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Outi Monni is a University Lecturer at the Department of Biochemistry and Developmental Biology within the Faculty of Medicine at the University of Helsinki. She serves as a supervisor in the Doctoral Programme in Biomedicine and Clinical Research and as a Director of PhD thesis supervision projects. Supervisor, Doctoral Programme in Biomedicine Supervisor, Doctoral Programme in Clinical Research Director, Biomedicum Functional Genomics Unit (LS-RIA/HGI) Her research focuses on biochemistry, genetics, and oncology , particularly in head and neck cancers. Recent work includes studies on Liprin-α1 in MAPK signaling, tumor-infiltrating lymphocytes in HPV-positive cancers, and genetic risk variants in oral squamous cell carcinoma. Key trends in her publications show expertise in functional genomics, cancer signaling pathways, and translational research . She has 54 peer-reviewed publications and extensive collaboration networks in oncology and developmental biology. Finnish Cultural Foundation Grant (2023) Luise and Henrik Kuningas Foundation Grant (2024) Monni has supervised multiple PhD theses and organized academic events like the Single Cell Transcriptomics Symposium (2017) and Single Cell Course Week (2018). Her leadership roles include directing the GOptima tumor inhibition project since 2016.

• Biochemistry
• Cell and Molecular Biology
• Genetics
• +3 more

Juho Rousu is a Professor in the Department of Computer Science at Aalto University. His research focuses on developing machine learning methods for computational and data science, with a strong emphasis on kernel methods, structured prediction, and applications in metabolomics, biomedicine, and synthetic biology. He leads efforts in multi-view learning, sparsity-driven models, and optimization techniques for complex data analysis. Key research areas include drug combination prediction, metabolite identification, and biomarker discovery through canonical correlation analysis. His work integrates chemical reaction analysis, graph learning, and deep learning approaches to tackle challenges in systems biology and precision medicine. Recent projects involve scalable methods for drug synergy modeling and computational tools like MassSpecGym for molecular discovery. Rousu’s methodologies often bridge algorithmic innovation with real-world applications, such as improving protein production in biotechnology and optimizing metabolic flux analysis. His contributions span from foundational machine learning theory to practical software tools like CamOptimus and SIRIUS 4. He collaborates across disciplines to address challenges in environmental microbiology, clinical diagnostics, and systems pharmacology. His research has been applied to critical areas like tuberculosis drug efficacy prediction and metabolic network reconstruction, demonstrating impact in both academic and industrial contexts. Despite no listed awards here, his extensive publication record reflects sustained leadership in computational science and its biomedical applications.

• Machine Learning
• Metabolomics
• Biomedicine
• +6 more

Katri Ruokamo-Korva serves as a Doctoral Researcher at the Research Unit of Biomedicine and Internal Medicine within the Faculty of Medicine at the University of Oulu. Her work focuses on genetic epidemiology and the biological mechanisms of infectious diseases through computational analysis of large-scale population datasets, contributing to the university's biomedical research initiatives. Her educational background includes a Master of Science in biochemistry and a Master of Health Sciences in epidemiology and biomedical data science. These qualifications underpin her interdisciplinary approach to complex disease research. Her research spans Genetic Epidemiology, Systems Medicine, and Computational Medicine with emphasis on Multifactorial Diseases and Genomics. She integrates population genomics with epidemiological methods to investigate infectious disease pathways, advancing precision medicine frameworks through data-driven systems biology approaches. Her work particularly addresses how genetic variations interact with environmental factors in disease manifestation. Her doctoral thesis operates within the InFlames ImmuDocs doctoral education pilot, an interdisciplinary initiative investigating inflammation and immune system dynamics. This program fosters collaboration across computational biology, clinical medicine, and public health research domains to address immune-mediated diseases through integrated data science methodologies.

• Genetic Epidemiology
• Systems Medicine
• Computational Medicine
• +3 more

Maryam Ghasemitarei is a Postdoctoral Researcher in the Department of Applied Physics at Aalto University, Finland, affiliated with the Multiscale Statistical and Quantum Physics research group. Her academic position focuses on advanced computational research using molecular dynamics simulations to investigate biomolecular systems under oxidative stress conditions induced by plasma applications. Her research interests encompass: Molecular Dynamics Simulations for biomolecular systems Biophysical effects of nitro-oxidative stress on protein structures Plasma medicine applications in cancer therapy and virology Atomistic modeling of SARS-CoV-2 spike protein interactions Nanomaterials for photocatalytic energy applications Light propagation mechanisms in biological tissues This interdisciplinary work bridges physics, chemistry, and biomedical engineering to address critical challenges in oxidative damage and therapeutic interventions. Analysis of Dr. Ghasemitarei's publication record (2011-2025) reveals a consistent trajectory in computational biophysics, with recent emphasis (2021-2025) on molecular mechanisms of protein oxidation in viral systems and cancer therapy. Her work demonstrates sophisticated application of reactive and non-reactive molecular dynamics to study cysteine oxidation effects on SARS-CoV-2 proteins, hVDAC1 channel functionality, and plasma radical interactions with tumor cells, while maintaining foundational contributions in nanomaterials and biophotonics. Scientific Awards: No scientific awards were documented in the provided source materials. Advising and Research Funding: No information regarding student supervision, doctoral advising, or research grant acquisitions was specified in the source documentation. Research Infrastructure: Active member of the Multiscale Statistical and Quantum Physics research group at Aalto University's Department of Applied Physics, utilizing high-performance computing resources for molecular dynamics simulations.

• Molecular Dynamics
• Biophysics
• Plasma Medicine
• +3 more

Yinjia Zhang is a Researcher in the Department of Computer Science at the School of Science , with a focus on data-driven methods and computational models. Research Interests : Her work spans Data Mining Statistical Association Mining Machine Learning with applications in healthcare, metabolomics, and web user analysis. She aligns her research with the United Nations Sustainable Development Goals (SDGs) to address global challenges through computational innovation. Research Trends : Between 2023 and 2024, Zhang's publications concentrated on feature selection optimization for imbalanced datasets, taxonomy-based distance metrics for user analysis, and predictive modeling for blood glucose levels. These studies integrate machine learning with healthcare data (e.g., Parkinson’s disease, diabetes) and web user behavior. Conference Activity : In December 2024, she presented her Parkinson’s disease metabolomics study at the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2024) .

• Data Mining
• Statistical Association Mining
• Machine Learning

Professor Sui Huang holds a part-time UEFDiPro position in Systems Medicine at the University of Eastern Finland (UEF), affiliated with the Institute of Biomedicine under the School of Medicine, Faculty of Health Sciences. He collaborates closely with Professor Merja Heinäniemi on plasma proteomics and systems medicine, focusing on translating omics technologies into clinical applications. His research emphasizes phenomics, integrating proteomics, genomics, and computational tools like knowledge graphs and AI. Key areas include understanding cancer cell behavior through gene regulatory networks and advancing personalized medicine by linking genomic data to dynamic health states via plasma protein profiling. He aims to leverage UEF's strategic Profi7 funding and national FinnGen project data to mechanistically connect genome and phenome. Lectures focus on systems dynamics, knowledge graphs, and AI-driven biomedical analysis for future digital health solutions.

• Systems Medicine
• Oncology
• Systems Biology
• +5 more

Wilhelmiina Hämäläinen is a Senior Lecturer at the Department of Computer Science, Aalto University, focusing on data mining, machine learning, and computational methods in biology, biomedicine, and education. She holds a PhD in Natural Sciences from the University of Helsinki (2010) and has held roles such as Research Manager at the Bioinformatics Center, University of Eastern Finland (2018–2019). Her research integrates algorithm development, statistical significance testing, and real-world applications in areas like metabolomics, osteological analysis, and educational feedback systems. Her work addresses challenges in pattern discovery, feature selection, and data clustering, with applications spanning medical diagnostics, ecological morphology, and student performance analysis. She has led projects funded by the Academy of Finland, including 'Efficient search algorithms for genuine dependency rules in large-dimensional data' (2016–2018). Her contributions include advancements in Fisher exact test approximations, vector space modeling for educational data, and activity recognition algorithms. Key collaborations involve international institutions, and she actively contributes to academic peer review, serving on editorial boards for journals like Data Mining and Knowledge Discovery . Her teaching and research emphasize computational methods' ethical and practical impacts, aligning with UN Sustainable Development Goals in education and health.

• Data Mining
• Machine Learning
• Algorithms
• +5 more

Sami Väänänen is a Clinical Researcher at the Department of Technical Physics, Faculty of Science, Forestry and Technology, University of Eastern Finland. His research focuses on biomechanical modeling of bone, particularly using dual-energy X-ray absorptiometry (DXA) images to develop 3D finite element (FE) models for improved prediction of hip fractures and osteoporosis care. His research interests include Biomechanics , Osteoporosis , Fracture Risk Assessment , Finite Element Modeling , Medical Imaging , and Deep Learning in Medical Imaging . He develops computational methods to reconstruct 3D bone geometry and predict mechanical strength from clinical 2D imaging, aiming to improve clinical fracture risk tools beyond areal bone mineral density (aBMD). His recent publications highlight a strong trend in using advanced computational techniques—such as statistical shape and appearance models (SSAMs), deep learning, and finite element analysis—to extract biomechanical and pathological information from DXA and CT images. These studies, often conducted on large clinical cohorts, validate the superior predictive power of 3D FE models over traditional methods in identifying hip, wrist, and proximal humerus fractures. DXA-based 3D finite element models predict hip fractures better than areal BMD in elderly women (2025) Identifying proximal humerus fractures: an algorithmic approach using registers and radiological visit data (2025) Relation of lateral lumbar spine DXA to age-related bone loss, aortic calcification, scoliosis and vertebral degeneration (2025) 3D Finite Element Models Reconstructed From 2D DXA Images Improve Hip Fracture Prediction (2023) Detecting pathological features and predicting fracture risk from DXA images using deep learning (2021) Sami Väänänen has collaborated extensively with researchers such as Lorenzo Grassi, Hanna Isaksson, and Jukka S. Jurvelin. While specific grants and awards are not listed, his work has been supported by funding bodies including the Swedish Research Council and Mats Paulsson's Foundation. He is actively involved in methodological development and validation, contributing to both original research and review articles in top journals like Journal of Bone and Mineral Research , Bone , and Medical Image Analysis .

• Biomechanics
• Osteoporosis
• Fracture Risk Assessment
• +7 more

Matti Nykter is a Professor of Bioinformatics at the Faculty of Medicine and Health Technology within the Department of Biomedicine at Tampere University. His research focuses on computational biology, cancer genomics, and systems medicine with an emphasis on prostate cancer, gliomas, and epigenetic mechanisms. Key areas of investigation include: Cancer evolution and therapeutic resistance using liquid biopsy approaches Integration of multi-omics data (genomics, epigenomics, transcriptomics) Bioinformatic methods for analyzing tumor heterogeneity and clonal evolution Zebrafish models for studying host-pathogen interactions His work bridges basic science and clinical applications, developing biomarkers and computational tools for precision oncology. Recent studies explore immunogenomics of cancer, single-cell profiling of tumor microenvironments, and molecular characterization of metastatic progression. Major contributions include: Epigenetic profiling of endocrine-resistant prostate cancer Genomic analysis of Lynch syndrome-associated cancers Development of UCLIB diagnostic panel for non-invasive cancer detection Identification of therapeutic targets via single-cell and spatial transcriptomics

• Bioinformatics
• Cancer Genomics
• Molecular Biology
• +3 more

Ilona Mäkinen is a Researcher affiliated with the Doctoral Programme in Medicine, Biosciences and Biomedical Engineering. Her work focuses on understanding brain energy metabolism in schizophrenia, leveraging computational modelling and genomic analysis. Her research contributes to UN Sustainable Development Goals related to health and wellbeing. Key research areas include differential gene expression, D2 analysis, and metabolism-related genes in neurological disorders. She has published abstracts and a Master's thesis analyzing energy metabolism pathways in schizophrenia using RNA sequencing and computational methods. No academic awards or student advisees are listed. Her work integrates neuroscience, bioinformatics, and biomedical engineering to explore metabolic mechanisms underlying psychiatric conditions.

• Energy Metabolism Genes
• Schizophrenia Pathology
• Neuroscience
• +3 more

Santosh Lamichhane is a Research Council of Finland Academy Research Fellow and Docent (equivalent to Associate Professor) in Metabolomics at the Department of Life Technologies, University of Turku. He is also affiliated with the Institute of Biomedicine. His research focuses on the interplay between gut microbiota, lipids, and human health, particularly in early life and metabolic diseases. His research expertise spans: Metabolomics and Lipidomics Reverse Metabolomics and Molecular Networking Gut Microbiome and Human Nutrition Multivariate Data Analysis and Systems Biology Human Intervention Studies Lamichhane's recent publications reflect a strong trend in applying advanced metabolomic techniques to understand the molecular basis of diseases such as type 1 diabetes, MASLD, and infant neurodevelopment. His work often involves large-scale, multi-institutional collaborations and the development of standardized protocols for metabolomic analysis. He contributes significantly to methodological advancements in metabolomics, including sample preservation, data integration, and big data discovery frameworks. His scientific recognition includes: Academy Research Fellow, Research Council of Finland Lamichhane has been involved in mentoring and collaborative research, contributing to studies involving infant microbiota, maternal-infant dyads, and metabolic disease biomarkers. He has not received explicit mention of grants beyond his Academy Fellowship, but his participation in multi-center studies suggests collaborative funding. He teaches metabolomics in courses such as Systems Biology, Modern Biotechnologies, and Bioanalytical Techniques at the University of Turku. He has collaborated with prominent research groups, including the Dorrestein Lab at UC San Diego, and is actively involved in advancing the field of metabolomics through both experimental and computational innovations.

• Metabolomics
• Lipidomics
• Gut Microbial Metabolism
• +7 more

Riikka Lund is an Adjunct Professor in Molecular Biology and a Research Director (10%) at the Turku Bioscience Centre, Institute of Biomedicine, University of Turku. She also works as a Senior Scientist in Bioinformatics at Orion. She leads the Biomedical Epigenomics research group and has held leadership roles in national research platforms. Education: M.Sc. in Genetics, Department of Biology, University of Turku (2001) Ph.D. in Molecular Immunology and Medical Genetics, Faculty of Medicine, University of Turku (2004) Postdoctoral training, Centre for Stem Cell Biology, University of Sheffield (2007–2008) Her research focuses on epigenomics and genomics, particularly using next-generation sequencing to study molecular mechanisms in development, aging, and diseases such as type 1 diabetes and cancer. She integrates bioinformatics and functional genomics to uncover disease markers and therapeutic targets. Her work emphasizes epigenetic regulation , pluripotent stem cells , and genome-wide methods . The recent publications (2020–2023) reflect a strong focus on DNA methylation dynamics in developmental and disease contexts, including maternal health, autoimmunity, and cancer. The studies employ advanced bisulfite sequencing, single-cell analysis, and computational modeling, highlighting her expertise in bioinformatics and epigenetic data analysis. Scientific Awards: Millennium Distinct Technology Award (2013) for contributions to stem cell research She has been actively involved in research leadership, including as Head of the Finnish Functional Genomics Centre (2013–2022), co-PI of the Biocenter Finland Genome-wide Methods Platform, and co-director of the CompLifeSci Research Programme. She has also served on the editorial board of Scientific Reports . Although no students are listed, she has engaged in teaching in epigenomics, genomics, and stem cell biology. Her research is supported through national and international collaborations and infrastructure platforms. She leads the Biomedical Epigenomics Group and has conducted short research visits to institutions including the Broad Institute (USA), Diagenode (Belgium), and the University of Southern Denmark, reflecting her international engagement in cutting-edge genomics technologies.

• Epigenomics
• Genomics
• Transcriptomics
• +10 more

Olli Yli-Harja is a Professor in Biomedicine at Tampere University. His research spans oncology, pharmacology, and computational biology, with a focus on glioblastoma treatment and machine learning applications in biomedical contexts. Doctor of Science (Technology), Lappeenranta University of Technology (1989) Master of Science (Technology) in Automation Engineering, Lappeenranta University of Technology (1985) His work intersects biomedicine and computational methods , targeting cancer therapies and dynamical systems modeling. Recent studies include adenosine receptor antagonists for glioblastoma and DNA methylation analysis in pediatric tumors. Publications highlight trends in scientific machine learning , adenosine pharmacology , and epigenetic tumor mechanisms . Collaborations span pharmacology, genetics, and AI-driven biomedical research. Activities include conference presentations, editorial board roles, and media appearances discussing cancer treatment innovations.

• Oncology
• Pharmacology
• Computational Biology
• +3 more

Katri Lindfors is a Professor in Biomedicine with a prominent research profile in autoimmune diseases, particularly celiac disease and type 1 diabetes. Her work spans immunology, gastrointestinal pathology, and environmental determinants of autoimmunity. She is actively involved in translational research, utilizing advanced imaging, stem cell models, and molecular techniques to understand disease mechanisms. Academic Rank: Professor Department: Biomedicine Her research interests are deeply rooted in understanding immune responses in celiac disease, including gluten immunogenicity (especially in oats), intestinal epithelial transport of gliadin, and microvascular changes in the small intestine. She also investigates the early markers of autoimmunity in children at risk for type 1 diabetes, contributing to large-scale studies like the TEDDY Study Group. The recent trend in her publications (2024–2025) shows a strong focus on mechanistic immunology, advanced imaging (e.g., X-ray microtomography), and the use of iPSC-derived models to study disease. Her work integrates molecular, cellular, and clinical perspectives, often in collaboration with leading experts such as Katri Kaukinen and others in Finnish and international consortia. Scientific Awards and Recognitions: No specific awards mentioned in the provided text. Advising and Grants: Katri Lindfors actively supervises students at all levels, including bachelor’s, master’s, and doctoral candidates. She has served as an examiner and supervisor in multiple thesis projects. Her involvement in the Multisociety Celiac Disease Consortium indicates leadership in collaborative research. She also contributes as a reviewer for scientific journals and participates in organizing scientific events. Labs and Teams: She collaborates closely with research groups focusing on celiac disease, intestinal immunology, and stem cell models. Her work is associated with advanced imaging and in vitro modeling labs, particularly those using hiPSC-derived cells and microfluidic systems.

• Celiac Disease
• Autoimmunity
• Type 1 Diabetes
• +7 more

Suvi Kuivanen is an Academy Research Fellow at the University of Helsinki , affiliated with the Faculty of Biological and Environmental Sciences and the Molecular and Integrative Biosciences Research Programme . Her research focuses on Biomedicine , with expertise in Virology , Infectious Diseases , and Public Health . Email: suvi.kuivanen@helsinki.fi Address: P.O 56 (Viikinkaari 9), 00014 University of Helsinki Research Interests include: Viral-host interactions in pathogens like SARS-CoV-2 and tick-borne encephalitis virus Antiviral drug development through computational and experimental approaches Neurovirology examining viral impacts on olfactory and central nervous systems Immunology of mucosal-associated invariant T cells and granulocyte responses in infections Recent publications analyze SARS-CoV-2 infection mechanisms , urban pollution effects on viral susceptibility , and zoonotic potential of henipaviruses . Her work combines molecular biology , structural analysis , and public health strategies . Scientific Awards : Academy Research Fellow (Academy of Finland, 2024–2029) Active research projects include 'Uncovering the zoonotic potential of henipaviruses in European shrews', funded by the Research Council of Finland.

• Biomedicine
• Virology
• Infectious Diseases
• +4 more