Congratulations to Weikaixin for his recent publication in Leukemia!
Weikaixin Kong is a doctoral researcher in Tero Aittokallio’s group. He received his Bachelor degree (2015-2019) and Master degree (2019-2021) in Pharmaceutical Sciences in Peking University. His research focus is to develop and apply machine learning methods to predict effective and safe drug combinations or survival differences among leukemia patients, with the medical aim to explore and better understand the disease mechanisms of leukemias and to develop robust multi-omics signatures for prognostic and treatment prediction. Apart from work, he enjoys hobbies such as swimming and cooking.
Congratulations to Lea for her work in developing and advancing the Science Basement!
Lea Urpa is a doctoral researcher in Aarno Palotie and Mark Daly's groups in the final stages of her PhD. She studies the genetic architecture of neurodevelopmental and psychiatric disorders, focusing on the effect of rare and common variants on the predisposition and cause of these disorders. In addition to this, she is the founding chair of The Science Basement, a non-profit organization based at the University of Helsinki that promotes opportunities and training in science communication for early career researchers. The Science Basement is focused on open, experimental SciComm activities and a low bar of entry- with no experience necessary to join and learn. Lea received her MSc in Translational Medicine at the University of Helsinki and joined FIMM as a FIMM-EMBL rotation student. In her spare time she enjoys biking around Helsinki and doing the 'fun work' of science communication.
Congratulations to Alina and Dimitrios for receiving Instrumentarium Science Foundation dissertation grants!
Alina Malyutina is a doctoral researcher at Jing Tang's and Caroline Heckman's groups. She is working on development of clinically-relevant computational and statistical methods for the rational design of drug combinations for individual cancer patients. Additionally, she is investigating the molecular mechanisms underlying proteasome inhibitors' resistance in Multiple Myeloma and exploring the ways to resensitize the malignant cells to those inhibitors again. Alina received her MSc degree in Computational Engineering at the Lappeenranta University of Technology and joined FIMM as a FIMM-EMBL/HIIT PhD rotation student.
Dimitrios Tsallos is a doctoral researcher in Caroline Heckman's group. His thesis focuses on counteracting chemotherapy induced thrombocytopenia. Additionally, he is working on biomarker discovery in Multiple Myeloma using ex vivo drug sensitivity data from patient samples, combined with RNA sequencing, DNA sequencing data. Dimitrios holds a master's degree in Precision Medicine and Pharmacological Innovation from the University of Glasgow where he worked on drug screening using a high-throughput drug screening assay for protein-protein interaction as part of an industrial collaboration.
Congratulations to Gabin for his recent publication in OMICS!
Gabin Drouard, Miina Ollikainen, Juha Mykkänen, Olli Raitakari, Terho Lehtimäki, Mika Kähönen, Pashupati P. Mishra, Xiaoling Wang, and Jaakko Kaprio. Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values. OMICS: A Journal of Integrative Biology. Mar 2022. 130-141.
Gabin is a doctoral researcher from the Kaprio Group. He holds a master's degree in applied mathematics, and a master's degree in public health specialized in epidemiology. He has recently started his research on multi-omics data integration, and wishes to engage in multidisciplinary work to study complex phenotypes. The project he is currently working on focuses on multi-omics deep learning approaches for continuous phenotype prediction. Apart from work, he enjoys Finnish hobbies such as fishing and sauna.
Congratulations to Anil for receiving a Finnish Cancer Institute (FICAN) two-year salary grant!
Anil is a postdoctoral researcher in Mark Daly's group. His research focuses on the development of computational tools to predict personalized anticancer drug combinations using genome and transcriptome data. Currently, he is working on the development of computational methods to suggest anticancer drug combinations targeting tumor-microenvironment interaction.
Anil holds a Ph.D. degree in Biology from the Academy of Scientific and Innovative Research (AcSIR), India, where he explored DNA methylations conferring risk to Type 2 diabetes in the Indian population. In his spare time, he enjoys running, reading, and playing football.
Congratulations to Heidi Hautakangas for her recent publication in Nature Genetics!
Hautakangas, H., Winsvold, B.S., Ruotsalainen, S.E. et al. Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles. Nat Genet 54, 152–160 (2022).
Heidi is a doctoral researcher in Matti Pirinen's group. Her thesis work focuses on studying the genetic background of migraine and its subtypes by utilizing large-scale genome-wide data. Currently, she is working on fine-mapping the migraine risk loci to pinpoint the putative causal variants and genes that confer a migraine susceptibility. Heidi holds a Master's degree in statistics from the University of Helsinki, where she studied in the Biometry and bioinformatics Master’s program. In her spare time, she enjoys orienteering and cycling, reading, and spending time with her friends.
Congratulations to Jarno Kettunen for his recent publication in Diabetologia!
Kettunen, J.L.T., Rantala, E., Dwivedi, O.P. et al. A multigenerational study on phenotypic consequences of the most common causal variant of HNF1A-MODY. Diabetologia (2021).
Jarno coordinates the FINNMODY study in Tiinamaija Tuomi’s group. In October 2021, he defended his thesis on monogenic diabetes in Finland. The FINNMODY study has the main interest in monogenic disorders of glucose metabolism. The more specific goals are 1) to characterize variant–disease associations and assess whether genetic and non-genetic factors modulate the clinical presentation of monogenic diabetes; 2) to describe clinical features associated with monogenic diabetes and to provide recommendations for the diagnostic work-up, treatment and monitoring; 3) to evaluate how monogenic gene variants deteriorate glucose metabolism; and 4) to identify new gene variants and genes associated with monogenic diabetes.