1,721,019 research outputs found
A fast heuristic for the prize-collecting Steiner tree problem
No full textThe Prize-Collecting Steiner Tree Problem (PCSTP) is a generalized version of the Steiner Tree Problem. PCSTP is well known and well studied problem in Combinatorial Optimization. Since PCSTP is NP-hard, it is computationally costly to achieve solutions for large instances. However, many real life network problems come with a wide range of variables and large instance sizes. Therefore, there is a need for efficient and fast heuristic algorithms to discover the hidden knowledge behind vast networks. There exists a fast heuristic algorithm for the Steiner Tree Problem in the literature, which is based on Minimum Spanning Trees. In this paper, we propose to extend the existing heuristic algorithm to solve PCSTP. The performance of the extended heuristic (MST-PCST) is evaluated on available benchmark instances from the literature. We also test MST-PCST on randomly generated huge graph instances with up to 40000 nodes and 120000 edges. We report the average gap percentage between the solutions of MST-PCST and existing solution approaches in the literature. Results show that overall performance of MST-PCST is promising with tolerable gap percentage and reasonable running time on larger instances. It has a significantly faster running time when graphs scale up which can shed light on large real world network instances
A matheuristic algorithm for the Prize-collecting Steiner Tree Problem
Full text linkThe Prize-collecting Steiner Tree Problem (PCSTP) is a well studied problem in combinatorial optimization. It has a wide range of applications in the literature, for instance in fiber optics such as gas distribution and district heating. In this study, we focus on its application in functional analysis of genes on bio-genetic graphs. In bio-genetics its extremely possible to have a huge graphs to interpret. Since the PCSTP is NP-hard, it is time consuming to obtain solutions for large instances. Thus, there is a need for efficient and fast heuristic algorithms to discover the hidden knowledge behind the vast bio-genetic networks. We propose a matheuristic composed of heuristic clustering algorithm and existing mixed integer liner programming to solve PCSTP. We evaluated the performance of our matheuristic on available real-world benchmark instances from the biology and compared it with existing heuristic approach in the literature. With respect to heuristic results, we obtained solutions with similar or better objective function values. On the other hand the existing heuristic solved the benchmark instances with smaller running time compared to proposed matheuristic
A prize-collecting Steiner tree application for signature selection to stratify diffuse large B-cell lymphoma subtypes
No full textBackground: With the explosion of high-throughput data available in biology, the bottleneck is shifted to effective data interpretation. By taking advantage of the available data, it is possible to identify the biomarkers and signatures to distinguish subtypes of a specific cancer in the context of clinical trials. This requires sophisticated methods to retrieve the information out of the data, and various algorithms have been recently devised.
Results: Here, we applied the prize-collecting Steiner tree (PCST) approach to obtain a gene expression signature for the classification of diffuse large B-cell lymphoma (DLBCL). The PCST is a network-based approach to capture new insights about genomic data by incorporating an interaction network landscape. Moreover, we adopted the ElasticNet incorporating PCA as a classification method. We used seven public gene expression profiling datasets (three for training, and four for testing) available in the literature, and obtained 10 genes as signature. We tested these genes by employing ElasticNet, and compared the performance with the DAC algorithm as current golden standard. The performance of the PCST signature with ElasticNet outperformed the DAC in distinguishing the subtypes. In addition, the gene expression signature was able to accurately stratify DLBCL patients on survival data.
Conclusions: We developed a network-based optimization technique that performs unbiased signature selection by integrating genomic data with biological networks. Our classifier trained with the obtained signature outperformed the state-of-the-art method in subtype distinction and survival data stratification in DLBCL. The proposed method is a general approach that can be applied on other classification problems
A Comparison of Heuristic Methods for the Prize-Collecting Steiner Tree Problem and Their Application in Genomics
No full textThe prize-collecting Steiner tree (PCST) problem is a broadly studied problem in combinatorial optimization. It has been used to model several real world problems related to utility networks. More recently, researchers have started using PCSTs to study biological networks. Biological networks are typically very large in size. This can create a considerable challenge for the available PCST solving methods. Taking this fact into account, we have developed methods for the PCST that efficiently scale up to large biological network instances. Namely, we have devised a heuristic method based on the Minimum Spanning Tree and a matheuristic method composed of a heuristic clustering phase and a solution phase. In this work, we provide a performance comparison for these methods by testing them on large gene interaction networks. Experimental results are reported for the methods, including running times and objective values of the solutions
Discovering Subgroups of Patients from DNA Copy Number Data Using NMF on Compacted Matrices
No full textIn the study of complex genetic diseases, the identification of subgroups of patients sharing similar genetic characteristics represents a challenging task, for example, to improve treatment decision. One type of genetic lesion, frequently investigated in such disorders, is the change of the DNA copy number (CN) at specific genomic traits. Non-negative Matrix Factorization (NMF) is a standard technique to reduce the dimensionality of a data set and to cluster data samples, while keeping its most relevant information in meaningful components. Thus, it can be used to discover subgroups of patients from CN profiles. It is however computationally impractical for very high dimensional data, such as CN microarray data. Deciding the most suitable number of subgroups is also a challenging problem. The aim of this work is to derive a procedure to compact high dimensional data, in order to improve NMF applicability without compromising the quality of the clustering. This is particularly important for analyzing high-resolution microarray data. Many commonly used quality measures, as well as our own measures, are employed to decide the number of subgroups and to assess the quality of the results. Our measures are based on the idea of identifying robust subgroups, inspired by biologically/clinically relevance instead of simply aiming at well-separated clusters. We evaluate our procedure using four real independent data sets. In these data sets, our method was able to find accurate subgroups with individual molecular and clinical features and outperformed the standard NMF in terms of accuracy in the factorization fitness function. Hence, it can be useful for the discovery of subgroups of patients with similar CN profiles in the study of heterogeneous diseases
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
A Fast Prize-Collecting Steiner Forest Algorithm for Functional Analyses in Biological Networks
No full textThe Prize-collecting Steiner Forest (PCSF) problem is NP-hard, requiring extreme computational effort to find exact solutions for large inputs. We introduce a new heuristic algorithm for PCSF which preserves the quality of solutions obtained by previous heuristic approaches while reducing the runtime by a factor of 10 for larger graphs. By decreasing the draw on computational resources, this algorithm affords systems biologists the opportunity to analyze larger biological networks faster and narrow their analyses to individual patients. Keywords: Prize-collecting Steiner Forest, Biological networksNational Institutes of Health (U.S.) (U54-NS-091046)National Institutes of Health (U.S.) (U01-CA184898)Swiss National Science Foundation (205321-147138
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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