scholarly journals A Fast Prize-Collecting Steiner Forest Algorithm for Functional Analyses in Biological Networks

2017 ◽  
pp. 263-276 ◽  
Author(s):  
Murodzhon Akhmedov ◽  
Alexander LeNail ◽  
Francesco Bertoni ◽  
Ivo Kwee ◽  
Ernest Fraenkel ◽  
...  
Keyword(s):  
Biological Networks ◽  
Steiner Forest ◽  
Prize Collecting ◽  
Functional Analyses

A Primal-Dual Algorithm for the Generalized Prize-Collecting Steiner Forest Problem

2017 ◽  
Vol 5 (2) ◽  
pp. 219-231
Author(s):  
Lu Han ◽  
Da-Chuan Xu ◽  
Dong-Lei Du ◽  
Chen-Chen Wu
Keyword(s):  
Dual Algorithm ◽  
Steiner Forest ◽  
Primal Dual ◽  
Prize Collecting

scholarly journals ST-Steiner: a spatio-temporal gene discovery algorithm

2019 ◽  
Vol 35 (18) ◽  
pp. 3433-3440 ◽  
Author(s):  
Utku Norman ◽  
A Ercument Cicek
Keyword(s):  
Gene Discovery ◽  
Gene Interaction ◽  
Autism Spectrum ◽  
Supplementary Information ◽  
Temporal Dimension ◽  
Cascading Effect ◽  
Gene Interaction Networks ◽  
Steiner Forest ◽  
Prize Collecting ◽  
Spatio Temporal

AbstractMotivationWhole exome sequencing (WES) studies for autism spectrum disorder (ASD) could identify only around six dozen risk genes to date because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited.ResultsHere, we present a spatio-temporal gene discovery algorithm, which leverages information from evolving gene co-expression networks of neurodevelopment. The algorithm solves a prize-collecting Steiner forest-based problem on co-expression networks, adapted to model neurodevelopment and transfer information from precursor neurodevelopmental windows. The decisions made by the algorithm can be traced back, adding interpretability to the results. We apply the algorithm on ASD WES data of 3871 samples and identify risk clusters using BrainSpan co-expression networks of early- and mid-fetal periods. On an independent dataset, we show that incorporation of the temporal dimension increases the predictive power: predicted clusters are hit more and show higher enrichment in ASD-related functions compared with the state-of-the-art.Availability and implementationThe code is available at http://ciceklab.cs.bilkent.edu.tr/st-steiner.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Simultaneous Reconstruction of Multiple Signaling Pathways via the Prize-Collecting Steiner Forest Problem

2012 ◽  
pp. 287-301 ◽  
Author(s):  
Nurcan Tuncbag ◽  
Alfredo Braunstein ◽  
Andrea Pagnani ◽  
Shao-Shan Carol Huang ◽  
Jennifer Chayes ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Simultaneous Reconstruction ◽  
Steiner Forest ◽  
Prize Collecting ◽  
Multiple Signaling

scholarly journals Simultaneous Reconstruction of Multiple Signaling Pathways via the Prize-Collecting Steiner Forest Problem

2013 ◽  
Vol 20 (2) ◽  
pp. 124-136 ◽  
Author(s):  
Nurcan Tuncbag ◽  
Alfredo Braunstein ◽  
Andrea Pagnani ◽  
Shao-Shan Carol Huang ◽  
Jennifer Chayes ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Simultaneous Reconstruction ◽  
Steiner Forest ◽  
Prize Collecting ◽  
Multiple Signaling

scholarly journals Euclidean Prize-Collecting Steiner Forest

Algorithmica ◽  
2011 ◽  
Vol 62 (3-4) ◽  
pp. 906-929 ◽  
Author(s):  
MohammadHossein Bateni ◽  
MohammadTaghi Hajiaghayi
Keyword(s):  
Steiner Forest ◽  
Prize Collecting

scholarly journals Euclidean Prize-Collecting Steiner Forest

2010 ◽  
pp. 503-514
Author(s):  
MohammadHossein Bateni ◽  
MohammadTaghi Hajiaghayi
Keyword(s):  
Steiner Forest ◽  
Prize Collecting

scholarly journals Connectivity Problems on Heterogeneous Graphs

2018 ◽  
Author(s):  
Jimmy Wu ◽  
Alex Khodaverdian ◽  
Benjamin Weitz ◽  
Nir Yosef
Keyword(s):  
Computational Biology ◽  
Biological Networks ◽  
Single Cells ◽  
Network Connectivity ◽  
Interaction Network ◽  
Reference Database ◽  
Worst Case ◽  
Network Problem ◽  
Prize Collecting ◽  
Reference Graph

AbstractBackgroundNetwork connectivity problems are abundant in computational biology research, where graphs are used to represent a range of phenomena: from physical interactions between molecules to more abstract relationships such as gene co-expression. One common challenge in studying biological networks is the need to extract meaningful, small subgraphs out of large databases of potential interactions. A useful abstraction for this task turned out to be the Steiner network problems: given a reference “database” graph, find a parsimonious subgraph that satisfies a given set of connectivity demands. While this formulation proved useful in a number of instances, the next challenge is to account for the fact that the reference graph may not be static. This can happen for instance, when studying protein measurements in single cells or at different time points, whereby different subsets of conditions can have different protein milieu.Results and DiscussionWe introduce the condition Steiner network problem in which we concomitantly consider a set of distinct biological conditions. Each condition is associated with a set of connectivity demands, as well as a set of edges that are assumed to be present in that condition. The goal of this problem is to find a minimal subgraph that satisfies all the demands through paths that are present in the respective condition. We show that introducing multiple conditions as an additional factor makes this problem much harder to approximate. Specifically, we prove that for C conditions, this new problem is NP-hard to approximate to a factor of C – ϵ, for every C ≥ 2 and ϵ > 0, and that this bound is tight. Moving beyond the worst case, we explore a special set of instances where the reference graph grows monotonically between conditions, and show that this problem admits substantially improved approximation algorithms. We also developed an integer linear programming solver for the general problem and demonstrate its ability to reach optimality with instances from the human protein interaction network.ConclusionOur results demonstrate that in contrast to most connectivity problems studied in computational biology, accounting for multiplicity of biological conditions adds considerable complexity, which we propose to address with a new solver. Importantly, our results extend to several network connectivity problems that are commonly used in computational biology, such as Prize-Collecting Steiner Tree, and provide insight into the theoretical guarantees for their applications in a multiple condition setting.AvailabilityOur solver for the general condition Steiner network problem is available at https://github.com/YosefLab/condition_connectivity_problems

Trial-based functional analyses as basis for elopement intervention.

2017 ◽  
Vol 17 (2) ◽  
pp. 166-173 ◽  
Author(s):  
Joseph M. Lambert ◽  
Crystal I. Finley ◽  
Carmen E. Caruthers
Keyword(s):  
Functional Analyses
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