Michael Arock

A novel generalized design methodology and realization of Boolean operations using DNA.

The biological deoxyribonucleic acid (DNA) strand has been increasingly seen as a promising computing unit. A new algorithm is formulated in this paper to design any DNA Boolean operator with molecular beacons (MBs) as its input. Boolean operators realized using the proposed design methodology is presented. The developed operators adopt a uniform representation for logical 0 and 1 for any Boolean operator. The Boolean operators designed in this work employ only a hybridization operation at each stage. Further, this paper for the first time brings out the realization of a binary adder and subtractor using molecular beacons. Simulation results of the DNA-based binary adder and subtractor are given to validate the design.

A parallel GWO technique for aligning multiple molecular sequences

Sequence analysis paves way for structural and functional analysis in Bioinformatics. The preliminary step for this sequence analysis is aligning the molecular sequences. This paper introduces parallelism in aligning multiple sequences by parallelizing a bio-inspired algorithm called Grey Wolf Optimizer (GWO) technique. Owing to the tradeoff between accurate solutions and less computational time, many heuristic algorithms are developed. The GWO algorithm involves search agents, which are treated as initial solutions for the optimization problem. Data parallelism is employed in the initialization phase and generation phase. This technique is implemented in Quadro 4000 a CUDA based GPU using threads. The results show that the proposed algorithm reduces the computational time than other existing ones.

DNA algorithm employing temperature gradient for Freeze-Tag Problem in swarm robotics

The biological deoxyribonucleic acid (DNA) strand is found to be a promising computing unit. An attempt has been made to solve Freeze-Tag Problem (FTP) using DNA. In this paper, the thermodynamic properties of DNA have been utilized along with other biochemical operations to obtain the optimal awakening schedule. Actual distance values are represented using the thermodynamic properties of DNA. The proposed method also finds the minimum spanning tree (MST) of the FTP to obtain the optimal awakening schedule. All possible Euler cycles of the different spanning trees of the problem are first generated. From this generated Euler cycle, the MST is obtained, which is the optimal awakening schedule to the problem. Moreover, the proposed approach can be adopted to solve many real-life applications like broadcasting and scheduling problems, with necessary modifications. In this work, an instance with seven robots is solved using DNA computing.

Event detection using lexical chain

This paper describes a new architecture for event detection from text documents. The proposed system correctly identifies the sentences that describe an event of interest to extract its participants. It follows an unsupervised method for identifying the lexical chains from the raw sentences taken as a training data. The lexical chain constructed using Wordnet lexicon is then used for identifying event mention. The significance of the proposed system is it is the first system that applies lexical chain for event identification. The entire architecture is divided into three tasks namely, natural language pre-processing, lexical chain construction and event detection.

Planted (l, d) - Motif Finding using Particle Swarm Optimization

In Bioinformatics, Motif Finding is one of the most popular problems, which has many applications. Generally, it is to locate recurring patterns in the sequence of nucleotides or amino acids. As we can’t expect the pattern to be exact matching copies owing to biological mutations, the motif finding turns to be an NPcomplete problem. By approximating the same in different aspects, scientists have provided many solutions in the literature. The most of the algorithms suffer with local optima. Particle swarm optimization (PSO) is a new global optimization technique which has wide applications. It finds the global best solution by simply adjusting the trajectory of each individual towards its own best location and towards the best particle of the swarm at each generation. We have adopted the features of the PSO to solve the Planted Motif Finding Problem and have designed a sequential algorithm. We have performed experiments with simulated data it outperforms MbGA and PbGA. The PMbPSO also applied for real biological data sets and observe that the algorithm is also able to detect known TFBS accurately when there are no mutations. General Terms: Evolutionary Optimization Techniques, Bioinformatics, Computational Biology.

Parallel algorithms for robot path planning with simpler VLSI architecture

This paper proposes a parallel algorithm for robot path planning on a linear array with a reconfigurable pipelined bus system (LARPBS) through the construction of a Voronoi diagram on a binary image of the workspace. The algorithm is based on a d4 distance metric, and it does not incur any additional time or processor requirements compared with those of a previously reported proposal (Tzionas et al., 1997). This paper recommends the same model as the simpler VLSI architecture for the problem in question.

Aligning two molecular sequences using genetic operators in grey wolf optimiser technique

Sequence analysis is one of the most important concepts in the domain of bioinformatics. Molecular sequence alignment is a predominant problem in sequence analysis. In this paper, we proposed a new approach for pairwise sequence alignment using a recent meta-heuristic algorithm called Grey Wolf Optimiser GWO technique in which genetic operators are integrated to yield efficient solutions. This algorithm obtains the initial set of alignments by inserting gaps randomly and uses the search agents in GWO for exploration and exploitation. In addition to this, genetic operators like crossover and mutation are applied for faster convergence. A novel horizontal crossover and a single-point crossover that suits, particularly for sequence alignment problem, are employed in this paper. Here, two mutations are used depending upon their threshold value. This threshold value depends on a novel fitness function F

Effective hybrid PSO and K-means clustering algorithm for gene expression data

DNA micro-array technology helps monitor the expression levels of thousands of genes. This paper presents clustering of gene expression data using particle swarm optimisation (PSO) and K-means algorithm combined. Recent studies have shown that partition-based clustering algorithms are more suitable for clustering large datasets. Partition-based K-means is used mostly because of its simple implementation and fast convergence. But it suffers local optima. PSO is a population-based stochastic search process, which searches automatically for the optimal solution in the search space. So, it is combined with K-means algorithm for clustering. In the previous PSO employing papers, particles flock at boundary. Our technique removes boundary blocks by moving the boundary particles towards the global best particle to improve effectiveness of PSO. The results of hybrid PSO, K-means and PSO algorithms are compared for several datasets. Among the three algorithms, the hybrid PSO algorithm performs well for most of the datasets.

A parallel combinatorial algorithm for subtle motifs

In bioinformatics, motif finding is one of the most common problems. It is to locate recurring patterns in the sequence of nucleotides or amino acids. The main difficulty of the problem is that the patterns are not exact matches owing to biological mutations. It is NP-complete. Within the literature many solutions have been provided for this challenging problem. Nevertheless, they do not address certain subtleties. Among them, one is addressed by Hu (2003). In this paper, we propose a parallel combinatorial algorithm for subtle motif finding on a Shared Memory Multiprocessor model. We suggest a method of implementation for the same.

Data clustering using modified k-medoids algorithm

This paper proposes a modified k-medoids algorithm for data clustering. This algorithm is applied on seven different datasets including two gene expression datasets and two medical datasets. It improves initial medoids selection and employs updated medoids selection. Records in the datasets are divided into k groups. Initial medoids are selected from each group and updated medoids are also selected within the group objects, instead of replacing all objects one by one. Two salient features of this algorithm are: 1 it avoids unnecessary selection of all medoids in the dataset 2 distance matrix is calculated only once which avoids every time scanning of large database. Both these processes reduce execution time in our approach. The proposed algorithm is applied on synthetic, genome expression and medical datasets. The outcomes are validated using various measures like Rand Index and FM Index. Experiments show that proposed algorithm runs fast and finds better results than the existing algorithms.