Predicting protein structural classes based on complex networks and recurrence analysis.

Protein sequences are divided into four structural classes. The determination of class is a challenging and beneficial task in the bioinformatics field. Several methods have been proposed to this end, but most utilize too many features and produce unsuitable results. In the present, features are extracted based on the predicted secondary structures. At first, predicted secondary structure sequences are mapped into two time series by the chaos game representation. Then, a recurrence matrix is calculated from each of the time series. The recurrence matrix is identified with the adjacency matrix of a complex network and measures are applied for the characterization of complex networks to these recurrence matrixes. For a given protein sequence, a total of 24 characteristic features can be calculated and these are fed into Fisher's discriminated analysis algorithm for classification. To examine the proposed method, two widely used low similarity benchmark datasets design and test its performance. A comparison with the results of existing methods shows that the current study's approach provides a satisfactory performance for protein structural class prediction.

AROHap: An effective algorithm for single individual haplotype reconstruction based on asexual reproduction optimization.

In this paper, a method for single individual haplotype (SIH) reconstruction using Asexual reproduction optimization (ARO) is proposed. Haplotypes, as a set of genetic variations in each chromosome, contain vital information such as the relationship between human genome and diseases. Finding haplotypes in diploid organisms is a challenging task. Experimental methods are expensive and require special equipment. In SIH problem, we encounter with several fragments and each fragment covers some parts of desired haplotype. The main goal is bi-partitioning of the fragments with minimum error correction (MEC). This problem is addressed as NP-hard and several attempts have been made in order to solve it using heuristic methods. The current method, AROHap, has two main phases. In the first phase, most of the fragments are clustered based on a practical metric distance. In the second phase, ARO algorithm as a fast convergence bio-inspired method is used to improve the initial bi-partitioning of the fragments in the previous step. AROHap is implemented with several benchmark datasets. The experimental results demonstrate that satisfactory results were obtained, proving that AROHap can be used for SIH reconstruction problem.