J C Rajapakse
Affiliation: Nanyang Technological University
- Stability of building gene regulatory networks with sparse autoregressive modelsJagath C Rajapakse
Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore 639798
BMC Bioinformatics 12:S17. 2011..This paper investigates the stability of sparse auto-regressive models of building GRN from gene expression data...
- Correlation of cell membrane dynamics and cell motilityMerlin Veronika
Computation and Systems Biology, Singapore MIT Alliance, Nanyang Technological University, Singapore 637460
BMC Bioinformatics 12:S19. 2011..We aim to bridge the gap between membrane dynamics and cell states from the perspective of whole cell movement by identifying cell edge patterns and its correlation with cell dynamics...
- Detecting robust time-delayed regulation in Mycobacterium tuberculosisIti Chaturvedi
Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore
BMC Genomics 10:S28. 2009..Using the derived networks, we discuss the delayed regulations and robustness of the DNA damage seen in the bacterium...
- Sub-population analysis based on temporal features of high content imagesMerlin Veronika
Computation and Systems Biology, Singapore MIT Alliance, Nanyang Technological University, Singapore
BMC Bioinformatics 10:S4. 2009..The demand of new platforms, coupled with availability of terabytes of data has challenged the traditional technique of identifying cell populations by manual methods and resulted in development of high-dimensional analytical methods...
- RecMotif: a novel fast algorithm for weak motif discoveryHe Quan Sun
School of Computer Engineering, Nanyang Technological University, 639798, Singapore
BMC Bioinformatics 11:S8. 2010....
- Probabilistic framework for brain connectivity from functional MR imagesJagath C Rajapakse
School of Computer Engineering and the BioInformatics Research Centre, Nanyang Technological University, 50 Nanyang Avenue, 639798 Singapore
IEEE Trans Med Imaging 27:825-33. 2008..Experimental results show that the present approach outperforms earlier fMRI analysis techniques on synthetic functional images and robustly derives brain connectivity from real fMRI data...
- Learning effective brain connectivity with dynamic Bayesian networksJagath C Rajapakse
Bioinformatics Research Center, Nanyang Technological University, Singapore
Neuroimage 37:749-60. 2007..Furthermore, we study the effects of hemodynamic noise, scanner noise, inter-scan interval, and the variability of hemodynamic parameters on the derived connectivity...
- Markov encoding for detecting signals in genomic sequencesJagath C Rajapakse
Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore 639798
IEEE/ACM Trans Comput Biol Bioinform 2:131-42. 2005..We demonstrate the efficacy of the Markov encoding method in the detection of three genomic signals, namely, splice sites, transcription start sites, and translation initiation sites...
- Exploratory analysis of brain connectivity with ICAJagath C Rajapakse
Max-Planck-Institute of Cognitive Neuroscience, Leipzig, Germany
IEEE Eng Med Biol Mag 25:102-11. 2006
- Proteomic cancer classification with mass spectrometry dataJagath C Rajapakse
Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore
Am J Pharmacogenomics 5:281-92. 2005..To illustrate the importance of feature selection in cancer classification, we present a method based on support vector machine-recursive feature elimination (SVM-RFE), demonstrated on two cancer datasets from ovarian and lung cancer...
- Bayesian approach to segmentation of statistical parametric mapsJ C Rajapakse
School of Computer Engineering, Nanyang Technological University, Singapore, Singapore
IEEE Trans Biomed Eng 48:1186-94. 2001....
- Random-grid stereologic volumetry of MR head scansJ C Rajapakse
School of Computer Engineering, Nanyang Technological University, Singapore
J Magn Reson Imaging 12:833-41. 2000..Also, the effects of grid sizes, the optimal directions of sectioning the object for volume estimates of the brain and ventricles, and the reliability of the technique are investigated. J. Magn. Reson. Imaging 2000;12:833-841...
- Modeling hemodynamic response for analysis of functional MRI time-seriesJ C Rajapakse
Max Planck Institute of Cognitive Neuroscience, Leipzig, Germany
Hum Brain Mapp 6:283-300. 1998....