627 research outputs found
Node-attribute graph layout for small-world networks
Small-world networks are a very commonly occurring type of graph in the real-world, which exhibit a clustered structure that is not well represented by current graph layout algorithms. In many cases we also have information about the nodes in such graphs, which are typically depicted on the graph as node colour, shape or size. Here we demonstrate that these attributes can instead be used to layout the graph in high-dimensional data space. Then using a dimension reduction technique, targeted projection pursuit, the graph layout can be optimised for displaying clustering. The technique out-performs force-directed layout methods in cluster separation when applied to a sample, artificially generated, small-world network
Visualization for biomedical ontologies alignment
Tese de mestrado, Bioinformática e Biologia Computacional (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2016Desde o início do século, a investigação biomédica e a prática clínica levaram a uma acumulação de grandes quantidades de informação, por exemplo, os dados resultantes da sequenciação genómica ou os registos médicos. As ontologias fornecem um modelo estruturado com o intuito de representar o conhecimento e têm sido bem sucedidas no domínio biomédico na melhoria da interoperabilidade e partilha. O desenvolvimento desconectado das ontologias biomédicas levou à criação de modelos que apresentam domínios idênticos ou sobrepostos. As técnicas de emparelhamento de ontologias foram desenvolvidas afim de estabelecer ligações significativas entre as classes das ontologias, por outras palavras, para criar alinhamentos. Para alcançar um alinhamento ótimo é, não só importante melhorar as técnicas de emparelhamentos mas também criar as ferramentas necessárias para que possa existir intervenção humana, particularmente na visualização. Apesar da importância da intervenção de utilizadores e da visualização no emparelhamento de ontologias, poucos sistemas o suportam, sobretudo para grandes e complexas ontologias como as do domínio biomédico, concretamente no contexto da revisão de alinhamentos e interpretação de incoerências lógicas. O objetivo central desta tese consistiu na investigação dos principais paradigmas de visualização de ontologias, no contexto do alinhamento de ontologias biomédicas, e desenvolver abordagens de visualização e interação que vão de encontro a estes desafios. O trabalho desenvolvido levou, então, à criação de um novo módulo de visualização para um sistema de emparelhamento do state of the art que suporta a revisão de alinhamentos, e à construção de uma ferramenta online que visa ajudar o utilizador a compreender os conflitos encontrados nos alinhamentos, ambos baseados numa abordagem de visualização de subgrafos. Ambas as contribuições foram avaliadas em pequena escala, por testes a utilizadores que revelaram a relevância da visualização de subgrafos contra a visualização em árvore, mais comum no domínio biomédico.Since the begin of the century, biomedical research and clinical practice have resulted in the accumulation of very large amounts of information, e.g. data from genomic sequencing or medical records. Ontologies provide a structured model to represent knowledge and have been quite successful in the biomedical domain at improving interoperability and sharing. The disconnected development of biomedical ontologies has led to the creation of models that have overlapping or even equal domains. Ontology matching techniques were developed to establish meaningful connections between classes of the ontologies, in other words to create alignments. In order to achieve an optimal alignment, it is not only important to improve the matching techniques but also to create the necessary tools for human intervention, namely in visualization. Despite the importance of user intervention and visualization in ontology matching, few systems support these, especially for large and complex ontologies such as those in the biomedical domain, specifically in the context of the alignment revision and logical incoherence explanation. The central objective of this thesis was to investigate the main ontology visualization paradigms, in the context of biomedical ontology matching, and to develop visualization and interaction approaches addressing those challenges. The work developed lead to the creation of a new visualization module for a state of the art ontology matching system, that supports the alignment review, and to the construction of an online tool that aims to help the user understand the conflicts found in the alignments both based on a subgraph visualization approach. Both contributions were evaluated, in a small-scale, by user tests that revealed the relevance of subgraph visualization versus the more common tree visualization for the biomedical domain
Explorative Graph Visualization
Netzwerkstrukturen (Graphen) sind heutzutage weit verbreitet. Ihre Untersuchung dient dazu, ein besseres Verständnis ihrer Struktur und der durch sie modellierten realen Aspekte zu gewinnen. Die Exploration solcher Netzwerke wird zumeist mit Visualisierungstechniken unterstützt. Ziel dieser Arbeit ist es, einen Überblick über die Probleme dieser Visualisierungen zu geben und konkrete Lösungsansätze aufzuzeigen. Dabei werden neue Visualisierungstechniken eingeführt, um den Nutzen der geführten Diskussion für die explorative Graphvisualisierung am konkreten Beispiel zu belegen.Network structures (graphs) have become a natural part of everyday life and their analysis helps to gain an understanding of their inherent structure and the real-world aspects thereby expressed. The exploration of graphs is largely supported and driven by visual means. The aim of this thesis is to give a comprehensive view on the problems associated with these visual means and to detail concrete solution approaches for them. Concrete visualization techniques are introduced to underline the value of this comprehensive discussion for supporting explorative graph visualization
Stimulated Raman adiabatic passage-like protocols for amplitude transfer generalize to many bipartite graphs
Adiabatic passage techniques, used to drive a system from one quantum state
into another, find widespread application in physics and chemistry. We focus on
techniques to spatially transport a quantum amplitude over a strongly coupled
system, such as STImulated Raman Adiabatic Passage (STIRAP) and Coherent
Tunnelling by Adiabatic Passage (CTAP). Previous results were shown to work on
certain graphs, such as linear chains, square and triangular lattices, and
branched chains. We prove that similar protocols work much more generally, in a
large class of (semi-)bipartite graphs. In particular, under random couplings,
adiabatic transfer is possible on graphs that admit a perfect matching both
when the sender is removed and when the receiver is removed. Many of the
favorable stability properties of STIRAP/CTAP are inherited, and our results
readily apply to transfer between multiple potential senders and receivers. We
numerically test transfer between the leaves of a tree, and find surprisingly
accurate transfer, especially when straddling is used. Our results may find
applications in short-distance communication between multiple quantum
computers, and open up a new question in graph theory about the spectral gap
around the value 0.Comment: 17 pages, 3 figures. v2 is made more mathematical and precise than v
A Computational Framework for Learning from Complex Data: Formulations, Algorithms, and Applications
Many real-world processes are dynamically changing over time. As a consequence, the observed complex data generated by these processes also evolve smoothly. For example, in computational biology, the expression data matrices are evolving, since gene expression controls are deployed sequentially during development in many biological processes. Investigations into the spatial and temporal gene expression dynamics are essential for understanding the regulatory biology governing development. In this dissertation, I mainly focus on two types of complex data: genome-wide spatial gene expression patterns in the model organism fruit fly and Allen Brain Atlas mouse brain data. I provide a framework to explore spatiotemporal regulation of gene expression during development. I develop evolutionary co-clustering formulation to identify co-expressed domains and the associated genes simultaneously over different temporal stages using a mesh-generation pipeline. I also propose to employ the deep convolutional neural networks as a multi-layer feature extractor to generate generic representations for gene expression pattern in situ hybridization (ISH) images. Furthermore, I employ the multi-task learning method to fine-tune the pre-trained models with labeled ISH images. My proposed computational methods are evaluated using synthetic data sets and real biological data sets including the gene expression data from the fruit fly BDGP data sets and Allen Developing Mouse Brain Atlas in comparison with baseline existing methods. Experimental results indicate that the proposed representations, formulations, and methods are efficient and effective in annotating and analyzing the large-scale biological data sets
A biophysical approach to large-scale protein-DNA binding data
About this book * Cutting-edge genome analysis methods from leading bioinformaticians An accurate description of current scientific developments in the field of bioinformatics and computational implementation is presented by research of the BioSapiens Network of Excellence. Bioinformatics is essential for annotating the structure and function of genes, proteins and the analysis of complete genomes and to molecular biology and biochemistry. Included is an overview of bioinformatics, the full spectrum of genome annotation approaches including; genome analysis and gene prediction, gene regulation analysis and expression, genome variation and QTL analysis, large scale protein annotation of function and structure, annotation and prediction of protein interactions, and the organization and annotation of molecular networks and biochemical pathways. Also covered is a technical framework to organize and represent genome data using the DAS technology and work in the annotation of two large genomic sets: HIV/HCV viral genomes and splicing alternatives potentially encoded in 1% of the human genome
The State of the Art in Multilayer Network Visualization
Modelling relationships between entities in real-world systems with a simple
graph is a standard approach. However, reality is better embraced as several
interdependent subsystems (or layers). Recently the concept of a multilayer
network model has emerged from the field of complex systems. This model can be
applied to a wide range of real-world datasets. Examples of multilayer networks
can be found in the domains of life sciences, sociology, digital humanities and
more. Within the domain of graph visualization there are many systems which
visualize datasets having many characteristics of multilayer graphs. This
report provides a state of the art and a structured analysis of contemporary
multilayer network visualization, not only for researchers in visualization,
but also for those who aim to visualize multilayer networks in the domain of
complex systems, as well as those developing systems across application
domains. We have explored the visualization literature to survey visualization
techniques suitable for multilayer graph visualization, as well as tools,
tasks, and analytic techniques from within application domains. This report
also identifies the outstanding challenges for multilayer graph visualization
and suggests future research directions for addressing them
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