Staff
Dr Vilne has more than 13 years of experience in bioinformatics and its application in biomedicine, analysing genomic data (arrays, WES/WGS) transcriptome and microbiome data.
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Her major focus has been integrative multi-omics analysis for personalized medicine, starting from pair-wise integrations (e.g., expression quantitative trait loci; eQTLs) and re-construction of gene co-expression networks/modules, linking those to the life-style and environment data to the application of artificial intelligence/machine learning approaches, mainly in the context of coronary artery disease.
Keywords: multi-omics data integration, artificial intelligence/machine learning, genome, genome-wide association studies, transcriptome, microbiome, clinical and lifestyle data analyses, disease risk prediction, mitochondria, coronary artery disease
ORCID 0000-0002-1084-7067
LinkedIn: baiba-vilne-4427221a
baiba[pnkts]vilne
rsu[pnkts]lv
Dr Stalidzans has more than 15 years experience in systems biology. Main research focus has been on modeling and optimisation of genome-scale stoichiometric models and pathway-scale kinetic models in different organisms.
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His major focus has been on revealing mechanisms of complex processes by building mathematical models of interactions between systems elements to replicate the experimentally observed behavior of system of interest. These activities have been performed mostly on metabolism and physiology based pharmacokinetic processes.
He has a co-authored of COBRA v3.0 toolbox for constraint-based stoichiometric modeling.
Keywords: systems biology, precision medicine, mechanistic modeling, metabolism, ordinary differential equations, genome scale models, physiology based pharmacokinetic models
ORCID 0000-0001-6063-0184
LinkedIn: egils-stalidzans-b5bbb729/
egils[pnkts]stalidzans
rsu[pnkts]lv
rsu [pts] lvDr Takemoto's current research interests include to better understand communication, human emotion, and cognition/attention, combined with monitoring brain activity, facial expressions, and gaze patterns using eye tracking, face recognition, virtual assistant communication, functional magnetic resonance imaging (fMRI) and machine learning technology. She is actively involved in several research topics, such as detecting of mental/cognitive disorders for the development of support systems.
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Keywords: eye tracking, facial recognition, fMRI, neuropsychology, human-computer interaction research
ORCID 0000-0003-0998-773X
LinkedIn: ayutakemo
ayumi[pnkts]takemoto
rsu[pnkts]lv
Currently Neiburga performs mRNA analysis in the project Predominantly primary antibody deficiencies among adults: solving etiology and causes of clinical variability (No. lzp-2020/1-0269).
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In addition, Neiburga is involved in multiple other bioinformatics related projects such as identifying miRNA variation in coronary artery disease and linking it to genomic information in collaboration with Deutsches Herzzentrum München Klinik für Herz- und Kreislauferkrankungen, identifying hypoxia associated biomarkers in HER2+ breast cancer cell lines, and identifying bacterial vs viral infection biomarkers in children with fever by transcriptome analysis in urine.
Previously, Neiburga has also worked on translational profiling of neuronal cells and mathematical modelling of plant metabolic pathways.
Keywords: transcriptome analysis, miRNA, mRNA
ORCID 0000-0002-4731-7596
LinkedIn: katrīna-neiburga-9a3583150
KatrinaDaila[pnkts]Neiburga
rsu[pnkts]lv
Sawant is a Visiting Researcher currently mainly working on the ERA PerMed funded project “PRecisiOn medicine in CAD patients: artificial intelliGence for integRated gEnomic, functional and anatomical aSSessment of the coronary collateral circulation (PROGRESS)”.
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He is also involved in other bioinformatics projects like “Using Machine Learning to Model the Complex Interplay Between Diet, Genetic Factors and Mitochondria in Coronary Artery Disease”, where he is analysing different dietary and (mitochondrial) genetic factors trying to understand the effects such factors have on development of coronary artery disease.
Previously, Sawant has been involved in development of genomic pipelines and analysis of sequencing data (exome, whole genome sequencing and transcriptome data) acquired from samples of various tumour origins. Aniket was also involved in development of the Infectious Pathogen Detector (IPD) and the analysis of Retrotransposon expression in cancer patients.
Keywords: genome-wide association studies (GWAS), machine learning, NGS-data analysis, cancer, cardiovascular diseases, coronary collateral circulation
ORCID 0000-0002-9650-4566
LinkedIn: aniket-sawant-2bb22b14a
aniket[pnkts]sawant
rsu[pnkts]lv
MSc. Egija Berga-Švītiņa’s research interests are focused on personalized medicine. Currently, Egija is a PhD candidate at RSU where she performs genome-wide association studies (GWAS), polygenic risk score (PRS) calculations for breast and ovarian cancer patients.
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In addition, she has a practical experience in multiple research projects, including the Latvian Council of Science (LCS) and ERA-NET financed projects. E. Bergai-Švītiņai also has also a longstanding expertise in molecular diagnostics such as next-generation sequencing (NGS) library preparation, raw data pre-processing and interpretation; chromosomal microarray analysis (CMA) preparation, data handling and interpretation.
Keywords: genome-wide association studies (GWAS), polygenic risk score (PRS) calculations, next-generation sequencing (NGS) data analysis and interpretation, hereditary breast and ovarian cancer
ORCID 0000-0001-5150-0185
LinkedIn: egija-berga-švītiņa-383225b6
egija[pnkts]berga-svitina
rsu[pnkts]lv
Līvija Bārdiņa studied Bioinformatics in Munich (Germany) and her thesis was dedicated to methods detecting digenic disease genes.
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Currently, in cooperation with the Children's Clinical University Hospital, Bārdiņa is mainly involved in genomic data analysis with a focus on somatic and structural variation detection for clinical applications. In addition, she is actively working in two projects financed by the Latvian Council of Science (LCS) - "Discovering biomarkers of disease progression and variability in Charcot-Marie-Tooth neuropathy" (No. lzp-2021/1-0327, in cooperation with Asst. Prof. Ķēniņa), and "Elucidating comprehensive aetiology of cervical insufficiency to foster timely diagnosis of preterm delivery and prevent adverse outcomes in obstetrics” (No. lzp-2020/1-004, co-operation with Prof. Rezeberga).
Keywords: genomic data analysis, whole exome/genome sequencing data analysis, somatic variations, structural variations, gene-based association tests, clinical genomics
ORCID 0000-0002-8211-9798
livija[pnkts]bardina
rsu[pnkts]lv
Kristīna has studied information technology at the Latvia University of Life Sciences and Technologies.
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Research interests are related to human metabolic processes and their impact on health maintenance and disease development. Currently in collaboration with the Institute of Food Safety, Animal Health and Environment "BIOR" (consultant LU doctoral student Juris Ķibilds) Kristīna is working on her master's thesis, which involves the application of artificial intelligence and machine learning methods in the analysis of human gut microbiome data.
In addition, she has practical experience in other research projects, including Sustainable up-cycling of agricultural residues: modular cascading waste conversion system and Production potential of top building block chemicals by Zymomonas mobilis: stoichiometric analysis, where she was involved in stoichiometric modelling of metabolism for different organisms, software development for the integration of experimental data and result analysis.
Keywords: microbiome, artificial intelligence/machine learning, clinical and lifestyle data analyses, disease risk prediction, multi-omics data integration, genome-scale metabolic modelling
ORCID 0000-0002-3075-970X
kristina[pnkts]grausa
rsu[pnkts]lv
