Gene expression imputation techniques for robust post genomic knowledge discovery
- Authors: Sehgal, Muhammad Shoaib B , Gondal, Iqbal , Dooley, Laurence
- Date: 2008
- Type: Text , Book chapter
- Relation: Studies in Computational Intelligence p. 185-206
- Full Text: false
- Reviewed:
- Description: Microarrays measure expression patterns of thousands of genes at a time, under same or diverse conditions, to facilitate faster analysis of biological processes. This gene expression data is being widely used for diagnosis, prognosis and tailored drug discovery. Microarray data, however, commonly contains missing values, which can have high impact on subsequent biological knowledge discovery methods. This has been catalyst for the manifest of different imputation algorithms, including Collateral Missing Value Estimation (CMVE), Bayesian Principal Component Analysis (BPCA), Least Square Impute (LSImpute), Local Least Square Impute (LLSImpute) and K-Nearest Neighbour (KNN). This Chapter investigates the impact of missing values on post genomic knowledge discovery methods like, Gene Selection and Gene Regulatory Network (GRN) reconstruction. A framework for robust subsequent biological knowledge inference has been proposed which has shown significant improvements in the outcomes of Gene Selection and GRN reconstruction methods.
Heuristic non parametric collateral missing value imputation : A step towards robust post-genomic knowledge discovery
- Authors: Sehgal, Muhammad Shoaib B , Gondal, Iqbal , Dooley, Laurence , Coppel, Ross
- Date: 2008
- Type: Text , Conference paper
- Relation: Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2008) Vol. 5625
- Full Text:
- Reviewed:
- Description: Microarrays are able to measure the patterns of expression of thousands of genes in a genometo give profiles that faciliate much faster analysis of biological process for diagnosis, prognosis and tailored drug discovery. Microarrays, however commonly have missing values, various algorithms have been proposed including Collateral Missing Value Estimation (CMVE), Bayesian Principal Component Analysis (BPCA), Least Square Impute (LSImpute). Local Least Square Impute (LLSImpute) and K-Nearest Neighbour (KNN).
Multi-dimensional adaptive SINR based vertical handoff for heterogeneous wireless networks
- Authors: Yang, Kemeng , Gondal, Iqbal , Qiu, Bin
- Date: 2008
- Type: Text , Journal article
- Relation: IEEE Communications Letters Vol. 12, no. 6 (2008), p. 438-440
- Full Text: false
- Reviewed:
- Description: Vertical handoff in next generation heterogeneous wireless networks is a multi-dimensional issue. In this article we propose a multi-dimensional adaptive SINR based vertical handoff algorithm (MASVH) which uses the combined effects of SINR, user required bandwidth, user traffic cost and utilization from participating access networks to make handoff decisions for multi-attribute QoS consideration. Simulation results confirm that the new MASVH algorithm improves the system performance in terms of higher throughput and lower dropping probability, as well as reduces the user traffic cost for accessing the integrated wireless networks.
Multiple radio channels and directional antennas in suburban ad hoc networks
- Authors: Rokonuzzaman, S. K. , Pose, Ronald , Gondal, Iqbal
- Date: 2008
- Type: Text , Conference paper
- Relation: 2008 International Symposium on Parallel and Distributed Processing with Applications p. 379-386
- Full Text: false
- Reviewed:
- Description: The Suburban Ad Hoc Network (SAHN) is a cooperative ad hoc wireless mesh network. Nodes are owned and operated by end-users without reliance on central infrastructure. It provides symmetrical bandwidth allowing peer-to-peer services and distributed servers. We minimize the use of scarce unlicensed RF spectrum supported by Smart Antenna technology. RF interference in such networks and techniques and strategies to reduce it are examined. Traffic is spread across multiple frequency channels, and multiple directional beams to achieve improved spatial re-use. We focus on the control of smart antennas rather than their design. By dynamically adjusting our network topology using Smart Antennas and dynamically re-routing current communications we optimize the network for its current traffic needs.