Capacitated clustering problem in computational biology: Combinatorial and statistical approach for sibling reconstruction

Abstract

The capacitated clustering problem (CCP) has been studied in a wide range of applications. In this study, we investigate a challenging CCP in computational biology, namely, sibling reconstruction problem (SRP). The goal of SRP is to establish the sibling relationship (i.e., groups of siblings) of a population from genetic data. The SRP has gained more and more interests from computational biologists over the past decade as it is an important and necessary keystone for studies in genetic and population biology. We propose a large-scale mixed integer formulation of the CCP for SRP, that is based on both combinatorial and statistical genetic concepts. The objective is not only to find the minimum number of sibling groups, but also to maximize the degree of similarity of individuals in the same sibling groups Preprint submitted to Computers and Operations Research April 28, 2011 while each sibling group is subject to genetic constraints derived from the Mendel's laws. We develop a new randomized greedy optimization algorithm to effectively and efficiently solve this SRP. The algorithm consists of two key phases: construction and enhancement. In the construction phase, a greedy approach with randomized perturbation is applied to construct multiple sibling groups iteratively. In the enhancement phase, a two-stage local search with a memory function is used to improve the solution quality with respect to the similarity measure. We demonstrate the effectiveness of the proposed algorithm using real biological data sets and compare it with state-of-the-art approaches in the literature. We also test it on larger simulated data sets.The experimental results show that the proposed algorithm provide the best reconstruction solutions.

A case study on environmental sustainability: A study of the trophic changes in fish species as a result of the damming of rivers through clustering analysis

“…Wiwie, Baumbach, and Röttger (2015) assessed 13 well-known methods using 24 datasets ranging from gene expression to protein domains. The use of the Capacitated Clustering Problem (CCP) in the Sibling Reconstruction Problem (SRP) was investigated by Chou, Chaovalitwongse, Berger-Wolf, DasGupta, and Ashey (2012). Azzag, Venturini, Oliver, and Cuinot (2007) applied the approach in an analysis of healthy human skin, in online mining of website usage, and in the automatic construction of web portals.…”

An Iterative Neighborhood Local Search Algorithm for Capacitated Centered Clustering Problem

“…The goal is to minimize the total distance between each point and the geometric center of its cluster. CCCP has been applied to many fields, such as the location design of garbage collection areas and sales centers [5], the network design of agricultural product supply chains [6], the site selection of offshore wind farms [7], and sibling reconstruction problem (SRP) in computational biology [8].…”

Column-Generation Framework of Nonlinear Similarity Model for Reconstructing Sibling Groups

“…Chaovalitwongse et al (2010) proposed a complete mixed-integer programming (MIP) model of the SRP and developed an iterative mathematical programming algorithm to reconstruct high-quality sibling groups. Subsequently, Chou et al (2012) developed a randomized greedy algorithm to efficiently find good reconstruction solutions.…”