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.
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
Dr 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.
Keywords: eye tracking, facial recognition, fMRI, neuropsychology, human-computer interaction research
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).
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
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)”.
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
Līvija Bārdiņa studied Bioinformatics in Munich (Germany) and her thesis was dedicated to methods detecting digenic disease genes.
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