Pattern recognition in bioinformatics : Girls lose out
- Authors: Ahmad, Shandar , Chetty, Madhu , Schmidt, Bertil
- Date: 2010
- Type: Text , Journal article
- Relation: Pattern Recognition Letter Vol. 31, no. 14 (2010), p. 2071-2072
- Full Text: false
- Reviewed:
- Description: Editorial- With the advent of high speed computers, in-silico studies on biological patterns in recent years have been significantly impacted by the pattern recognition techniques. In this special issue, ‘Pattern Recognition in Bioinformatics’, we present various sophisticated algorithms for a wide range of pattern recognition problems from the world of complex biological systems, whether these are specific sequence signatures – motifs that stand out in discovering its partner – or substructures in an interaction network that determines an organisms’ response to external stimuli. The 12 high-quality articles included in this special issue are essentially based on significant extensions of the selected papers presented at the Third International Conference on Pattern Recognition in Bioinformatics (PRIB 2008) held in Melbourne, Australia. All these selected papers for special issue have again undergone a thorough review by at least three reviewers who are experts in the field. The fresh review process was followed to ensure that the papers met the high standards of scientific and technical merit of the Pattern Recognition Letters journal. The issue is broadly divided into three sections of four papers each, namely (1) Section 1: Interaction Networks and Feature-based Predictions (2) Section 2: Microarray and Transcription Data Analysis (3) Section 3: Sequence Analysis and Motif Discovery
Combining segmental semi-Markov models with neural networks for protein secondary structure prediction
- Authors: Bidargaddi, Niranjan , Chetty, Madhu , Kamruzzaman, Joarder
- Date: 2009
- Type: Text , Journal article
- Relation: Neurocomputing Vol. 72, no. 3943-3950 (2009), p.3943-3950
- Full Text: false
- Reviewed:
- Description: Predicting the secondary structure of proteins from a primary sequence alone has been variously approached from either a classification or a generative model perspective. The most prominent classification methods have used neural networks, which involves mappings from a local window of residues in the sequence to the structural state of the central residue in the window, thus capturing the local interactions effectively. However, they fail to capture distant interactions among residues. The generative models based on Bayesian segmentation capture sequence structure relationships using generalized hidden Markov models with explicit state duration. They capture non-local interactions through a joint sequence-structure probability distribution based on structural segments. In this paper, we investigate a combined architecture of Bayesian segmentation at the first stage and neural network at the second stage which captures both local and non-local correlation, to increase the single sequence prediction accuracy. The combined architecture is further enhanced by using neural network optimization and ensemble techniques.
Evaluating influence of microRNA in reconstructing gene regulatory networks
- Authors: Chowdhury, Ahsan , Chetty, Madhu , Nguyen, Vinh
- Date: 2015
- Type: Text , Journal article
- Relation: Cognitive neurodynamics Vol. 8, no. 3 (2015), p. 251-9
- Full Text: false
- Reviewed:
- Description: Gene regulatory network (GRN) consists of interactions between transcription factors (TFs) and target genes (TGs). Recently, it has been observed that micro RNAs (miRNAs) play a significant part in genetic interactions. However, current microarray technologies do not capture miRNA expression levels. To overcome this, we propose a new technique to reverse engineer GRN from the available partial microarray data which contains expression levels of TFs and TGs only. Using S-System model, the approach is adapted to cope with the unavailability of information about the expression levels of miRNAs. The versatile Differential Evolutionary algorithm is used for optimization and parameter estimation. Experimental studies on four in silico networks, and a real network of Saccharomyces cerevisiae called IRMA network, show significant improvement compared to traditional S-System approach.
Stochastic S-system modeling of gene regulatory network
- Authors: Chowdhury, Ahsan , Chetty, Madhu , Evans, Rob
- Date: 2015
- Type: Text , Journal article
- Relation: Cognitive Neurodynamics Vol. 9, no. 5 (2015), p. 535-547
- Full Text: false
- Reviewed:
- Description: Microarray gene expression data can provide insights into biological processes at a system-wide level and is commonly used for reverse engineering gene regulatory networks (GRN). Due to the amalgamation of noise from different sources, microarray expression profiles become inherently noisy leading to significant impact on the GRN reconstruction process. Microarray replicates (both biological and technical), generated to increase the reliability of data obtained under noisy conditions, have limited influence in enhancing the accuracy of reconstruction. Therefore, instead of the conventional GRN modeling approaches which are deterministic, stochastic techniques are becoming increasingly necessary for inferring GRN from noisy microarray data. In this paper, we propose a new stochastic GRN model by investigating incorporation of various standard noise measurements in the deterministic S-system model. Experimental evaluations performed for varying sizes of synthetic network, representing different stochastic processes, demonstrate the effect of noise on the accuracy of genetic network modeling and the significance of stochastic modeling for GRN reconstruction. The proposed stochastic model is subsequently applied to infer the regulations among genes in two real life networks: (1) the well-studied IRMA network, a real-life in-vivo synthetic network constructed within the Saccharomycescerevisiae yeast, and (2) the SOS DNA repair network in Escherichiacoli. © 2015, Springer Science+Business Media Dordrecht.
PCA based population generation for genetic network optimization
- Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
- Date: 2018
- Type: Text , Journal article
- Relation: Cognitive Neurodynamics Vol. 12, no. 4 (2018), p. 417-429
- Full Text:
- Reviewed:
- Description: A gene regulatory network (GRN) represents a set of genes and its regulatory interactions. The inference of the regulatory interactions between genes is usually carried out using an appropriate mathematical model and the available gene expression profile. Among the various models proposed for GRN inference, our recently proposed Michaelis–Menten based ODE model provides a good trade-off between the computational complexity and biological relevance. This model, like other known GRN models, also uses an evolutionary algorithm for parameter estimation. Considering various issues associated with such population based stochastic optimization approaches (e.g. diversity, premature convergence due to local optima, accuracy, etc.), it becomes important to seed the initial population with good individuals which are closer to the optimal solution. In this paper, we exploit the inherent strength of principal component analysis (PCA) in a novel manner to initialize the population for GRN optimization. The benefit of the proposed method is validated by reconstructing in silico and in vivo networks of various sizes. For the same level of accuracy, the approach with PCA based initialization shows improved convergence speed.