Solving biclustering with a GRASP-like metaheuristic: two case-study on gene expression analysis

The explosion of "omics" data over the past few decades has generated an increasing need of efficiently analyzing high-dimensional gene expression data in several different and heterogenous contexts, such as for example in information retrieval, knowledge discovery, and data mining. For this reason, biclustering, or simultaneous clustering of both genes and conditions has generated considerable interest over the past few decades. Unfortunately, the problem of locating the most significant bicluster has been shown to be NP-complete. We have designed and implemented a GRASP-like heuristic algorithm to efficiently find good solutions in reasonable running times, and to overcome the inner intractability of the problem from a computational point of view. Experimental results on two datasets of expression data are promising indicating that this algorithm is able to find significant biclusters, especially from a biological point of view

A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data

<p>Abstract</p> <p>Background</p> <p>In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of rows coherent with groups of columns. This kind of clustering is called <it>biclustering</it>. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed.</p> <p>Methods</p> <p>We introduce <it>BiMine</it>, a new enumeration algorithm for biclustering of DNA microarray data. The proposed algorithm is based on three original features. First, <it>BiMine </it>relies on a new evaluation function called <it>Average Spearman's rho </it>(ASR). Second, <it>BiMine </it>uses a new tree structure, called <it>Bicluster Enumeration Tree </it>(BET), to represent the different biclusters discovered during the enumeration process. Third, to avoid the combinatorial explosion of the search tree, <it>BiMine </it>introduces a parametric rule that allows the enumeration process to cut tree branches that cannot lead to good biclusters.</p> <p>Results</p> <p>The performance of the proposed algorithm is assessed using both synthetic and real DNA microarray data. The experimental results show that <it>BiMine </it>competes well with several other biclustering methods. Moreover, we test the biological significance using a gene annotation web-tool to show that our proposed method is able to produce biologically relevant biclusters. The software is available upon request from the authors to academic users.</p

Matrix Reordering Methods for Table and Network Visualization

International audienceThis survey provides a description of algorithms to reorder visual matrices of tabular data and adjacency matrix of networks. The goal of this survey is to provide a comprehensive list of reordering algorithms published in different fields such as statistics, bioinformatics, or graph theory. While several of these algorithms are described in publications and others are available in software libraries and programs, there is little awareness of what is done across all fields. Our survey aims at describing these reordering algorithms in a unified manner to enable a wide audience to understand their differences and subtleties. We organize this corpus in a consistent manner, independently of the application or research field. We also provide practical guidance on how to select appropriate algorithms depending on the structure and size of the matrix to reorder, and point to implementations when available

Biclustering sobre datos de expresión génica basado en búsqueda dispersa

Falta palabras claveLos datos de expresión génica, y su particular naturaleza e importancia, motivan no sólo el desarrollo de nuevas técnicas sino la formulación de nuevos problemas como el problema del biclustering. El biclustering es una técnica de aprendizaje no supervisado que agrupa tanto genes como condiciones. Este doble agrupamiento lo diferencia del clustering tradicional sobre este tipo de datos ya que éste sólo agrupa o bien genes o condiciones. La presente tesis presenta un nuevo algoritmo de biclustering que permite el estudio de distintos criterios de búsqueda. Dicho algoritmo utiliza esquema de búsqueda dispersa, o scatter search, que independiza el mecanismo de búsqueda del criterio empleado. Se han estudiado tres criterios de búsqueda diferentes que motivan las tres principales aportaciones de la tesis. En primer lugar se estudia la correlación lineal entre los genes, que se integra como parte de la función objetivo empleada por el algoritmo de biclustering. La correlación lineal permite encontrar biclusters con patrones de desplazamiento y escalado, lo que mejora propuestas anteriores. En segundo lugar, y motivado por el significado biológico de los patrones de activación-inhibición entre genes, se modifica la correlación lineal de manera que se contemplen estos patrones. Por último, se ha tenido en cuenta la información disponible sobre genes en repositorios públicos, como la ontología de genes GO, y se incorpora dicha información como parte del criterio de búsqueda. Se añade un término extra que refleja, por cada bicluster que se evalúe, la calidad de ese grupo de genes según su información almacenada en GO. Se estudian dos posibilidades para dicho término de integración de información biológica, se comparan entre sí y se comprueba que los resultados son mejores cuando se usa información biológica en el algoritmo de biclustering. Las tres aportaciones descritas, junto con una serie de pasos intermedios, han dado lugar a resultados publicados tanto en revistas como en conferencias nacionales e internacionales

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

AIRO 2016. 46th Annual Conference of the Italian Operational Research Society. Emerging Advances in Logistics Systems Trieste, September 6-9, 2016 - Abstracts Book

The AIRO 2016 book of abstract collects the contributions from the conference participants. The AIRO 2016 Conference is a special occasion for the Italian Operations Research community, as AIRO annual conferences turn 46th edition in 2016. To reflect this special occasion, the Programme and Organizing Committee, chaired by Walter Ukovich, prepared a high quality Scientific Programme including the first initiative of AIRO Young, the new AIRO poster section that aims to promote the work of students, PhD students, and Postdocs with an interest in Operations Research. The Scientific Programme of the Conference offers a broad spectrum of contributions covering the variety of OR topics and research areas with an emphasis on “Emerging Advances in Logistics Systems”. The event aims at stimulating integration of existing methods and systems, fostering communication amongst different research groups, and laying the foundations for OR integrated research projects in the next decade. Distinct thematic sections follow the AIRO 2016 days starting by initial presentation of the objectives and features of the Conference. In addition three invited internationally known speakers will present Plenary Lectures, by Gianni Di Pillo, Frédéric Semet e Stefan Nickel, gathering AIRO 2016 participants together to offer key presentations on the latest advances and developments in OR’s research

Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling

In the last decade, the Industrial Revolution 4.0 brought flexible supply chains and flexible design projects to the forefront. Nevertheless, the recent pandemic, the accompanying economic problems, and the resulting supply problems have further increased the role of logistics and supply chains. Therefore, planning and scheduling procedures that can respond flexibly to changed circumstances have become more valuable both in logistics and projects. There are already several competing criteria of project and logistic process planning and scheduling that need to be reconciled. At the same time, the COVID-19 pandemic has shown that even more emphasis needs to be placed on taking potential risks into account. Flexibility and resilience are emphasized in all decision-making processes, including the scheduling of logistic processes, activities, and projects

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Analyse et fouille de données de trajectoires d'objets mobiles

In this thesis, we explore two problems related to managing and mining moving object trajectories. First, we study the problem of sampling trajectory data streams. Storing the entirety of the trajectories provided by modern location-aware devices can entail severe storage and processing overheads. Therefore, adapted sampling techniques are necessary in order to discard unneeded positions and reduce the size of the trajectories while still preserving their key spatiotemporal features. In streaming environments, this process needs to be conducted "on-the-fly" since the data are transient and arrive continuously. To this end, we introduce a new sampling algorithm called spatiotemporal stream sampling (STSS). This algorithm is computationally-efficient and guarantees an upper bound for the approximation error introduced during the sampling process. Experimental results show that stss achieves good performances and can compete with more sophisticated and costly approaches. The second problem we study is clustering trajectory data in road network environments. We present three approaches to clustering such data: the first approach discovers clusters of trajectories that traveled along the same parts of the road network; the second approach is segment-oriented and aims to group together road segments based on trajectories that they have in common; the third approach combines both aspects and simultaneously clusters trajectories and road segments. We show how these approaches can be used to reveal useful knowledge about flow dynamics and characterize traffic in road networks. We also provide experimental results where we evaluate the performances of our propositions.Dans un premier temps, nous étudions l'échantillonnage de flux de trajectoires. Garder l'intégralité des trajectoires capturées par les terminaux de géo-localisation modernes peut s'avérer coûteux en espace de stockage et en temps de calcul. L'élaboration de techniques d'échantillonnage adaptées devient primordiale afin de réduire la taille des données en supprimant certaines positions tout en veillant à préserver le maximum des caractéristiques spatiotemporelles des trajectoires originales. Dans le contexte de flux de données, ces techniques doivent en plus être exécutées "à la volée" et s'adapter au caractère continu et éphémère des données. A cet effet, nous proposons l'algorithme STSS (spatiotemporal stream sampling) qui bénéficie d'une faible complexité temporelle et qui garantit une borne supérieure pour les erreurs d’échantillonnage. Nous montrons les performances de notre proposition en la comparant à d'autres approches existantes. Nous étudions également le problème de la classification non supervisée de trajectoires contraintes par un réseau routier. Nous proposons trois approches pour traiter ce cas. La première approche se focalise sur la découverte de groupes de trajectoires ayant parcouru les mêmes parties du réseau routier. La deuxième approche vise à grouper des segments routiers visités très fréquemment par les mêmes trajectoires. La troisième approche combine les deux aspects afin d'effectuer un co-clustering simultané des trajectoires et des segments. Nous démontrons comment ces approches peuvent servir à caractériser le trafic et les dynamiques de mouvement dans le réseau routier et réalisons des études expérimentales afin d'évaluer leurs performances