Academic Profile : Faculty
Prof Jagath Chandana Rajapakse
Professor, College of Computing & Data Science
Email
Journal Articles
(Not applicable to NIE
staff as info will be
pulled from PRDS)
(Not applicable to NIE
staff as info will be
pulled from PRDS)
1. C. Wang, L. W. Lue, R. Kaalia, P. Kumar, and J.C. Rajapakse, “Network-based integration of multi-omics data for clinical outcome prediction of neuroblastoma,” Scientific Reports , 2022, 12:15425, (IF = 4.992 ), DOI: nature.com/articles/s41598-022-19019-5
2. P. Hiort, J. Hugo, J. Zeinert, N. Muller, S. Kashyap, J. C. Rajapakse, F. Azhuaje, B. Y. Renard, and K. Baum, “DrDimont: explainable drug response prediction from differential analysis of multi-omics networks,” Bioinformatics , Vol. 38, Supplement 2, September 2022, Pages ii113-ii119, IF = 6.931 DOI:10.1093/bioinformatics/btac477
3. Y. H. Chan, C. Wang, W. K. Soh, and J. C. Rajapakse, “Combining neuroimaging and omics datasets for disease classification using graph neural networks,” Frontiers of Neuroscience, IF = 4.677, 23 May 2022, DOI: https://doi.org/10.3389/fnins.2022.866666
4. Y. H. Chan, W. C. Yew, J. C. Rajapakse, (2022). Semi-supervised Learning with Data Harmonisation for Biomarker Discovery from Resting State fMRI. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. Lecture Notes in Computer Science, vol 13431: 441-451, September 2022, Springer, DOI: 10.1007/978-3-031-16431-6_42
5. S. Gupta, M. Lim, and J. C. Rajapakse, “Decoding task-specific and task-general architectures of the brain,” Human Brain Mapping , 43: 2801 - 2816, IF = 4.554, February 2022, DOI: onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.25817
6. C. Wang, X. Lye, R. Kaalia, P. Kumar, and J. C. Rajapakse, “Deep learning and multi-omics approach to predict drug responses in cancer,” BMC Bioinformatics, 22:632, IF = 3.169, 10.1186/s12859-022-04964-9 , May 2021
7. S. Gupta, Y. H. Chan, and J. C. Rajapakse, “Obtaining leaner deep neural networks for decoding brain functional connectome in a single shot,” Neurocomputing, January, 2021, DOI: 10.1016/j.neucom.2020.04.152, IF = 4.438
8. X. Zhong and J. C. Rajapakse, “Graph embeddings on gene ontology annotations for protein-protein interaction prediction,” BMC Bioinformatics, 21, 516, December 2020, DOI: 10.1186/s12859-020-03816-8
9. S. Gupta and J. C. Rajapakse, “Iterative consensus spectral clustering improves detection of subject and group level brain functional modules,” Scientific Reports, 10: 7590, May 2020, DOI:/10.1038/s41598-020-63552-0, IF = 4.011
10. S. Gupta, J. C. Rajapakse, and R. E. Welsch, “Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer’s disease and autism spectrum disorder,” NeuroImage: Clinical , Volume 25, 102186, Jan 2020, DOI: /10.1016/j.nicl.2020.102186 , IF = 3.943
11. L.-C. Tranchevent, F. Azuaje, and J. C. Rajapakse, “A deep neural network approach to predicting clinical outcomes of neuroblastoma patients,” BMC Medical Genomics, 12, 178, Dec 2019, DOI: 10.1186/s12920-019-0628-y , IF = 3.317
12. R. Kaalia and J.C. Rajapakse, “Refining modules to determine functionally significant clusters in molecular networks,” BMC Genomics, 20: 901, Dec 2019, DOI: 10.1186/s12864-019-6294-9 , IF = 3.730
13. X. Zhong, R. Kaalia, and J. C. Rajapakse, “GO2Vec: transforming GO terms and proteins to vector representations using graph embeddings” BMC Genomics, 20: 918, Dec 2019, DOI: 10.1186/s12864-019-6272-2, IF = 3.730
14. S. Gupta, Y. H. Chen, and J. C. Rajapakse “Decoding brain functional connectivity implicated in AD and MCI,” Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Lecture Notes in Computer Science, LNCS 11766, pp. 781–789, 2019, DOI: 10.1007/978-3-030-32248-9_87
15. K. Baum, J. C. Rajapakse, and F. Azuaje, “Analysis of correlation-based molecular networks from different omics data by fitting stochastic block models,” F1000Research, 8: 465, Aug 2019, DOI: 10.12688/f1000research.18705.2
16. W. Liu and J. C. Rajapakse, “Fusing gene expressions and transitive protein interactions for inference of gene regulatory networks,” BMC Systems Biology, 13(Suppl 2): 37, April 2019, DOI: 10.1186/s12918-019-0695-x , IF = 2.05
17. A. N. Barrett, C. Y. Fong, A. Subramanian, W. Liu, Y. Feng, M. Choolani, A. Biswas, J. C. Rajapakse, and A. Bongso, “Human Wharton’s jelly mesenchymal stem cells show unique expressions compared with bone marrow mesenchymal stem cells using single-cell RNA sequencing,” Stem Cells and Development, 28(3), Feb 2019, DOI: 10.1089/scd.2018.0132, IF = 3.315
18. R. Kaalia and J. C. Rajapakse, “Functional homogeneity and specificity of topological modules in human proteome,” BMC Bioinformatics , 19: 553, Feb 2019, DOI: 10.1186/s12859-018-2549-8, IF = 2.511
19. X. Sui and J. C. Rajapakse, “Profiling heterogeneity of Alzheimer’s disease using white matter impairment factors,” Neuroimage: Clinical , 20, pp. 1222 – 1232, Oct 2018, DOI: 10.1016/j.nicl.2018.10.026 , IF = 4.348
20. W. Liu, J. Liu, and J. C. Rajapakse, “Gene ontology enrichment improves performances of functional similarity of genes,” Scientific Reports , 8: 12100, Aug 2018, DOI: 10.1038/s41598-018-30455-0 ,IF = 4.122
21. D. N. Wadduwage, J. Kay, V. R. Singh, O. Kiraly, M. R. Sukup-Jackson, J. C. Rajapakse, B. P. Engelward, and P. T. C. So, “Automated fluorescence intensity and gradient analysis enables detection of rare fluorescent mutant cells deep within the tissue of RaDR mice,” Scientific Reports , 8:12108, Aug 2018, DOI: 10.1038/s41598-018-30557-9, IF = 4.122,
22. L.-C. Tranchevent, P. V. Nazarov, T. Kaoma, G. P. Schmartz, A. Muller, S.-Y. Kim, J. C. Rajapakse, and F. Azuaje, “Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach,” Biology Direct , 13:12, June 2018, DOI: 10.1186/s13062-018-0214-9 , IF = 2.649
2. P. Hiort, J. Hugo, J. Zeinert, N. Muller, S. Kashyap, J. C. Rajapakse, F. Azhuaje, B. Y. Renard, and K. Baum, “DrDimont: explainable drug response prediction from differential analysis of multi-omics networks,” Bioinformatics , Vol. 38, Supplement 2, September 2022, Pages ii113-ii119, IF = 6.931 DOI:10.1093/bioinformatics/btac477
3. Y. H. Chan, C. Wang, W. K. Soh, and J. C. Rajapakse, “Combining neuroimaging and omics datasets for disease classification using graph neural networks,” Frontiers of Neuroscience, IF = 4.677, 23 May 2022, DOI: https://doi.org/10.3389/fnins.2022.866666
4. Y. H. Chan, W. C. Yew, J. C. Rajapakse, (2022). Semi-supervised Learning with Data Harmonisation for Biomarker Discovery from Resting State fMRI. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. Lecture Notes in Computer Science, vol 13431: 441-451, September 2022, Springer, DOI: 10.1007/978-3-031-16431-6_42
5. S. Gupta, M. Lim, and J. C. Rajapakse, “Decoding task-specific and task-general architectures of the brain,” Human Brain Mapping , 43: 2801 - 2816, IF = 4.554, February 2022, DOI: onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.25817
6. C. Wang, X. Lye, R. Kaalia, P. Kumar, and J. C. Rajapakse, “Deep learning and multi-omics approach to predict drug responses in cancer,” BMC Bioinformatics, 22:632, IF = 3.169, 10.1186/s12859-022-04964-9 , May 2021
7. S. Gupta, Y. H. Chan, and J. C. Rajapakse, “Obtaining leaner deep neural networks for decoding brain functional connectome in a single shot,” Neurocomputing, January, 2021, DOI: 10.1016/j.neucom.2020.04.152, IF = 4.438
8. X. Zhong and J. C. Rajapakse, “Graph embeddings on gene ontology annotations for protein-protein interaction prediction,” BMC Bioinformatics, 21, 516, December 2020, DOI: 10.1186/s12859-020-03816-8
9. S. Gupta and J. C. Rajapakse, “Iterative consensus spectral clustering improves detection of subject and group level brain functional modules,” Scientific Reports, 10: 7590, May 2020, DOI:/10.1038/s41598-020-63552-0, IF = 4.011
10. S. Gupta, J. C. Rajapakse, and R. E. Welsch, “Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer’s disease and autism spectrum disorder,” NeuroImage: Clinical , Volume 25, 102186, Jan 2020, DOI: /10.1016/j.nicl.2020.102186 , IF = 3.943
11. L.-C. Tranchevent, F. Azuaje, and J. C. Rajapakse, “A deep neural network approach to predicting clinical outcomes of neuroblastoma patients,” BMC Medical Genomics, 12, 178, Dec 2019, DOI: 10.1186/s12920-019-0628-y , IF = 3.317
12. R. Kaalia and J.C. Rajapakse, “Refining modules to determine functionally significant clusters in molecular networks,” BMC Genomics, 20: 901, Dec 2019, DOI: 10.1186/s12864-019-6294-9 , IF = 3.730
13. X. Zhong, R. Kaalia, and J. C. Rajapakse, “GO2Vec: transforming GO terms and proteins to vector representations using graph embeddings” BMC Genomics, 20: 918, Dec 2019, DOI: 10.1186/s12864-019-6272-2, IF = 3.730
14. S. Gupta, Y. H. Chen, and J. C. Rajapakse “Decoding brain functional connectivity implicated in AD and MCI,” Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Lecture Notes in Computer Science, LNCS 11766, pp. 781–789, 2019, DOI: 10.1007/978-3-030-32248-9_87
15. K. Baum, J. C. Rajapakse, and F. Azuaje, “Analysis of correlation-based molecular networks from different omics data by fitting stochastic block models,” F1000Research, 8: 465, Aug 2019, DOI: 10.12688/f1000research.18705.2
16. W. Liu and J. C. Rajapakse, “Fusing gene expressions and transitive protein interactions for inference of gene regulatory networks,” BMC Systems Biology, 13(Suppl 2): 37, April 2019, DOI: 10.1186/s12918-019-0695-x , IF = 2.05
17. A. N. Barrett, C. Y. Fong, A. Subramanian, W. Liu, Y. Feng, M. Choolani, A. Biswas, J. C. Rajapakse, and A. Bongso, “Human Wharton’s jelly mesenchymal stem cells show unique expressions compared with bone marrow mesenchymal stem cells using single-cell RNA sequencing,” Stem Cells and Development, 28(3), Feb 2019, DOI: 10.1089/scd.2018.0132, IF = 3.315
18. R. Kaalia and J. C. Rajapakse, “Functional homogeneity and specificity of topological modules in human proteome,” BMC Bioinformatics , 19: 553, Feb 2019, DOI: 10.1186/s12859-018-2549-8, IF = 2.511
19. X. Sui and J. C. Rajapakse, “Profiling heterogeneity of Alzheimer’s disease using white matter impairment factors,” Neuroimage: Clinical , 20, pp. 1222 – 1232, Oct 2018, DOI: 10.1016/j.nicl.2018.10.026 , IF = 4.348
20. W. Liu, J. Liu, and J. C. Rajapakse, “Gene ontology enrichment improves performances of functional similarity of genes,” Scientific Reports , 8: 12100, Aug 2018, DOI: 10.1038/s41598-018-30455-0 ,IF = 4.122
21. D. N. Wadduwage, J. Kay, V. R. Singh, O. Kiraly, M. R. Sukup-Jackson, J. C. Rajapakse, B. P. Engelward, and P. T. C. So, “Automated fluorescence intensity and gradient analysis enables detection of rare fluorescent mutant cells deep within the tissue of RaDR mice,” Scientific Reports , 8:12108, Aug 2018, DOI: 10.1038/s41598-018-30557-9, IF = 4.122,
22. L.-C. Tranchevent, P. V. Nazarov, T. Kaoma, G. P. Schmartz, A. Muller, S.-Y. Kim, J. C. Rajapakse, and F. Azuaje, “Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach,” Biology Direct , 13:12, June 2018, DOI: 10.1186/s13062-018-0214-9 , IF = 2.649