Esa Pitkänen is a FIMM-EMBL Group Leader and Academy Research Fellow at the Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE) and the Applied Tumor Genomics Research Program in the Research Programs Unit, University of Helsinki.
I am enrolled in the Bioinformatics master's program at the University of Hamburg and joining Esa's group for my thesis. My project pursues a deep learning approach to detect DNA adducts from Nanopore sequencing data. I previously received a master's degree in Nutritional Sciences from Friedrich Schiller University in Jena, where I performed research on the repair of the base modification O6-methylguanine.
Daniyar earned his B.Sc. in Biology and M.Sc. in Molecular Medicine from Nazarbayev University, Kazakhstan. During his studies, he worked as bioinformatics technician, with his research focusing on genome assembly and the study of genetic variation in Kazakh population. In 2022, he was selected for the FIMM-EMBL rotation program, after which he joined as a PhD student under the supervision of the Pitkänen and Ganna groups. His PhD research is centered on early cancer prediction through the analysis of genetic and epigenetic patterns in cell-free DNA
Tua works within the data team of the iCAN Digital Precision Cancer Medicine project as a data science coordinator. In this role she coordinates the daily activities of the data science team, and facilitates interactions between them and other iCAN parties.
Her academic background is in understanding cell migration in complex in vivo environments by utilising live imaging, genetics and computational tools (PhD in biochemistry within the Weavers Lab in University of Bristol, England). She is now interested in exploring how the data created in scientific research can be utilised effectively to answer scientific questions and impact clinical outcomes.
I am a postdoctoral researcher in the Tumor Genomics (Aaltonen) and Machine Learning in Biomedicine (Pitkänen) groups. My research focuses mainly on next-generation sequencing-powered disease genetics, which involves analyses of inherited and somatically acquired genetic variants of all types as well as software development. I got my formal education in the University of Helsinki where I received degrees in computer science (BSc), bioinformatics (MSc), and cancer genetics (PhD).
Yrjö is a doctoral researcher in the Integrative Life Science doctoral programme. He obtained his MSc in Bioinformatics and Digital Health from Aalto University. His research focuses on developing novel analysis methods for nanopore sequencing data. These include methods for simulating nanopore sequencing data and detecting DNA adducts from nanopore sequencing data. Novel methods for adduct detection can shed light on the initiation of various cancers. As a part of his research, he is studying adducts in colorectal cancer in collaboration with the Aaltonen group (Faculty of Medicine, U. Helsinki).
Anna Kuosmanen is the bioinformatics team lead in the iCAN data team. She obtained her PhD in computer science (bioinformatics) from University of Helsinki in 2018, worked as a postdoc in Professor Aaltonen's Tumor Genomics research group 2018-2020, and joined iCAN data team in August 2020. In the data team she's responsible for leading the development of the bioinformatics side of the iCAN data platform.
Laura is a postdoctoral researcher in Jette Lengefeld's lab and Esa Pitkänen's group. In her current project she will focus on cell size variability among healthy blood donors in FinnGen and hematological patients utilizing multi-omics data. Before, she did a postdoc in Outi Kilpivaara's and Ulla Wartiovaara-Kautto's Hematological Genetics lab, and Esa Pitkänen's Machine Learning in Biomedicine group within an iCAN subproject. There her research focused on understanding how leukemias arise from premalignant conditions utilizing single-cell sequencing data and TP53 variant status of single-cells. She received her PhD in computer science in the field of data mining (University of Helsinki), and her MSc and BSc in bioinformatics (Friedrich Schiller University in Jena, Germany).
I am a PhD student in the ILS program. I received my Bachelor’s in biology and Master's in Life Science Informatics from University of Helsinki. Previously I have worked as a research assistant at the University of Helsinki. My current research focuses on looking into how genomic and epigenomic features affect cancer mutagenesis. In my research I am especially interested in more detailed knowledge on how these features affect the mutagenesis of specific mutations or in different cancer types. My overall interest in research is in applying machine learning methods on DNA sequence data to gain novel biological insights.
I'm currently wrapping up my Master's degree in Life Science Informatics at the University of Helsinki, specializing in Bioinformatics and Systems Medicine. My academic journey started with a Bachelor's in Bioinformatics at Saarland University in Germany. Within Esa’s team, I'm focused on researching new methods for analyzing single-cell RNA sequencing data, particularly RNA velocity and pseudo-time analysis. Recently, my thesis explored RNA velocity analysis methods using both healthy and diseased bone marrow data.
Prima Sanjaya is currently a doctoral student in the fields of artificial intelligence in biomedicine, at the Institute for Molecular Medicine Finland. His research is centered around the development of explainable deep learning techniques in multimodal genomics and medical image data for clinical use.
He received his MSc degree from Dongseo University, South Korea in computer science--working in the Machine Learning Research Lab. Then, he worked in Department of Radiology, Seoul National University Hospital as a medical imaging researcher.
He loves to share ideas, thoughts and innovations. His passion in basic science and computer technology brought him to interdisciplinary work.
Nora is a MSc student in Bioinformatics at the University of Tübingen (Germany) and joined the group for her thesis. She received her BSc in Biomedical Science from the University of Marburg (Germany), where she deepened her interest in tumor and immune biology. Her current research focuses on cancer classification utilizing genomics and transcriptomics data as well as machine learning, aiming for better clinical feasibility. Applying machine learning methods in biomedical research combines Nora’s academic background and interest to contribute to the fight against cancer.
Veronika is a bioinformatician with an interest in ‘omics technologies and large-scale data processing. She is currently involved in the development of an iCAN data platform to connect, analyze and store clinical and research cancer data.
Prior to joining the data team in the iCAN Digital Precision Cancer Medicine project, she worked at the Mary MacKillop Institute for Health Research (Australia), and the Medical Bioinformatics Centre at Turku Centre for Biotechnology. Veronika was awarded her PhD in bioinformatics by University of Turku in 2019, where she developed computational methods and tools for mass-spectrometry based phosphoproteomics studies. She earned her MSc in Computer Science from her native St. Petersburg State Electrotechnical University (Russia) in 2008.
Ara Taalas is a PhD student in the field of biomedical machine learning at the Institute for Molecular Medicine Finland (FIMM), with a MSc in Bioinformation Technology from Aalto University. He is currently splitting his time between Terveystalo and FIMM in order to pursue research on predictive diagnostics from complex longitudinal data in the clinical domain. He holds previous experience in the modelling of stem cell differentiation processes and novel drug candidate discovery, and enjoys playing excessively complex board games.
I am a Doctoral student at the Helsinki Institute of life science. My long way to FIMM student started in Moscow, where I have got my bachelor’s degree in biology in Russian State Agrarian University and a master’s degree in bioinformatics in High School of Economics. The combination of biology and data science seems to me the most fascinating direction in science at the moment.
My previous researches had quite a wide spectrum of interests, from exploring the Bovine leukemia virus resistance in cattle to an analysis of alternative splicing in African non-biting midges, Polypedilum vanderplanki. Currently, I am interested in neuroscience research combining image processing, deep learning and epigenetic analysis.
In my free time, I'm practicing meditation, learning to play the ukulele, drawing by watercolor, spending time in nature, meeting new people, and opening for myself new activities.
Veera Timonen received her BSc degree in Biology/Genetics from Oulu University in 2017 and her MSc degree in Bioinformatics from Tampere University in 2019, working in the field of bioimage informatics. She has worked with e.g. breast and lung cancer. Currently she is a doctoral student at the Institute for Molecular Medicine Finland (FIMM) working on applying machine learning methods to biomedical data, with an emphasis on deep learning and multimodality of data.