J. Rajapakse

发表

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

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

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

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

Segmentation of MR images with intensity inhomogeneities

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

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

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

Cerebral magnetic resonance image segmentation using data fusion.

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

Markov Encoding for Detecting Signals in Genomic Sequences

J. Rajapakse, L. Ho, 2005, TCBB.

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

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

Markov/Neural Model for Eukaryotic Promoter Recognition

J. Rajapakse, Sy-Loi Ho, 2008 .

FPGA Implementations of Neural Networks

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

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

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

Prediction of protein secondary structure using Bayesian method and support vector machines

J. Rajapakse, M. N. Nguyen, 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

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.

Quantitative Magnetic Resonance Imaging of Human Brain Development: Ages 4–18

Jagath C. Rajapakse, Jay N. Giedd, B. J. Casey, 1996 .

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.

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.

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.

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.

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).

Gene Classification Using Codon Usage and Support Vector Machines

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

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.

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

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

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.

Toward surface quantification of liver fibrosis progression.

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

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.

Sexual dimorphism of the developing human brain

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

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.

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

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

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

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

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.

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.

Combining Semantic Similarity and GO Enrichment for Computation of Functional Similarity

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

Gene Ontology Enrichment Improves Performances of Functional Similarity of Genes

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

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.

Cerebral and hippocampal volumetry in early Alzheimer's disease

Jagath C. Rajapakse, Yih-Yian Sitoh, J. Karthik, 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

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

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

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.

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).

Computer architecture, digital systems and parallel processing

Anil Kumar Gupta, Ling Li, S. C. Hui, 2001 .

Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics

Bertil Schmidt, Jagath C. Rajapakse, Gwenn Volkert, 2006 .

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

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

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

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

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

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

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

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

PHOTON REASSIGNMENT FOR WIDE-FIELD STRUCTURED ILLUMINATION MICROSCOPY

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

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

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

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

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

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.

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

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

Cerebral MRI of human brain development: Ages 4–18

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

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 .

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

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