Biclustering fMRI time series

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020Biclustering é um método de análise que procura gerar clusters tendo em conta simultaneamente as linhas e as colunas de uma matriz de dados. Este método tem sido vastamente explorado em análise de dados genéticos. Apesar de diversos estudos reconhecerem as capacidades deste método de análise em outras áreas de investigação, as últimas duas décadas tem sido marcadas por um número elevado de estudos aplicados em dados genéticos e pela ausência de uma linha de investigação que explore as capacidades de biclustering fora desta área tradicional Esta tese segue pistas que sugerem potencial no uso de biclustering em dados de natureza espaço-temporal. Considerando o contexto particular das neurociências, esta tese explora as capacidades dos algoritmos de biclustering em extrair conhecimento das séries temporais geradas por técnicas de imagem por ressonância magnética funcional (fMRI). Eta tese propõe uma metodologia para avaliar a capacidade de algoritmos de biclustering em estudar dados fMRI, considerando tanto dados sintéticos como dados reais. Para avaliar estes algoritmos, usamos métricas de avaliação interna. Os nossos resultados discutem o uso de diversas estratégias de busca, revelando a superioridade de estratégias exaustivos para obter os biclusters mais homogéneos. No entanto, o elevado custo computacional de estratégias exaustivas ainda são um desafio e é necessário pesquisa adicional para a busca eficiente de biclusters no contexto de análise de dados fMRI. Propomos adicionalmente uma nova metodologia de análise de biclusters baseada em algoritmos de descoberta de padrões para determinar os padrões mais frequentes presentes nas soluções de biclustering geradas. Um bicluster não é mais que um hipervértice num hipergrafo . Extrair padrões frequentes numa solução de biclustering implica extrair os hipervértices mais significativos. Numa primeira abordagem, isto permite entender relações entre regiões do cérebro e traçar perfis temporais que métodos tradicionais de estudos de correlação não são capazes de detetar. Adicionalmente, o processo de gerar os biclusters permite filtrar ligações pouco interessantes, permitindo potencialmente gerar hipergrafos de forma eficiente. A questão final é o que podemos fazer com este conhecimento. Conhecer a relação entre regiões do cérebro é o objetivo central das neurociências. Entender as ligações entre regiões do cérebro para vários sujeitos permitem traçar perfis. Nesse caso, propomos uma metodologia para extrapolar biclusters para dados tridimensionais e efetuar triclustering. Adicionalmente, entender a ligação entre zonas cerebrais permite identificar doenças como a esquizofrenia, demência ou o Alzheimer. Este trabalho aponta caminhos para o uso de biclustering na análise de dados espaço-temporais, em particular em neurociências. A metodologia de avaliação proposta mostra evidências da eficácia do biclustering para encontrar padrões locais em dados de fMRI, embora mais trabalhos sejam necessários em relação à escalabilidade para promover a aplicação em cenários reais.The effectiveness of biclustering, simultaneous clustering of both rows and columns in a data matrix, has been primarily shown in gene expression data analysis. Furthermore, several researchers recognize its potentialities in other research areas. Nevertheless, the last two decades witnessed many biclustering algorithms targeting gene expression data analysis and a lack of consistent studies exploring the capacities of biclustering outside this traditional application domain. Following hints that suggest potentialities for biclustering on Spatiotemporal data, particularly in neurosciences, this thesis explores biclustering’s capacity to extract knowledge from fMRI time series. This thesis proposes a methodology to evaluate biclustering algorithms’ feasibility to study the fMRI signal, considering both synthetic and realworld fMRI datasets. In the absence of ground truth to compare bicluster solutions with a reference one, we used internal valuation metrics. Results discussing the use of different search strategies showed the superiority of exhaustive approaches, obtaining the most homogeneous biclusters. However, their high computational cost is still a challenge, and further work is needed for the efficient use of biclustering in fMRI data analysis. We propose a new methodology for analyzing biclusters based on performing pattern mining algorithms to determine the most frequent patterns present in the generated biclustering solutions. A bicluster is nothing more than a hyperlink in a hypergraph. Extracting frequent patterns in a biclustering solution implies extracting the most significant hyperlinks. In a first approach, this allows to understand relationships between regions of the brain and draw temporal profiles that traditional methods of correlation studies cannot detect. Additionally, the process of generating biclusters allows filtering uninteresting links, potentially allowing to generate hypergraphs efficiently. The final question is, what can we do with this knowledge. Knowing the relationship between brain regions is the central objective of neurosciences. Understanding the connections between regions of the brain for various subjects allows one to draw profiles. In this case, we propose a methodology to extrapolate biclusters to threedimensional data and perform triclustering. Additionally, understanding the link between brain zones allows identifying diseases like schizophrenia, dementia, or Alzheimer’s. This work pinpoints avenues for the use of biclustering in Spatiotemporal data analysis, in particular neurosciences applications. The proposed evaluation methodology showed evidence of biclustering’s effectiveness in finding local fMRI data patterns, although further work is needed regarding scalability to promote the application in real scenarios

Bayesian nonparametric clusterings in relational and high-dimensional settings with applications in bioinformatics.

Recent advances in high throughput methodologies offer researchers the ability to understand complex systems via high dimensional and multi-relational data. One example is the realm of molecular biology where disparate data (such as gene sequence, gene expression, and interaction information) are available for various snapshots of biological systems. This type of high dimensional and multirelational data allows for unprecedented detailed analysis, but also presents challenges in accounting for all the variability. High dimensional data often has a multitude of underlying relationships, each represented by a separate clustering structure, where the number of structures is typically unknown a priori. To address the challenges faced by traditional clustering methods on high dimensional and multirelational data, we developed three feature selection and cross-clustering methods: 1) infinite relational model with feature selection (FIRM) which incorporates the rich information of multirelational data; 2) Bayesian Hierarchical Cross-Clustering (BHCC), a deterministic approximation to Cross Dirichlet Process mixture (CDPM) and to cross-clustering; and 3) randomized approximation (RBHCC), based on a truncated hierarchy. An extension of BHCC, Bayesian Congruence Measuring (BCM), is proposed to measure incongruence between genes and to identify sets of congruent loci with identical evolutionary histories. We adapt our BHCC algorithm to the inference of BCM, where the intended structure of each view (congruent loci) represents consistent evolutionary processes. We consider an application of FIRM on categorizing mRNA and microRNA. The model uses latent structures to encode the expression pattern and the gene ontology annotations. We also apply FIRM to recover the categories of ligands and proteins, and to predict unknown drug-target interactions, where latent categorization structure encodes drug-target interaction, chemical compound similarity, and amino acid sequence similarity. BHCC and RBHCC are shown to have improved predictive performance (both in terms of cluster membership and missing value prediction) compared to traditional clustering methods. Our results suggest that these novel approaches to integrating multi-relational information have a promising future in the biological sciences where incorporating data related to varying features is often regarded as a daunting task

Anthropometry: An R Package for Analysis of Anthropometric Data

The development of powerful new 3D scanning techniques has enabled the generation of large up-to-date anthropometric databases which provide highly valued data to improve the ergonomic design of products adapted to the user population. As a consequence, Ergonomics and Anthropometry are two increasingly quantitative fields, so advanced statistical methodologies and modern software tools are required to get the maximum benefit from anthropometric data. This paper presents a new R package, called Anthropometry, which is available on the Comprehensive R Archive Network. It brings together some statistical methodologies concerning clustering, statistical shape analysis, statistical archetypal analysis and the statistical concept of data depth, which have been especially developed to deal with anthropometric data. They are proposed with the aim of providing effective solutions to some common anthropometric problems, such as clothing design or workstation design (focusing on the particular case of aircraft cockpits). The utility of the package is shown by analyzing the anthropometric data obtained from a survey of the Spanish female population performed in 2006 and from the 1967 United States Air Force survey. This manuscript is also contained in Anthropometry as a vignette

A bioinformatics potpourri

© 2018 The Author(s). The 16th International Conference on Bioinformatics (InCoB) was held at Tsinghua University, Shenzhen from September 20 to 22, 2017. The annual conference of the Asia-Pacific Bioinformatics Network featured six keynotes, two invited talks, a panel discussion on big data driven bioinformatics and precision medicine, and 66 oral presentations of accepted research articles or posters. Fifty-seven articles comprising a topic assortment of algorithms, biomolecular networks, cancer and disease informatics, drug-target interactions and drug efficacy, gene regulation and expression, imaging, immunoinformatics, metagenomics, next generation sequencing for genomics and transcriptomics, ontologies, post-translational modification, and structural bioinformatics are the subject of this editorial for the InCoB2017 supplement issues in BMC Genomics, BMC Bioinformatics, BMC Systems Biology and BMC Medical Genomics. New Delhi will be the location of InCoB2018, scheduled for September 26-28, 2018

Social Vulnerability and How It Matters: A Bibliometric Analysis

Interdisciplinary and cross-cultural studies of the impacts of environment and social vulnerability must be undertaken to address the problem of social vulnerability in the foreseeable future. Scientist or social scientists should first continuously strive towards approaches can integrate municipal technological expertise, experiences, knowledge, perceptions, and expectations into emergency circumstances, so that people can be sharper on issues and offer responses with their matters. In this paper. We performing the Bibliometric Analysis to review published papers on the keyword 'Social Vulnerability'. There are 29,468 papers published in the last 52 years from 1969 to November 2020. Disaster research by implementing the Internet of Things (IoT), data mining, machine learning is still needed

Fouille de données complexes et biclustering avec l'analyse formelle de concepts

Knowledge discovery in database (KDD) is a process which is applied to possibly large volumes of data for discovering patterns which can be significant and useful. In this thesis, we are interested in data transformation and data mining in knowledge discovery applied to complex data, and we present several experiments related to different approaches and different data types.The first part of this thesis focuses on the task of biclustering using formal concept analysis (FCA) and pattern structures. FCA is naturally related to biclustering, where the objective is to simultaneously group rows and columns which verify some regularities. Related to FCA, pattern structures are its generalizations which work on more complex data. Partition pattern structures were proposed to discover constant-column biclustering, while interval pattern structures were studied in similar-column biclustering. Here we extend these approaches to enumerate other types of biclusters: additive, multiplicative, order-preserving, and coherent-sign-changes.The second part of this thesis focuses on two experiments in mining complex data. First, we present a contribution related to the CrossCult project, where we analyze a dataset of visitor trajectories in a museum. We apply sequence clustering and FCA-based sequential pattern mining to discover patterns in the dataset and to classify these trajectories. This analysis can be used within CrossCult project to build recommendation systems for future visitors. Second, we present our work related to the task of antibacterial drug discovery. The dataset for this task is generally a numerical matrix with molecules as rows and features/attributes as columns. The huge number of features makes it more complex for any classifier to perform molecule classification. Here we study a feature selection approach based on log-linear analysis which discovers associations among features.As a synthesis, this thesis presents a series of different experiments in the mining of complex real-world data.L'extraction de connaissances dans les bases de données (ECBD) est un processus qui s'applique à de (potentiellement larges) volumes de données pour découvrir des motifs qui peuvent être signifiants et utiles. Dans cette thèse, on s'intéresse à deux étapes du processus d'ECBD, la transformation et la fouille, que nous appliquons à des données complexes. Nous présentons de nombreuses expérimentations s'appuyant sur des approches et des types de données variés.La première partie de cette thèse s'intéresse à la tâche de biclustering en s'appuyant sur l'analyse formelle de concepts (FCA) et aux pattern structures. FCA est naturellement liées au biclustering, dont l'objectif consiste à grouper simultanément un ensemble de lignes et de colonnes qui vérifient certaines régularités. Les pattern structures sont une généralisation de la FCA qui permet de travailler avec des données plus complexes. Les "partition pattern structures'' ont été proposées pour du biclustering à colonnes constantes tandis que les "interval pattern structures'' ont été étudiées pour du biclustering à colonnes similaires. Nous proposons ici d'étendre ces approches afin d'énumérer d'autres types de biclusters : additif, multiplicatif, préservant l'ordre, et changement de signes cohérents.Dans la seconde partie, nous nous intéressons à deux expériences de fouille de données complexes. Premièrement, nous présentons une contribution dans la quelle nous analysons les trajectoires des visiteurs d'un musée dans le cadre du projet CrossCult. Nous utilisons du clustering de séquences et de la fouille de motifs séquentiels basée sur l'analyse formelle de concepts pour découvrir des motifs dans les données et classifier les trajectoires. Cette analyse peut ensuite être exploitée par un système de recommandation pour les futurs visiteurs. Deuxièmement, nous présentons un travail sur la découverte de médicaments antibactériens. Les jeux de données pour cette tâche, généralement des matrices numériques, décrivent des molécules par un certain nombre de variables/attributs. Le grand nombre de variables complexifie la classification des molécules par les classifieurs. Ici, nous étudions une approche de sélection de variables basée sur l'analyse log-linéaire qui découvre des associations entre variables.En somme, cette thèse présente différentes expériences de fouille de données réelles et complexes

Bibliometric Studies and Worldwide Research Trends on Global Health

Global health, conceived as a discipline, aims to train, research and respond to problems of a transboundary nature, in order to improve health and health equity at the global level. The current worldwide situation is ruled by globalization, and therefore the concept of global health involves not only health-related issues, but also those related to the environment and climate change. Therefore, in this Special Issue, the problems related to global health have been addressed from a bibliometric approach in four main areas: environmental issues, diseases, health, education and society

Evolution Strategies for Learning Sparse Matrix Representations of Gene Regulatory Networks

Currently, a massive amount of temporal gene expression data is available to researchers, which makes it possible to infer Gene Regulatory Networks (GRNs). Gene regulatory networks are theoretical models to represent excitatory and inhibitory interactions between genes. GRNs are useful in understanding how genes function, and hence they are also useful in pharmaceutical and other applications in biology and medicine. However, despite the importance of GRNs, the process of inferring GRNs from observational data is very difficult. This thesis applies evolutionary algorithms to the problem of GRN inference. We propose a novel evolutionary algorithm: hierarchical evolution strategy (HES) to target the specific difficulties in GRN inference. We propose a sparse matrix representation of GRN to account for sparse connectivity in biological gene interactions. Unlike traditional evolution strategies, we divide our optimization into two concurrent processes: connectivity construction and numerical optimization. In each generation, we first establish connectivity structure of the GRN. Inside the same generation, we apply a secondary ES to find the best numerical values with those fixed connections. We also propose a hybrid crowding method to maintain high population diversity while applying the evolutionary algorithms. High population diversity leads to broader exploration area in the search space, therefore preventing premature convergence. The results obtained show that the proposed HES outperforms other algorithms, and has the potential to scale up to realistic problems with thousands of genes