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020Collaborative Filtering is one of the most popular and successful approaches for Recommender Systems. However, some challenges limit the effectiveness of Collaborative Filtering approaches when dealing with recommendation data, mainly due to the vast amounts of data and their sparse nature. In order to improve the scalability and performance of Collaborative Filtering approaches, several authors proposed successful approaches combining Collaborative Filtering with clustering techniques. In this work, we study the effectiveness of biclustering, an advanced clustering technique that groups rows and columns simultaneously, in Collaborative Filtering. When applied to the classic U-I interaction matrices, biclustering considers the duality relations between users and items, creating clusters of users who are similar under a particular group of items. We propose USBCF, a novel biclustering-based Collaborative Filtering approach that creates user specific models to improve the scalability of traditional CF approaches. Using a realworld dataset, we conduct a set of experiments to objectively evaluate the performance of the proposed approach, comparing it against baseline and state-of-the-art Collaborative Filtering methods. Our results show that the proposed approach can successfully suppress the main limitation of the previously proposed state-of-the-art biclustering-based Collaborative Filtering (BBCF) since BBCF can only output predictions for a small subset of the system users and item (lack of coverage). Moreover, USBCF produces rating predictions with quality comparable to the state-of-the-art approaches

Utilizing gene co-expression networks for comparative transcriptomic analyses

The development of high-throughput technologies such as microarray and next-generation RNA sequencing (RNA-seq) has generated numerous transcriptomic data that can be used for comparative transcriptomics studies. Transcriptomes obtained from different species can reveal differentially expressed genes that underlie species-specific traits. It also has the potential to identify genes that have conserved gene expression patterns. However, differential expression alone does not provide information about how the genes relate to each other in terms of gene expression or if groups of genes are correlated in similar ways across species, tissues, etc. This makes gene expression networks, such as co-expression networks, valuable in terms of finding similarities or differences between genes based on their relationships with other genes. The desired outcome of this research was to develop methods for comparative transcriptomics, specifically for comparing gene co-expression networks (GCNs), either within or between any set of organisms. These networks represent genes as nodes in the network, and pairs of genes may be connected by an edge representing the strength of the relationship between the pairs. We begin with a review of currently utilized techniques available that can be used or adapted to compare gene co-expression networks. We also work to systematically determine the appropriate number of samples needed to construct reproducible gene co-expression networks for comparison purposes. In order to systematically compare these replicate networks, software to visualize the relationship between replicate networks was created to determine when the consistency of the networks begins to plateau and if this is affected by factors such as tissue type and sample size. Finally, we developed a tool called Juxtapose that utilizes gene embedding to functionally interpret the commonalities and differences between a given set of co-expression networks constructed using transcriptome datasets from various organisms. A set of transcriptome datasets were utilized from publicly available sources as well as from collaborators. GTEx and Gene Expression Omnibus (GEO) RNA-seq datasets were used for the evaluation of the techniques proposed in this research. Skeletal cell datasets of closely related species and more evolutionarily distant organisms were also analyzed to investigate the evolutionary relationships of several skeletal cell types. We found evidence that data characteristics such as tissue origin, as well as the method used to construct gene co-expression networks, can substantially impact the number of samples required to generate reproducible networks. In particular, if a threshold is used to construct a gene co-expression network for downstream analyses, the number of samples used to construct the networks is an important consideration as many samples may be required to generate networks that have a reproducible edge order when sorted by edge weight. We also demonstrated the capabilities of our proposed method for comparing GCNs, Juxtapose, showing that it is capable of consistently matching up genes in identical networks, and it also reflects the similarity between different networks using cosine distance as a measure of gene similarity. Finally, we applied our proposed method to skeletal cell networks and find evidence of conserved gene relationships within skeletal GCNs from the same species and identify modules of genes with similar embeddings across species that are enriched for biological processes involved in cartilage and osteoblast development. Furthermore, smaller sub-networks of genes reflect the phylogenetic relationships of the species analyzed using our gene embedding strategy to compare the GCNs. This research has produced methodologies and tools that can be used for evolutionary studies and generalizable to scenarios other than cross-species comparisons, including co-expression network comparisons across tissues or conditions within the same species

Graphical Model approaches for Biclustering

In many scientific areas, it is crucial to group (cluster) a set of objects, based on a set of observed features. Such operation is widely known as Clustering and it has been exploited in the most different scenarios ranging from Economics to Biology passing through Psychology. Making a step forward, there exist contexts where it is crucial to group objects and simultaneously identify the features that allow to recognize such objects from the others. In gene expression analysis, for instance, the identification of subsets of genes showing a coherent pattern of expression in subsets of objects/samples can provide crucial information about active biological processes. Such information, which cannot be retrieved by classical clustering approaches, can be extracted with the so called Biclustering, a class of approaches which aim at simultaneously clustering both rows and columns of a given data matrix (where each row corresponds to a different object/sample and each column to a different feature). The problem of biclustering, also known as co-clustering, has been recently exploited in a wide range of scenarios such as Bioinformatics, market segmentation, data mining, text analysis and recommender systems. Many approaches have been proposed to address the biclustering problem, each one characterized by different properties such as interpretability, effectiveness or computational complexity. A recent trend involves the exploitation of sophisticated computational models (Graphical Models) to face the intrinsic complexity of biclustering, and to retrieve very accurate solutions. Graphical Models represent the decomposition of a global objective function to analyse in a set of smaller/local functions defined over a subset of variables. The advantages in using Graphical Models relies in the fact that the graphical representation can highlight useful hidden properties of the considered objective function, plus, the analysis of smaller local problems can be dealt with less computational effort. Due to the difficulties in obtaining a representative and solvable model, and since biclustering is a complex and challenging problem, there exist few promising approaches in literature based on Graphical models facing biclustering. 3 This thesis is inserted in the above mentioned scenario and it investigates the exploitation of Graphical Models to face the biclustering problem. We explored different type of Graphical Models, in particular: Factor Graphs and Bayesian Networks. We present three novel algorithms (with extensions) and evaluate such techniques using available benchmark datasets. All the models have been compared with the state-of-the-art competitors and the results show that Factor Graph approaches lead to solid and efficient solutions for dataset of contained dimensions, whereas Bayesian Networks can manage huge datasets, with the overcome that setting the parameters can be not trivial. As another contribution of the thesis, we widen the range of biclustering applications by studying the suitability of these approaches in some Computer Vision problems where biclustering has been never adopted before. Summarizing, with this thesis we provide evidence that Graphical Model techniques can have a significant impact in the biclustering scenario. Moreover, we demonstrate that biclustering techniques are ductile and can produce effective solutions in the most different fields of applications

Estatística em biologia molecular: o passado, o presente e o futuro

Vivemos na era mais mensurável da história. Na era do petabyte (1000 terabytes) o desafio não é mais o armazenamento de dados, é dar-lhes sentido. Sendo esta a era da revolução dos dados, a respetiva análise torna-se parte integrante de várias ciências. Por exemplo, a biologia molecular deixa de ser uma ciência onde os biólogos estudam um gene de cada vez, para passar a produzir milhares (agora milhões) de medições por amostra para analisar. Além disso, ao contrário da análise do ADN, que é estática, a análise da expressão genética é dinâmica, uma vez que nos vários tecidos expressam-se genes diferentes. O geneticista John Craig Venter, sequenciava organismos isolados, mas com o aparecimento de novas tecnologias e computadores com elevada capacidade de memória, que permitem a análise de dados bastante complexos, passou a estudar ecossistemas inteiros: sequenciação dos microorganismos do oceano, desde 2003, e do ar, desde 2005. A complexidade dos dados é ainda potenciada pelas novas tecnologias que, ao surgirem, são ainda pouco exploradas, produzindo dados com mais ruído dos que as anteriores. Esta complexidade e grau de variabilidade fazem com que a estatística seja um importante e inequívoco contributo na análise. Na realidade, o papel da estatística na biologia molecular vai além de uma mera intervenção. Trata-se de um pilar indissociável desta ciência! A estatística tem vindo a conquistar o seu espaço nesta nova área, tornando-se uma componente essencial de mérito reconhecido

A reinforcement learning recommender system using bi-clustering and Markov Decision Process

Collaborative filtering (CF) recommender systems are static in nature and does not adapt well with changing user preferences. User preferences may change after interaction with a system or after buying a product. Conventional CF clustering algorithms only identifies the distribution of patterns and hidden correlations globally. However, the impossibility of discovering local patterns by these algorithms, headed to the popularization of bi-clustering algorithms. Bi-clustering algorithms can analyze all dataset dimensions simultaneously and consequently, discover local patterns that deliver a better understanding of the underlying hidden correlations. In this paper, we modelled the recommendation problem as a sequential decision-making problem using Markov Decision Processes (MDP). To perform state representation for MDP, we first converted user-item votings matrix to a binary matrix. Then we performed bi-clustering on this binary matrix to determine a subset of similar rows and columns. A bi-cluster merging algorithm is designed to merge similar and overlapping bi-clusters. These bi-clusters are then mapped to a squared grid (SG). RL is applied on this SG to determine best policy to give recommendation to users. Start state is determined using Improved Triangle Similarity (ITR similarity measure. Reward function is computed as grid state overlapping in terms of users and items in current and prospective next state. A thorough comparative analysis was conducted, encompassing a diverse array of methodologies, including RL-based, pure Collaborative Filtering (CF), and clustering methods. The results demonstrate that our proposed method outperforms its competitors in terms of precision, recall, and optimal policy learning

Biclustering: Methods, Software and Application

Over the past 10 years, biclustering has become popular not only in the field of biological data analysis but also in other applications with high-dimensional two way datasets. This technique clusters both rows and columns simultaneously, as opposed to clustering only rows or only columns. Biclustering retrieves subgroups of objects that are similar in one subgroup of variables and different in the remaining variables. This dissertation focuses on improving and advancing biclustering methods. Since most existing methods are extremely sensitive to variations in parameters and data, we developed an ensemble method to overcome these limitations. It is possible to retrieve more stable and reliable bicluster in two ways: either by running algorithms with different parameter settings or by running them on sub- or bootstrap samples of the data and combining the results. To this end, we designed a software package containing a collection of bicluster algorithms for different clustering tasks and data scales, developed several new ways of visualizing bicluster solutions, and adapted traditional cluster validation indices (e.g. Jaccard index) for validating the bicluster framework. Finally, we applied biclustering to marketing data. Well-established algorithms were adjusted to slightly different data situations, and a new method specially adapted to ordinal data was developed. In order to test this method on artificial data, we generated correlated original random values. This dissertation introduces two methods for generating such values given a probability vector and a correlation structure. All the methods outlined in this dissertation are freely available in the R packages biclust and orddata. Numerous examples in this work illustrate how to use the methods and software.In den letzten 10 Jahren wurde das Biclustern vor allem auf dem Gebiet der biologischen Datenanalyse, jedoch auch in allen Bereichen mit hochdimensionalen Daten immer populärer. Unter Biclustering versteht man das simultane Clustern von 2-Wege-Daten, um Teilmengen von Objekten zu finden, die sich zu Teilmengen von Variablen ähnlich verhalten. Diese Arbeit beschäftigt sich mit der Weiterentwicklung und Optimierung von Biclusterverfahren. Neben der Entwicklung eines Softwarepaketes zur Berechnung, Aufarbeitung und graphischen Darstellung von Bicluster Ergebnissen wurde eine Ensemble Methode für Bicluster Algorithmen entwickelt. Da die meisten Algorithmen sehr anfällig auf kleine Veränderungen der Startparameter sind, können so robustere Ergebnisse erzielt werden. Die neue Methode schließt auch das Zusammenfügen von Bicluster Ergebnissen auf Subsample- und Bootstrap-Stichproben mit ein. Zur Validierung der Ergebnisse wurden auch bestehende Maße des traditionellen Clusterings (z.B. Jaccard Index) für das Biclustering adaptiert und neue graphische Mittel für die Interpretation der Ergebnisse entwickelt. Ein weiterer Teil der Arbeit beschäftigt sich mit der Anwendung von Bicluster Algorithmen auf Daten aus dem Marketing Bereich. Dazu mussten bestehende Algorithmen verändert und auch ein neuer Algorithmus speziell für ordinale Daten entwickelt werden. Um das Testen dieser Methoden auf künstlichen Daten zu ermöglichen, beinhaltet die Arbeit auch die Ausarbeitung eines Verfahrens zur Ziehung ordinaler Zufallszahlen mit vorgegebenen Wahrscheinlichkeiten und Korrelationsstruktur. Die in der Arbeit vorgestellten Methoden stehen durch die beiden R Pakete biclust und orddata allgemein zur Verfügung. Die Nutzbarkeit wird in der Arbeit durch zahlreiche Beispiele aufgezeigt

Multi-layered model of individual HIV infection progression and mechanisms of phenotypical expression

Cite as: Perrin, Dimitri (2008) Multi-layered model of individual HIV infection progression and mechanisms of phenotypical expression. PhD thesis, Dublin City University

Non-canonical Kinases and Substrates in Cancer Progression

This book is a printed edition of the Special Issue "Non-canonical Kinases and Substrates in Cancer Progression" that was published in the scientific journal Cancers. It was edited by Francisco M. Vega, Ph.D. from the University of Seville in Spain. It brings together the latest views and original research on non-canonical protein kinases, substrates, and scaffold