Jagath Rajapakse

发表

A quantitative MRI study of the corpus callosum in children and adolescents.

J C Rajapakse, J N Giedd, F X Castellanos, 1996, Brain research. Developmental brain research.

Random‐grid stereologic volumetry of MR head scans

J C Rajapakse, Jagath Rajapakse, 2000, Journal of magnetic resonance imaging : JMRI.

A technique for single-channel MR brain tissue segmentation: application to a pediatric sample.

J. Rapoport, J. Giedd, J. Rajapakse, 1996, Magnetic resonance imaging.

Deep learning and multi-omics approach to predict drug responses in cancer

J. Rajapakse, R. Kaalia, Conghao Wang, 2021, BMC Bioinformatics.

Sexual dimorphism of the developing human brain

Jagath C. Rajapakse, Jay N. Giedd, F. Xavier Castellanos, 1997, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

Quantitative MRI of the temporal lobe, amygdala, and hippocampus in normal human development: Ages 4–18 years

J. Rapoport, N. Lange, J. Giedd, 1995, The Journal of comparative neurology.

Fully Automated Peeling Technique For T1-Weighted, High-Quality Mr Head Scans

Ravinda G. N. Meegama, Jagath C. Rajapakse, R. Meegama, 2004, Int. J. Image Graph..

NURBS-Based Segmentation of the Brain in Medical Images

Ravinda G. N. Meegama, Jagath C. Rajapakse, R. Meegama, 2003, Int. J. Pattern Recognit. Artif. Intell..

Regularization approach to multisensor reconstruction

Raj S. Acharya, Jagath C. Rajapakse, R. Acharya, 1993, Other Conferences.

ICA with Reference

Wei Lu, Jagath C. Rajapakse, W. Lu, 2006, Neurocomputing.

Multiple SVM-RFE for gene selection in cancer classification with expression data

F. Azuaje, Kai-Bo Duan, J.C. Rajapakse, 2005, IEEE Transactions on NanoBioscience.

Multi-class support vector machines for protein secondary structure prediction.

Jagath C Rajapakse, Minh N Nguyen, M. N. Nguyen, 2003, Genome informatics. International Conference on Genome Informatics.

Complex ICA-R

Jagath C. Rajapakse, Wenda Chen, Jagath Rajapakse, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Evolutionary approach to ICA-R

Jacek M. Zurada, Swathi Kavuri Sri, Jagath C. Rajapakse, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Exploratory analysis of brain connectivity with ICA

J.C. Rajapakse, Choong Leong Tan, Xuebin Zheng, 2006, IEEE Engineering in Medicine and Biology Magazine.

Exploratory Analysis of Brain Connectivity with ICA Deriving Functional Connectivity Without a Prior Model

Choong Leong Tan, JAGATH C. RAJAPAKSE, CHOONG LEONG TAN, 2006 .

Approach and applications of constrained ICA

Wei Lu, Jagath C. Rajapakse, Jagath Rajapakse, 2005, IEEE Transactions on Neural Networks.

Denoising of functional MRI using ICA

Jagath C. Rajapakse, Kanyan Yang, Jagath Rajapakse, 2003, 3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the.

Eliminating indeterminacy in ICA

Wei Lu, Jagath C. Rajapakse, Wei Lu, 2003, Neurocomputing.

Eliminating indeterminacyin ICA

Wei Lu, Jagath C. Rajapakse, Wei Lu, 2003 .

Blind processing in neuroimaging: application to FMRI

J.C. Rajapakse, Jagath Rajapakse, 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

Subspace blind extraction by less-complete ICA

Jagath C. Rajapakse, Wei Lu, W. Lu, 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

Non independent strategies for blind source separation in functional MRI

Krzysztof Siwek, Andrzej Cichocki, Jagath C. Rajapakse, 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

Independent component analysis and beyond in brain imaging: EEG, MEG, fMRI, and PET

J.C. Rajapakse, A. Cichocki, V.D. Sanchez A, 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

Unique ICA solution by eliminating indeterminacy

J. Rajapakse, Wei Lu, Jagath Rajapakse, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

EXTRACTING TASK-RELATED COMPONENTS IN FUNCTIONAL MRI

Wei Lu, Jagath C. Rajapakse, Wei Lu, 2001 .

A neural network for undercomplete independent component analysis

Lu Wei, Jagath C. Rajapakse, Jagath Rajapakse, 2000, ESANN.

Constrained Independent Component Analysis

Wei Lu, Jagath C. Rajapakse, Wei Lu, 2000, NIPS.

Human Brain Mapping 6:283–300(1998) � Modeling Hemodynamic Response for Analysis of Functional MRI Time-Series

J. Rajapakse, J. Maisog, F. Kruggel, 2022 .

Detecting functional modules of the brain using eigen value decomposition of the signless Laplacian

Jagath C. Rajapakse, Shaohua Li, Xiuchao Sui, 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

qFibrosis: a fully-quantitative innovative method incorporating histological features to facilitate accurate fibrosis scoring in animal model and chronic hepatitis B patients.

Hanry Yu, Yan Wang, Jagath C Rajapakse, 2014, Journal of hepatology.

MultiDCoX: Multi-factor analysis of differential co-expression

Karuturi R. Krishna Murthy, Jagath C. Rajapakse, Herty Liany, 2017, BMC Bioinformatics.

MultiDCoX: Multi-factor analysis of differential co-expression

Jagath C. Rajapakse, Herty Liany, R. Krishna Murthy Karuturi, 2017, BMC Bioinformatics.

Comparative genomic workflow

Pooja, Chunxi Chen, J. Rajapakse, 2009, IEEE Engineering in Medicine and Biology Magazine.

Discovery of Conserved Noncoding DNA Patterns

Sy-Loi Ho, Jagath Rajapakse, Chunxi Chen, 2009 .

Comparative genomic study of Parkinson's disease candidate genes

G.W.L. Pang, Jagath Rajapakse, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

Inferring neutral evolution from Parkinson's disease genes

Jagath C. Rajapakse, Gavyn Pang, G.W.L. Pang, 2004, 2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

LVQ Approach Using AA Indices for Protein Subcellular Localisation Prediction

Jagath C. Rajapakse, Minh Ngoc Nguyen, Kok-Sin Toh, 2005, 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

Multiclass Gene Selection Using Pareto-Fronts

Jagath C. Rajapakse, Piyushkumar A. Mundra, P. Mundra, 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Color channel encoding with NMF for face recognition

Jagath C. Rajapakse, Menaka Rajapakse, Jeffrey Tan, 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks

Jagath C. Rajapakse, Wenting Liu, Wenting Liu, 2019, BMC Systems Biology.

Title Fusing gene expressions and transitive protein-proteininteractions for inference of gene regulatory networks

J. Rajapakse, Wenting Liu, Jagath Rajapakse, 2019 .

Inferring Gene Regulatory Networks from Time-Series Expressions Using Random Forests Ensemble

Jagath C. Rajapakse, Jie Zheng, Piyushkumar A. Mundra, 2013, PRIB.

Integration of Epigenetic Data in Bayesian Network Modeling of Gene Regulatory Network

Jagath C. Rajapakse, Jie Zheng, Iti Chaturvedi, 2011, PRIB.

Identification of a new export signal in Plasmodium yoelii: identification of a new exportome

J. Rajapakse, L. Rénia, P. Preiser, 2014, Cellular microbiology.

Rosa26-GFP Direct Repeat (RaDR-GFP) Mice Reveal Tissue- and Age-Dependence of Homologous Recombination in Mammals In Vivo

Dushan N. Wadduwage, P. So, B. Engelward, 2014, PLoS genetics.

Comparison of Human and Mouse Pseudogenes

Pooja, Jagath C. Rajapakse, Pooja, 2007, 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology.

Grid-Enabled BLASTZ: Application to Comparative Genomics

Jagath C. Rajapakse, Chunxi Chen, Jagath Rajapakse, 2007, J. VLSI Signal Process..

Differential pulmonary transcriptomic profiles in murine lungs infected with low and highly virulent influenza H3N2 viruses reveal dysregulation of TREM1 signaling, cytokines, and chemokines

R. Welsch, S. Rozen, B. Engelward, 2011, Functional & Integrative Genomics.

Di-codon usage for classification of genes

Jagath C. Rajapakse, Gary B. Fogel, Minh Ngoc Nguyen, 2009, Biosyst..

Di-codon Usage for Gene Classification

Jagath C. Rajapakse, Gary B. Fogel, Jianmin Ma, 2009, PRIB.

Gene Classification Using Codon Usage and Support Vector Machines

J. Rajapakse, M. N. Nguyen, Jianmin Ma, 2009, IEEE/ACM Transactions on Computational Biology & Bioinformatics.

Gene Classification using Codon Usage and SVMs

J. Rajapakse, M. N. Nguyen, J. Ma, 2005, 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

Splice site detection with a higher-order markov model implemented on a neural network.

Loi Sy Ho, Jagath C Rajapakse, L. Ho, 2003, Genome informatics. International Conference on Genome Informatics.

Improving signal-to-noise ratio of structured light microscopy based on photon reassignment

Jagath C. Rajapakse, Peter T. C. So, George Barbastathis, 2011, Biomedical optics express.

Two-Stage support vector machines for protein secondary structure prediction

Jagath C. Rajapakse, Minh Ngoc Nguyen, M. N. Nguyen, 2003, Neural Parallel Sci. Comput..

Serum Proteome and Cytokine Analysis in a Longitudinal Cohort of Adults with Primary Dengue Infection Reveals Predictive Markers of DHF

J. Rajapakse, S. Tannenbaum, E. Ooi, 2012, PLoS neglected tropical diseases.

Toward surface quantification of liver fibrosis progression.

Hanry Yu, Jagath C Rajapakse, Shuoyu Xu, 2010, Journal of biomedical optics.

Fibro-C-Index: comprehensive, morphology-based quantification of liver fibrosis using second harmonic generation and two-photon microscopy.

Hanry Yu, Shi Chang, Jagath C Rajapakse, 2009, Journal of biomedical optics.

Development of the human corpus callosum during childhood and adolescence: A longitudinal MRI study

Jagath C. Rajapakse, J. Rajapakse, J. Blumenthal, 1999, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

Statistical approach to segmentation of single-channel cerebral MR images

Jagath C. Rajapakse, Jay N. Giedd, Judith L. Rapoport, 1997, IEEE Transactions on Medical Imaging.

Quantitative magnetic resonance imaging of the corpus callosum in childhood onset schizophrenia

Jagath C. Rajapakse, Jay N. Giedd, Judith L. Rapoport, 1997, Psychiatry Research: Neuroimaging.

Regional MRI measurements of the corpus callosum: a methodological and developmental study

Jagath C. Rajapakse, Jay N. Giedd, Judith L. Rapoport, 1996, Brain and Development.

Brain anatomic magnetic resonance imaging in childhood-onset schizophrenia.

J C Rajapakse, J N Giedd, J L Rapoport, 1996, Archives of general psychiatry.

Temporal lobe morphology in childhood-onset schizophrenia.

J C Rajapakse, J N Giedd, J L Rapoport, 1996, The American journal of psychiatry.

Quantitative brain magnetic resonance imaging in attention-deficit hyperactivity disorder.

J. Rapoport, F. Castellanos, N. Lange, 1996, Archives of general psychiatry.

Excerpts of research in brain sciences and neural networks in Singapore

Meng Joo Er, Lipo Wang, Guang-Bin Huang, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer’s Disease and Autism Spectrum Disorder

Roy E. Welsch, Jagath C. Rajapakse, Sukrit Gupta, 2020, NeuroImage: Clinical.

Obtaining leaner deep neural networks for decoding brain functional connectome in a single shot

Jagath C. Rajapakse, Sukrit Gupta, Yi Hao Chan, 2020, bioRxiv.

Profiling heterogeneity of Alzheimer's disease using white-matter impairment factors

J. Rajapakse, Xiuchao Sui, Jagath Rajapakse, 2018, NeuroImage: Clinical.

Multi-Class Protein Subcellular Localization Prediction using Support Vector Machines

Peng Wai Meng, J. Rajapakse, Jagath Rajapakse, 2005, 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

Determination of the Relative Importance of Gene Function or Taxonomic Grouping to Codon Usage Bias Using Cluster Analysis and SVMs

Jagath C. Rajapakse, Gary B. Fogel, Jianmin Ma, 2006, 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology.

Gene Regulatory Networks from Gene Ontology

Kuiyu Chang, Jung-jae Kim, Jagath C. Rajapakse, 2013, ISBRA.

Regional glucose metabolic abnormalities are not the result of atrophy in Alzheimer's disease

G. Alexander, P. Pietrini, M. Furey, 1998, Neurology.

Neuromorphic model for information fusion

Jagath C. Rajapakse, R. Acharya, R. Acharya, 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

Multi sensor data fusion within hierarchical neural networks

Raj Acharya, J. Rajapakse, R. Acharya, 1990, 1990 IJCNN International Joint Conference on Neural Networks.

Medical image segmentation with MARA

Raj Acharya, J. Rajapakse, R. Acharya, 1990, 1990 IJCNN International Joint Conference on Neural Networks.

Gene and sample selection for cancer classification with support vectors based t-statistic

Jagath C. Rajapakse, Piyushkumar A. Mundra, P. Mundra, 2010, Neurocomputing.

Support vectors based correlation coefficient for gene and sample selection in cancer classification

Jagath C. Rajapakse, Piyushkumar A. Mundra, P. Mundra, 2010, 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

SVM-RFE With MRMR Filter for Gene Selection

J.C. Rajapakse, P.A. Mundra, P. Mundra, 2010, IEEE Transactions on NanoBioscience.

Support Vector Based T-Score for Gene Ranking

Jagath C. Rajapakse, Piyushkumar A. Mundra, P. Mundra, 2008, PRIB.

Title Spatiotemporal quantification of cell dynamics in the lungfollowing influenza virus infection

J. Rajapakse, Jianzhu Chen, Shuoyu Xu, 2018 .

Aging exacerbates damage and delays repair of alveolar epithelia following influenza viral pneumonia

J. Rajapakse, Jianzhu Chen, Hanry Yu, 2014, Respiratory Research.

Spatiotemporal quantification of cell dynamics in the lung following influenza virus infection

Hanry Yu, Jierong Cheng, Jagath C Rajapakse, 2013, Journal of biomedical optics.

Proteomic Cancer Classification with Mass Spectrometry Data

J. Rajapakse, W. K. Yeo, K. Duan, 2005, American journal of pharmacogenomics : genomics-related research in drug development and clinical practice.

Correlation of cell membrane dynamics and cell motility

Roy E. Welsch, Jagath C. Rajapakse, Paul Matsudaira, 2011, BMC Bioinformatics.

Refining modules to determine functionally significant clusters in molecular networks

J. Rajapakse, R. Kaalia, Jagath Rajapakse, 2019, BMC Genomics.

Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models.

Jagath C. Rajapakse, Francisco Azuaje, Katharina Baum, 2019, F1000Research.

Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models

F. Azuaje, J. Rajapakse, Katharina Baum, 2019, F1000Research.

Graph embeddings on gene ontology annotations for protein–protein interaction prediction

Jagath C Rajapakse, Xiaoshi Zhong, Xiaoshi Zhong, 2020, BMC Bioinformatics.

Predicting Missing and Spurious Protein-Protein Interactions Using Graph Embeddings on GO Annotation Graph

Jagath C. Rajapakse, Xiaoshi Zhong, Xiaoshi Zhong, 2019, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Features for cells and nuclei classification

Song Liu, Jagath C. Rajapakse, Piyushkumar A. Mundra, 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Iterative consensus spectral clustering improves detection of subject and group level brain functional modules

Sukrit Gupta, Jagath Rajapakse, 2020, Scientific Reports.

Prediction of Protein Secondary Structure with two-stage multi-class SVMs

Jagath C. Rajapakse, Minh Ngoc Nguyen, M. N. Nguyen, 2007, Int. J. Data Min. Bioinform..

Functional homogeneity and specificity of topological modules in human proteome

R. Kaalia, Jagath Rajapakse, 2019, BMC Bioinformatics.

Quantification of liver fibrosis via second harmonic imaging of the Glisson's capsule from liver surface

R. Welsch, J. Rajapakse, Hanry Yu, 2016, Journal of biophotonics.

Cerebral magnetic resonance image segmentation using data fusion.

J. Rapoport, J. Giedd, C. Decarli, 1996, Journal of computer assisted tomography.

Amino Acid Features for Prediction of Protein-Protein Interface Residues with Support Vector Machines

Jagath C. Rajapakse, Minh Ngoc Nguyen, Kaibo Duan, 2007, EvoBIO.

Protein-Protein Interface Residue Prediction with SVM Using Evolutionary Profiles and Accessible Surface Areas

Jagath C. Rajapakse, Minh Ngoc Nguyen, M. N. Nguyen, 2006, 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology.

Mixed spectrum analysis in spatial context: Application to fMRI

Arun Kumar, Lin Feng, Jagath C. Rajapakse, 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

Mixed Spectrum Analysis on fMRI Time-Series

Arun Kumar, Feng Lin, Jagath C. Rajapakse, 2016, IEEE Transactions on Medical Imaging.

Predicting Affective States of Programming Using Keyboard Data and Mouse Behaviors

Jagath C. Rajapakse, Owen Noel Newton Fernando, Hualin Liu, 2018, 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV).

Segmentation of Clustered Nuclei With Shape Markers and Marking Function

Jagath C. Rajapakse, Jierong Cheng, Jierong Cheng, 2009, IEEE Transactions on Biomedical Engineering.

A deep neural network approach to predicting clinical outcomes of neuroblastoma patients

Jagath C. Rajapakse, Francisco Azuaje, Léon-Charles Tranchevent, 2019, BMC Medical Genomics.

Deep neural network ensemble by data augmentation and bagging for skin lesion classification pdf

Jagath C. Rajapakse, Manik Goyal, Jagath Rajapakse, 2018, ArXiv.

Machine Learning in Bioinformatics

Jagath C. Rajapakse, Yan-Qing Zhang, Yanqing Zhang, 2008 .

Extracting EEG rhythms using ICA-R

Swathi Kavuri Sri, Jagath C. Rajapakse, Jagath Rajapakse, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

q Fibrosis: A fully-quantitative innovative method incorporating histological features to facilitate accurate ﬁbrosis scoring in animal model and chronic hepatitis B patients

R. Welsch, J. Rajapakse, Hanry Yu, 2014 .

A variant of SVM-RFE for gene selection in cancer classification with expression data

Jagath C. Rajapakse, Kaibo Duan, Jagath Rajapakse, 2004, 2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

Fitting networks models for functional brain connectivity

Jagath C. Rajapakse, Sukrit Gupta, Xiuchao Sui, 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

CUDAGRN: Parallel Speedup of Inferring Large Gene Regulatory Networks from Expression Data Using Random Forest

Rui Fan, Jagath C. Rajapakse, Jie Zheng, 2014, PRIB.

Intensity normalization of two-photon microscopy images for liver fibrosis analysis

Jagath C. Rajapakse, Peter T. C. So, Vijay Raj Singh, 2011, BiOS.

Automated fluorescence intensity and gradient analysis enables detection of rare fluorescent mutant cells deep within the tissue of RaDR mice

Peter T C So, Dushan N. Wadduwage, Vijay Raj Singh, 2018, Scientific Reports.

Human Wharton's Jelly Mesenchymal Stem Cells Show Unique Gene Expression Compared with Bone Marrow Mesenchymal Stem Cells Using Single-Cell RNA-Sequencing.

A. Barrett, M. Choolani, A. Subramanian, 2019, Stem cells and development.

A Cell Profiling Framework for Modeling Drug Responses from HCS Imaging

Jagath C Rajapakse, Victor Horodincu, Roy E Welsch, 2010, Journal of biomolecular screening.

FPGA Implementations of Neural Networks

Jagath C. Rajapakse, Amos R. Omondi, A. Omondi, 2006 .

Detecting robust time-delayed regulation in Mycobacterium tuberculosis

Jagath C Rajapakse, Iti Chaturvedi, I. Chaturvedi, 2009, BMC Genomics.

Contextual modeling of functional MR images with conditional random fields

Yang Wang, Jagath C. Rajapakse, Jagath Rajapakse, 2006, IEEE Transactions on Medical Imaging.

Neutrophils infected with highly virulent influenza H3N2 virus exhibit augmented early cell death and rapid induction of type I interferon signaling pathways.

R. Welsch, J. Rajapakse, B. Engelward, 2013, Genomics.

Editorial for special issue on “Soft Computing for Bioinformatics and Medical Informatics”

David W. Corne, Lipo Wang, Jagath C. Rajapakse, 2006, Soft Computing - A Fusion of Foundations, Methodologies and Applications.

Segmentation of MR images with intensity inhomogeneities

Frithjof Kruggel, Jagath C. Rajapakse, J. Rajapakse, 1998, Image Vis. Comput..

Estimation of the population of neutrophils induced to differentiate from the MPRO mouse promyelocytic cell line

Roy E. Welsch, Jagath C. Rajapakse, Vincent T. K. Chow, 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Proton Spectroscopic Imaging Shows Abnormalities in Glial and Neuronal Cell Pools in Frontal Lobe Epilepsy

A. Häkkinen, N. Lundbom, E. Gaily, 2001, Epilepsia.

Combining Neuroimaging and Omics Datasets for Disease Classification Using Graph Neural Networks

Yi Hao Chan, J. Rajapakse, Conghao Wang, 2022, Frontiers in Neuroscience.

Learning effective brain connectivity with dynamic Bayesian networks

Juan Zhou, Jagath C. Rajapakse, Juan Zhou, 2007, NeuroImage.

Differential pulmonary transcriptomic profiles in murine lungs infected with low and highly virulent influenza H3N2 viruses reveal dysregulation of TREM1 signaling, cytokines, and chemokines

R. Welsch, S. Rozen, B. Engelward, 2012, Functional & Integrative Genomics.

SVM-RFE peak selection for cancer classification with mass spectrometry data

Jagath C. Rajapakse, Kaibo Duan, Jagath Rajapakse, 2005, APBC.

Extraction of Fuzzy Features for Detecting Brain Activation from Functional MR Time-Series

Juan Zhou, Jagath C. Rajapakse, J. Rajapakse, 2006, ICONIP.

Efficient spectral analysis of Fourier transform mass spectrometry (FTMS) data

S. Abeysekera, K. Duan, Jagath Rajapakse, 2004, IEEE International Workshop on Biomedical Circuits and Systems, 2004..

Fuzzy-region-segmentation

J. Rajapakse, Jagath Rajapakse, H. Ong, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

Segmentation of subcortical brain structures using fuzzy templates

Juan Zhou, Jagath C. Rajapakse, Juan Zhou, 2005, NeuroImage.

Prediction of protein relative solvent accessibility with a two‐stage SVM approach

Jagath C Rajapakse, Minh N Nguyen, M. N. Nguyen, 2005, Proteins.

Pulse-modulated second harmonic imaging microscope quantitatively demonstrates marked increase of collagen in tumor after chemotherapy.

Hanry Yu, Jagath C Rajapakse, Shuoyu Xu, 2010, Journal of biomedical optics.

Identifying Cells in Histopathological Images

Jagath C. Rajapakse, Jierong Cheng, Merlin Veronika, 2010, ICPR Contests.

Sub-population analysis based on temporal features of high content images

Roy E. Welsch, Jagath C. Rajapakse, James Evans, 2009, BMC Bioinformatics.

Joint Tracking of Cell Morphology and Motion

Jagath C. Rajapakse, Jierong Cheng, Sohail Ahmed, 2009, PRIB.

Decoding Task States by Spotting Salient Patterns at Time Points and Brain Regions

Jagath C. Rajapakse, Sukrit Gupta, Yi Hao Chan, 2020, MLCN/RNO-AI@MICCAI.

Two‐stage support vector regression approach for predicting accessible surface areas of amino acids

Jagath C Rajapakse, Minh N Nguyen, J. Rajapakse, 2006, Proteins.

One-Versus-One and One-Versus-All Multiclass SVM-RFE for Gene Selection in Cancer Classification

Jagath C. Rajapakse, Minh Ngoc Nguyen, Kaibo Duan, 2007, EvoBIO.

GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings

Jagath C. Rajapakse, Xiaoshi Zhong, Rama Kaalia, 2019, BMC Genomics.

Discovering Power Laws in Entity Length pdf

Erik Cambria, Jagath C. Rajapakse, Xiaoshi Zhong, 2018, ArXiv.

Gene Ontology Enrichment Improves Performances of Functional Similarity of Genes

Jagath C. Rajapakse, Wenting Liu, Jianjun Liu, 2018, Scientific Reports.

NURBS-based visualization of age-related diversity in cortical morphology

Ravinda G. N. Meegama, Jagath C. Rajapakse, R. Meegama, 2006, J. Vis..

NURBS snakes

Ravinda G. N. Meegama, Jagath C. Rajapakse, R. Meegama, 2003, Image Vis. Comput..

Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma

J. Rajapakse, R. Kaalia, Conghao Wang, 2022, Scientific Reports.

Two-stage support vector machines to protein relative solvent accessibility prediction

Jagath C. Rajapakse, Minh Ngoc Nguyen, M. N. Nguyen, 2004, 2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

Two-Stage SVR Approach for Predicting Accessible Surface Areas of Amino Acids

J. Rajapakse, M. N. Nguyen, Jagath Rajapakse, 2005 .

Graphical approach for motif recognition in DNA sequences

Xiao Yang, Jagath C. Rajapakse, X. Yang, 2004, 2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

Graphical approach to weak motif recognition.

Jagath C Rajapakse, X. Yang, Xiao Yang, 2004, Genome informatics. International Conference on Genome Informatics.

Markov encoding for detecting signals in genomic sequences

Jagath C. Rajapakse, Loi Sy Ho, Jagath Rajapakse, 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Pattern Recognition in Bioinformatics: An Introduction

Limsoon Wong, Raj Acharya, Jagath C. Rajapakse, 2006, PRIB.

DrDimont: explainable drug response prediction from differential analysis of multi-omics networks

F. Azuaje, J. Rajapakse, B. Renard, 2022, bioRxiv.

Extracting rate changes in transcriptional regulation from MEDLINE abstracts

Kuiyu Chang, Guangxia Li, Jagath C. Rajapakse, 2014, BMC Bioinformatics.

Fuzzy approach to incorporate hemodynamic variability and contextual information for detection of brain activation

Juan Zhou, Jagath C. Rajapakse, Juan Zhou, 2008, Neurocomputing.

Modeling hemodynamic variability with fuzzy features for detecting brain activation from fMR time-series

Juan Zhou, Jagath C. Rajapakse, Juan Zhou, 2007, Neural Computing and Applications.

An exploratory approach to modelling effective connectivity

Jagath C. Rajapakse, Kanyan Yang, Jagath Rajapakse, 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

ICA Gives Higher-Order Functional Connectivity of Brain

Jagath C. Rajapakse, Kanyan Yang, Jagath Rajapakse, 2004 .

Toward Better Understanding of Protein Secondary Structure: Extracting Prediction Rules

Jacek M. Zurada, Jagath C. Rajapakse, Minh Ngoc Nguyen, 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Gene and sample selection using T-score with sample selection

Jagath C. Rajapakse, Piyushkumar A. Mundra, P. Mundra, 2016, J. Biomed. Informatics.

Marshall-Olkin power-law distributions in length-frequency of entities

Xiaoshi Zhong, Erik Cambria, Jagath Rajapakse, 2018, Knowl. Based Syst..

Quantitative magnetic resonance imaging of human brain development: ages 4-18.

B. J. Casey, J. Rapoport, F. Castellanos, 1996, Cerebral cortex.

Decoding task specific and task general functional architectures of the brain

Sukrit Gupta, Jagath Rajapakse, M. Lim, 2022, Human brain mapping.

Decoding brain functional connectivity implicated in AD and MCI

Jagath C. Rajapakse, Sukrit Gupta, Yi Hao Chan, 2019, bioRxiv.

Named Entity Analysis and Extraction with Uncommon Words pdf

Erik Cambria, Jagath C. Rajapakse, Xiaoshi Zhong, 2018, ArXiv.

Adaptive blind signal and image processing: learning algorithms and applications: A. Cichocki, S. Amari, Wiley, New York, 2002, 586pp., ISBN 0471 60791 6

Jagath C. Rajapakse, Jagath Rajapakse, 2002, Neurocomputing.

Validation of Gene Regulatory Networks from Protein-Protein Interaction Data: Application to Cell-Cycle Regulation

Jagath C. Rajapakse, Meena Kishore Sakharkar, Iti Chaturvedi, 2007, PRIB.

Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models [version 2; peer review: 1 approved, 2 approved with reservations]

F. Azuaje, Katharina Baum, Jagath Rajapakse, 2021 .

Discriminative Adversarial Learning for Speaker Independent Emotion Recognition

G. Huang, Zhiping Lin, Jagath Rajapakse, 2022, INTERSPEECH.

Cascaded Adversarial Learning for Speaker Independent Emotion Recognition

Chamara Kasun Liyanaarachchi Lekamalage, G. Huang, Zhiping Lin, 2022, 2022 International Joint Conference on Neural Networks (IJCNN).

Learning functional structure from fMR images

Jagath C. Rajapakse, Xuebin Zheng, Xuebin Zheng, 2006, NeuroImage.

MR image segmentation and tissue metabolite contrast in 1H spectroscopic imaging of normal and aging brain

Gioacchino Tedeschi, Jagath Rajapakse, Nina Lundbom, 1999, Magnetic resonance in medicine.

Levodopa and the feedback process on set‐shifting in parkinson's disease

J. Rajapakse, Juan Zhou, Y. Sitoh, 2011, Human brain mapping.

Wavelet analyses of event-related fMRI experiments

Keng Wah Choo, K. W. Choo, J. C. Rajapakse, 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

Graph Neural Networks With Multiple Prior Knowledge for Multi-Omics Data Analysis

Conghao Wang, Shiping Wang, Shunxin Xiao, 2023, IEEE journal of biomedical and health informatics.

Stability of building gene regulatory networks with sparse autoregressive models

Jagath C. Rajapakse, Piyushkumar A. Mundra, P. Mundra, 2011, BMC Bioinformatics.

Elucidating salient site-specific functional connectivity features and site-invariant biomarkers in schizophrenia via deep neural networks

Jagath Rajapakse, Q. Chew, Kang Sim, 2023, Scientific reports.

Sequential Modelling for Medical Image Segmentation

Jagath Rajapakse, Chun Hung How, 2023, bioRxiv.

Different hemodynamic responses in Chinese, Romanised Chinese and English reading tasks

Choong Leong Tan, J. Rajapakse, Y. Sitoh, 2004, IEEE International Workshop on Biomedical Circuits and Systems, 2004..

Bayesian approach to segmentation of statistical parametric maps

Jagath C. Rajapakse, Jayasanka Piyaratna, Jagath Rajapakse, 2001, IEEE Transactions on Biomedical Engineering.

Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics

E. Marchiori, J. Moore, Jagath Rajapakse, 2007 .

Predicting gender from structural and functional connectomes via brain and population graph neural networks

Jagath Rajapakse, Yi Hao Chan, Yinan He, 2023, bioRxiv.

Combining Semantic Similarity and GO Enrichment for Computation of Functional Similarity

Jianjun Liu, Jagath C. Rajapakse, Wenting Liu, 2017, bioRxiv.

Iterative consensus spectral clustering improves detection of subject and group level brain functional modules

Jagath C. Rajapakse, Sukrit Gupta, Sukrit Gupta, 2020, Scientific Reports.

Cerebral MRI of human brain development: Ages 4–18

J. Rapoport, F. Castellanos, J. Giedd, 1995, Biological Psychiatry.

Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation

Jagath Rajapakse, Chung-Soo Ahn, Rajib Rana, 2023, 2310.04703.

Phonological processing in Chinese–English bilingual biscriptals: An fMRI study

Tan Choong Leong, W. Tham, S. E. Ng, 2004, NeuroImage.

Phonological Processing in Chinese–english Bilingual Biscriptals: an Fmri Study

Jagath C. Rajapakse, Wendy W. P. Tham, Susan J. Rickard Liow, 2005 .

Multi-level Knowledge Integration with Graph Convolutional Network for Cancer Molecular Subtype Classification

Shiping Wang, Shunxin Xiao, Jagath Rajapakse, 2023, 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Extracting decision rules in prediction of protein secondary structure

Jacek M. Zurada, Jagath C. Rajapakse, Minh Ngoc Nguyen, 2008, 2008 8th IEEE International Conference on BioInformatics and BioEngineering.

Two-Stage Multi-Class Support Vector Machines to Protein Secondary Structure Prediction

Jagath C. Rajapakse, Minh Ngoc Nguyen, M. N. Nguyen, 2004, Pacific Symposium on Biocomputing.

Inferring Time-Delayed Gene Regulatory Networks Using Cross-Correlation and Sparse Regression

Roy E. Welsch, Mahesan Niranjan, Jagath C. Rajapakse, 2013, ISBRA.

PHOTON REASSIGNMENT FOR WIDE-FIELD STRUCTURED ILLUMINATION MICROSCOPY

P. So, J. Rajapakse, V. Singh, 2012 .

Tree-structured algorithm for long weak motif discovery

Malcolm Yoke Hean Low, H. Sun, W. Hsu, 2011, Bioinform..

Simultaneous Sample and Gene Selection Using T-score and Approximate Support Vectors

Jagath C. Rajapakse, Piyushkumar A. Mundra, D. A. K. Maduranga, 2013, PRIB.

Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach

Francisco Azuaje, Petr V Nazarov, Jagath C Rajapakse, 2018, Biology Direct.

Scale-space analysis of event-related fMRI data using higher harmonics

J. Rajapakse, Arun Kumar, Jagath Rajapakse, 2011, 2011 Defense Science Research Conference and Expo (DSR).

Neuronal and Hemodynamic Events from fMRI Time-Series

Frithjof Kruggel, Jagath C. Rajapakse, D. Yves von Cramon, 1998, J. Adv. Comput. Intell. Intell. Informatics.