A preliminary analysis on metaheuristics methods applied to the Haplotype Inference Problem

TL;DR

This paper explores the potential of metaheuristic and hybrid approaches to improve scalability and effectiveness in solving the complex Haplotype Inference problem in bioinformatics.

Contribution

It presents a feasibility study on applying metaheuristics and hybrid methods to Haplotype Inference, highlighting design issues and potential for hybrid solver development.

Findings

Metaheuristics show promise for larger instances of Haplotype Inference.

Hybrid approaches can effectively combine heuristics, local search, and learning mechanisms.

The study provides insights into modeling challenges and solver design for complex bioinformatics problems.

Abstract

Haplotype Inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This information allows researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as a combinatorial problem (under certain hypotheses, such as the pure parsimony criterion) and to solve it using off-the-shelf combinatorial optimization techniques. The main methods applied to Haplotype Inference are either simple greedy heuristic or exact methods (Integer Linear Programming, Semidefinite Programming, SAT encoding) that, at present, are adequate only for moderate size instances. We believe that metaheuristic and hybrid approaches could provide a better scalability. Moreover, metaheuristics can be very easily combined with problem specific heuristics and they can also be integrated with tree-based search techniques, thus providing a promising framework for hybrid systems in which a good trade-off between effectiveness and efficiency can be reached. In this paper we illustrate a feasibility study of the approach and discuss some relevant design issues, such as modeling and design of approximate solvers that combine constructive heuristics, local search-based improvement strategies and learning mechanisms. Besides the relevance of the Haplotype Inference problem itself, this preliminary analysis is also an interesting case study because the formulation of the problem poses some challenges in modeling and hybrid metaheuristic solver design that can be generalized to other problems.