Publikācijas:
- E.Celms, K.Cerans, K.Freivalds, P.Kikusts, L.Lace, G.Melkus, M.Opmanis, D.Rituma, P.Rucevskis, J.Viksna. Application of graph clustering and visualisation methods to analysis of biomolecular data. Communications in Computer and Information Science 838, 243-257, 2018.
- K.Freivalds, R.Liepins. Improving the neural GPU architecture for algorithm learning. Proceedings of NAMPI: Neural Abstract Machines & Program Induction, 2018.
- L.Lace, G.Melkus, P.Rucevskis, D.Ruklisa, E.Celms, K.Cerans, P.Kikusts, M.Opmanis, D.Rituma, J.Viksna. Graph-based characterisations of cell types and functionally related modules in promoter capture Hi-C data. Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3 BIOINFORMATICS: BIOINFORMATICS, 78-89, 2019.
- M.Opmanis. About correctness of graph-based social network analysis. Baltic Journal of Modern Computing 7(2), 271-292, 2019.
- D.Ruklisa, A.Brazma, K.Cerans, T.Schlitt, J.Viksna. Dynamics of gene regulatory network sand their dependence on network topology and quantitative parameters – the case of phage λ. BMC Bioinformatics, 20:296, 2019.
- J.Viksna, G.Melkus, E.Celms, K.Cerans, K.Freivalds, P.Kikusts, L.Lace, M.Opmanis, D.Rituma, P.Rucevskis. Topological structure analysis of chromatin interaction networks. BMC Bioinformatics, 20(Suppl 23):618, 2019.
- L.Lace, G.Melkus, P.Rucevskis, E.Celms, K.Cerans, P.Kikusts, M.Opmanis, D.Rituma, J.Viksna. Characteristic topological features of promoter capture Hi-C interaction networks. Communications in Computer and Information Science. (Accepted for publication)
- G.Melkus, P.Rucevskis, E.Celms, K.Cerans, K.Freivalds, P.Kikusts, L.Lace, M.Opmanis, D.Rituma, J.Viksna. Graph-based network analysis of transcriptional regulation pattern divergence in duplicated yeast gene pairs. ACM International Conference Proceeding Series. (Accepted for publication)
Konferenču prezentācijas:
- J.Viksna, M.Barzine, A.Brazma, E.Celms, K.Cerāns, J.Choudhary, N.Fonseca, K.Freivalds, F.Ghavidel, A.Jarnuczak, L.Lace, D.Rituma, J.Viksna. Deep learning for protein abundance prediction using Gene Ontology and RNA abundance information. RECOMB 2018.
- E.Celms, K.Cerans, K.Freivalds, P.Kikusts, L.Lace, G.Melkus, M.Opmanis, D.Rituma, P.Rucevskis, J.Viksna. Application of graph clustering and visualisation methods to analysis of biomolecular data. DB&IS 2018.
- J.Viksna, M.Barzine, A.Brazma, E.Celms, K.Cerāns, J.Choudhary, N.Fonseca, K.Freivalds, F.Ghavidel, A.Jarnuczak, L.Lace, D.Rituma, J.Viksna An integrated approach to missing data imputation in quantitative proteomics experiments. ECCB 2018.
- P.Rucevskis, M.Opmanis, P.Kikusts, E.Celms, L.Lace, G.Melkus, J.Viksna, D.Ruklisa. Predicting functionally related modules in promoter capture Hi-C data. ECCB 2018.
- L.Lace, G.Melkus, P.Rucevskis, E.Celms, K.Cerans, P.Kikusts, M.Opmanis, D.Rituma, J.Viksna. Graph-based characterisations of cell types and functionally related modules in promoter capture Hi-C data. BIOINFORMATICS/BIOSTEC 2019.
- G.Melkus, P.Rucevskis, E.Celms, K.Cerans, P.Kikusts, L.Lace, M.Opmanis, D.Rituma, J.Viksna. Topological structure analysis of Hi-C interaction graphs. ISMB/ECCB 2019.
- G.Melkus, P.Rucevskis, E.Celms, K.Cerans, K.Freivalds, P.Kikusts, L.Lace, M.Opmanis, D.Rituma, J.Viksna. Graph-based network analysis of transcriptional regulation pattern divergence in duplicated yeast gene pairs. ISMB/ECCB 2019.
- G.Melkus, P.Rucevskis, E.Celms, K.Cerans, K.Freivalds, P.Kikusts, L.Lace, M.Opmanis, D.Rituma, J.Viksna. Graph-based network analysis of transcriptional regulation pattern divergence in duplicated yeast gene pairs. CSBio 2019.
- J.Viksna, G.Melkus, E.Celms, K.Cerans, K.Freivalds, P.Kikusts, L.Lace, M.Opmanis, D.Rituma, P.Rucevskis. Topological structure analysis of chromatin interaction networks. GIW/ABACBS 2019.
Projekta ietvaros izstrādātā programmatūra un sagatavotās kopas ir publicēta GitHub repozitorijos: