Welcome to the upgraded BRAC University Institutional Repository. We are currently organizing collections after a recent system upgrade. Homepage category counters may temporarily show lower numbers while syncing, but over 27,000 repository items remain safe and accessible. Please use the search bar to find theses, scholarly outputs, and institutional documents.

Conference Paper

Permanent URI for this collectionhttps://hdl.handle.net/10361/7339

Browse

Recent Submissions

Now showing 1 - 2 of 2
  • Thumbnail Image
    listelement.badge.dso-type Item ,
    Inference of genetic networks using multi-objective hybrid SPEA2+ from microarray data
    (© 2013 IEEE, 2013-07) Showkat, Dilruba; Kabir, Mitra Lutful; Department of Computer Science and Engineering
    Multi-objective optimization plays a significant role in optimizing many real life problems, where we desire to optimize more than one objective. Numerous multi-objective optimization algorithm exists in research. NSGA-II and SPEA2 are widely used multi-objective optimization algorithms. SPEA2+ algorithm performs better than the other multi-objective optimization algorithms in terms of searching and maintaining diversity in the optimal solution. In this research, to reconstruct the gene regulatory network we have proposed a new Hybrid SPEA2+ algorithm based inference method. We have proposed a new objective function to obtain sparse gene network structure more precisely. To reverse engineer the gene regulatory network we have used linear time variant model. The proposed approach is at first tested against synthetic noise free time series datasets. It has successfully inferred all the correct regulations from noise free time series datasets. Then it was applied on synthetic noisy time series datasets. Even with the presence of noise, the proposed method have correctly captured all the correct gene regulations successfully. The proposed reconstruction method has been further validated by analyzing the real gene expression datasets of SOS DNA repair system in Escherichia coli. Our proposed method have shown its potency in finding more correct regulations and this has been confirmed by comparing the obtained gene regulations with the results of other existing researches.
  • Thumbnail Image
    listelement.badge.dso-type Item ,
    Reconstructing gene regulatory network with enhanced particle swarm optimization
    (© 2014 Springer International Publishing Switzerland, 2014) Sultana, Rezwana; Showkat, Dilruba; Samiullah, Md.; Chowdhury, Ahsan Raja Aja; Department of Computer Science and Engineering
    Inferring regulations among the genes is a well-known and significantly important problem in systems biology for revealing the fundamental cellular processes. Although computational models can be used as tools to extract the probable structure and dynamics of such networks from gene expression data, capturing the complex nonlinear system dynamics is a challenging task. In this paper, we have proposed a method to reverse engineering Gene Regulatory Network (GRN) from microarray data. Inspired from the biologically relevant optimization algorithm ‘Particle Swarm Optimization’ (PSO), we have enhanced the PSO incorporating two genetic algorithm operators, namely crossover and mutation. Furthermore, Linear Time Variant (LTV) Model is employed to modeling the GRN appropriately. In the evaluation, the proposed method shows superiority over the state-of-the-art methods when tested with synthetic network, both for the noise free and noise in data. The strength of the proposed method has also been verified by analyzing the real expression data set of SOS DNA repair system in Escherichia coli.