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 personalised 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
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
Egija Berga-Švītiņa specialises in personalised medicine, with a focus on cancer genetics and other complex disorders. During her PhD, she investigated genetic risk factors for breast and ovarian cancer using genome-wide association studies (GWAS) and polygenic risk score (PRS) calculations.
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In addition, she has extensive experience in multiple research projects and longstanding expertise in molecular diagnostics, including next-generation sequencing (NGS) data analysis (e.g., whole-exome sequencing (WES) and RNA sequencing (RNA-seq) data analysis) and their clinical interpretation, as well as copy-number variation (CNV) analysis through chromosomal microarray analysis (CMA). Currently, Egija works as a bioinformatician in the project Multidimensional mechanistic investigations of trans spinal direct current stimulation in motor neuron disease (DC4MND) (No. ES RTD/2023/18).
Keywords: genome-wide association studies (GWAS), next-generation sequencing (NGS) data analysis and clinical interpretation, polygenic risk score (PRS), cancer genetics
ORCID 0000-0001-5150-0185
LinkedIn: egija-berga-švītiņa-383225b6
egija[pnkts]berga-svitina
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
Elita holds a Master's degree in Biomedicine from Rīga Stradiņš University (RSU).
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She is currently engaged in a research project The Role of Clonal Hematopoiesis of Indeterminate Potential as a Driver of Cardiovascular Diseases and Its Association with Clinical Outcomes. In collaboration with cardiologists, she is investigating the genetic factors that contribute to the development of coronary artery disease, utilising polygenic risk score calculations.
Keywords: Coronary artery disease(CAD), polygenic risk score (PRS), genome-wide association studies (GWAS)
elita[pnkts]ozola
rsu[pnkts]lv
Edīte Vārtiņa, MD, PhD, has over 10 years of experience in medicine and cardiac surgery. She earned her PhD in Basic Medical Sciences from Rīga Stradiņš University (RSU) in 2021.
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Currently, Edīte is pursuing a Master's degree in Biostatistics at RSU, and her primary interests lie in research data, software, and workflow management.
Keywords: biostatistics, research output management, FAIR principles
ORCID: 0000-0002-9639-2883
LinkedIn: edite-vartina
edite[pnkts]vartina
rsu[pnkts]lv
Viktorija Daukšaitė recently earned her master’s degree in Systems Biology from Vilnius University. She has experience in transcriptomics and clinical data analysis, focusing on miRNA expression profiles and their association with disease mechanisms.
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Viktorija’s primary research involves analysing miRNA datasets to identify potential biomarkers and regulatory roles in disease. She is currently working on a project investigating the molecular differences between early-onset and late-onset coronary artery disease (CAD). Her work aims to integrate transcriptomic data with clinical outcomes to uncover novel biomarkers and better understand the progression of CAD.
Keywords: transcriptomic data analysis, miRNA, clinical data analysis, bioinformatics, Systems Biology
ORCID 0009-0002-8205-3730
LinkedIn: viktorija-daukšaitė-274178292
viktorija[pnkts]dauksaite
rsu[pnkts]lv
Marta Krūmiņa’s research interests are focused on computational neuroscience. Currently, Marta is working in the EU Joint Programme - Neurodegenerative Disease Research project Multidimensional mechanistic investigations of trans spinal direct current stimulation in motor neuron disease (DC4MND).
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Marta has an interdisciplinary academic background, having studied Physics at the University of Latvia and Philosophy of Science, Technology, and Society at the University of Twente. She recently completed her master's thesis in close collaboration with Dr. Sofia Rita Fernandes at the University of Lisbon, which involved computational modelling of the effects of external electric fields on motor neurons.
Keywords: computational neuroscience, neurodegenerative diseases
marta[pnkts]krumina
rsu[pnkts]lv
Akbar Abayev is a software engineer with expertise in DevOps and Cloud Engineering practices, specialising in bioinformatics and medical engineering. He has experience in developing and optimising workflows for high-throughput data analysis, utilising tools like WDL, Cromwell, and FastQC.
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With a strong command of high-performance computing (HPC) environments and containerisation technologies such as Docker and Singularity, he is skilled at building scalable and efficient data processing pipelines. In addition to his academic and professional achievements, Akbar has presented his scientific work related to Ultrasound Velocity and Shock Freezing for meat quality assessment at the FoodBalt conference in Tartu, Estonia. He also served as a Google Ambassador, where he contributed to various tech-focused initiatives and events. His work bridges technical innovation and computer science, fostering advancements in the field.
Keywords: DevOps, cloud engineering, high-performance computing, automated computational workflows, containerisation
GitHub: MedicalEnvironment
LinkedIn: akbar-abayev
akbar[pnkts]abayev
rsu[pnkts]lv
Georgy Lepsaya is a Computer Science student at the University of Latvia (UL). He is currently specialising in developing tools for the pre-processing and integrative analysis of clinical and multi-omics data, which encompasses genomic, transcriptomic, and proteomic information.
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In addition to his studies at UL and research work at the RSU Bioinformatics Group, Georgy has gained valuable experience in data engineering and research data management through a recent Internship at the Leibniz-Rechenzentrum in Germany.
Keywords: software engineering, research data management, clinical data analysis, multi-omics data pre-processing, data integration
GitHub: georgelepsaya
LinkedIn: georgy-lepsaya-063976239
georgy[pnkts]lepsaya
rsu[pnkts]lv
Computational Systems Biology Team
To find out more about the members of the Computational Systems Biology team, led by Dr.sc.ing. Egils Stalidzāns, visit the Computational Systems Biology Group website.
