Mohammad Sohel Rahman

Identifying rhythms in musical texts

A fundamental problem in music is to classify songs according to their rhythm. A rhythm is represented by a sequence of “Quick” (Q) and “Slow” (S) symbols, which correspond to the (relative) duration of notes, such that S = 2Q. In this paper, we present an efficient algorithm for locating the maximum-length substring of a music text t that can be covered by a given rhythm r.

On the protein folding problem in 2D-triangular lattices

BackgroundIn this paper, we present a novel approximation algorithm to solve the protein folding problem in HP model. Our algorithm is polynomial in terms of the length of the given HP string. The expected approximation ratio of our algorithm is 1-2lognn-1 for n ≥ 6, where n2 is the total number of H’s in a given HP string. The expected approximation ratio tends to reach 1 for large values of n. Hence our algorithm is expected to perform very well for larger HP strings.

Simple Linear Comparison of Strings in V-order*

In this paper we focus on a total (but non-lexicographic) ordering of strings called V-order. We devise a new linear-time algorithm for computing the V-comparison of two finite strings. In comparison with the previous algorithm in the literature, our algorithm is both conceptually simpler, based on recording letter positions in increasing order, and more straightforward to implement, requiring only linked lists.

Gene selection for cancer classification with the help of bees

BackgroundDevelopment of biologically relevant models from gene expression data notably, microarray data has become a topic of great interest in the field of bioinformatics and clinical genetics and oncology. Only a small number of gene expression data compared to the total number of genes explored possess a significant correlation with a certain phenotype. Gene selection enables researchers to obtain substantial insight into the genetic nature of the disease and the mechanisms responsible for it. Besides improvement of the performance of cancer classification, it can also cut down the time and cost of medical diagnoses.MethodsThis study presents a modified Artificial Bee Colony Algorithm (ABC) to select minimum number of genes that are deemed to be significant for cancer along with improvement of predictive accuracy. The search equation of ABC is believed to be good at exploration but poor at exploitation. To overcome this limitation we have modified the ABC algorithm by incorporating the concept of pheromones which is one of the major components of Ant Colony Optimization (ACO) algorithm and a new operation in which successive bees communicate to share their findings.ResultsThe proposed algorithm is evaluated using a suite of ten publicly available datasets after the parameters are tuned scientifically with one of the datasets. Obtained results are compared to other works that used the same datasets. The performance of the proposed method is proved to be superior.ConclusionThe method presented in this paper can provide subset of genes leading to more accurate classification results while the number of selected genes is smaller. Additionally, the proposed modified Artificial Bee Colony Algorithm could conceivably be applied to problems in other areas as well.