Genetic algorithm solution for partial digest problem.

One of the fundamental problems in computational biology is the construction of physical maps of chromosomes from the hybridisation experiments between unique probes and clones of chromosome fragments. Before introducing the shotgun sequencing method, Partial Digest Problem (PDP) was an intractable problem used to construct the physical maps of DNA sequence in molecular biology. In this paper, we develop a novel Genetic Algorithm (GA) for solving the PDP.

Tag SNP selection via a genetic algorithm.

Single Nucleotide Polymorphisms (SNPs) provide valuable information on human evolutionary history and may lead us to identify genetic variants responsible for human complex diseases. Unfortunately, molecular haplotyping methods are costly, laborious, and time consuming; therefore, algorithms for constructing full haplotype patterns from small available data through computational methods, Tag SNP selection problem, are convenient and attractive. This problem is proved to be an NP-hard problem, so heuristic methods may be useful.

Kavosh: a new algorithm for finding network motifs.

Background: Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks.

Genetic algorithm for dyad pattern finding in DNA sequences.

In this paper a novel genetic algorithm is presented for the dyad motif finding problem. The genetic algorithm uses a multi-objective fitness function based on the sum of pairs, the number of matches, and the information content. The individuals required for the population pool in the genetic algorithm are optimized by Gibbs sampling method.

New scoring schema for finding motifs in DNA Sequences.

Background: Pattern discovery in DNA sequences is one of the most fundamental problems in molecular biology with important applications in finding regulatory signals and transcription factor binding sites. An important task in this problem is to search (or predict) known binding sites in a new DNA sequence. For this reason, all subsequences of the given DNA sequence are scored based on an scoring function and the prediction is done by selecting the best score.

DNA algorithm for an unbounded fan-in Boolean circuit.

In this paper, we present a new DNA-based evaluation algorithm for a Boolean circuit that employs standard bio-molecular techniques. The algorithm operates on an unbounded fan-in Boolean circuit consisting of AND and OR gates. The whole simulation of our algorithm is proposed in a single test tube in O(1) time complexity and is much easier to implement in the laboratory than previously described models.