The strategic profiling area has yearly supported scientific research in health data science through providing of seed funding. See listing of projects here.
Year 2025
Enabling the use of unique exercise stress test dataset for cardiovascular assessment. Roni Ahola, Antti Vehkaoja.
High Resolution Brain Vasculature Mapping. Jari Hyttinen.
Equitable Health and Social Services Discovery. Konstantinos Stefanidis, Mark van Gils.
Direct long-read RNA sequencing to uncover aberrant tumour transcriptomes. Minna-Liisa Änkö.
Predictive Modeling of Self-Reported Health Diagnoses in Social Media. Jaakko Peltonen.
Towards a Digital Brain Ecosystem: Integrating Multimodal Patient Data with Mechanistic Models for Personalized Simulations in The Virtual Brain. Marja-Leena Linne.
Digital pathology infrastructure for TAU. Antti Kiviaho, Matti Nykter, Kirsi Rautajoki.
Spatial Single-Cell Analysis of Immune Regulation in Cancer. Kirsi Rautajoki.
AI-Based Decision Support for Unruptured Intracranial Aneurysms: Validating the GenAI Toolbox. Jussi Rasku, Pekka Abrahamsson, Juhana Frösen, Mark van Gils.
Peri-Prostatic Adipose Tissue and Its Role in Prostate Cancer Development and Progression. Aino Siltari, Teemu Murtola.
Predicting clinical outcome of intracranial aneurysms using prescribed drug use patterns: use case on COX2 inhibitors. Anna-Leena Vuorinen, Juhana Frösen, Mark van Gils.
Harmonised Data Curation and Ontology Mapping for Impact Outcome Pathways (IOPs). Alexandra Schaffert, Dario Greco, Angela Serra.
PulseWavePy: A Framework for Arterial Pulse Wave Decomposition and Analysis. Mira Haapatikka, Antti Vehkaoja
Exploring germline immune dysregulation factors in prostate cancer progression and therapy. Jake Lin, Matti Nykter.
Year 2024
Deep learning-based cancer immunoscoring from histopathological images. Rautajoki Kirsi, Ruusuvuori Pekka.
Multi-modal and multi-approach decision support in Unruptured Intracranial Aneurysm. Frösen Juhana, Gils Mark.
ProKG – A Knowledge Graph for Specialized and Explainable Artificial Intelligence in Prostate Cancer. Lai Xin.
Detecting causal biomarkers of frailty with interpretable machine learning. Lin Jake, Kuusisto Antti.
AI-driven discovery of molecules targeting oncogenic microRNA production. Änkö, Minna-Liisa, Bahrudeen Mohamed.
Integrating Neuroscience and AI: Brain-Inspired Unsupervised Learning Systems. Linne Marja-Leena.
GenAI-Powered Synthetic Data Toolbox for Health Science. Rasku Jussi, Gils Mark, Abrahamsson, Kemell, Hasan.
Patient Stratification and Decision Support for Prostate Cancer Diagnostics via Statistical and Neural Machine Learning. Moro Alessio.
Development of a Complete Neuromorphic Pipeline for Motor Imagery EEG Classification. Mäki-Marttunen Tuomo.
Data scientist for a large-scale register-based research project. Vuorinen Anna-Leena.
Physiological Exercise Thresholds with Advanced Data Analytics and AI. Räsänen Esa, van Gils Mark, Aalto-Setälä Katriina.
Detecting critical transitions in complex biological systems. Subramanyam Narayan.
“Metricize” the performance of cell-to-cell communication channels with Deep Learning approach. Tervonen Aapo, Soile Nymark, Huy Tran, Teemu Ihalainen, Jyrki Nummenmaa.
Year 2023
COhort of feMale Patients’ mammogRaphic texturE features – the effect of imaging acquisition parameterS and underlying biology: the COMPRESS trial. Otso Arponen, Tampere University and Tampere University Hospital.
Digital Lung: from health data integration to novel therapeutic solutions for idiopathic pulmonary fibrosis. Antonio Federico, Tampere University.
Unleashing Real World Data for cancer treatment – setting up the environment. Prof. Mark van Gils, Tampere University.
Developing mathematical and computational methods for quantifying cell-to-cell interactions from time lapse imaging data of cancer cells. Sara Hamis and Pieti Kirkkopelto, Tampere University.
Database for mechanosensitive proteins (‘Mechanosome’) and its utilization for identifying mutations relevant for cancer mechanosignaling. Prof. Vesa Hytönen, Jenni Keränen, and Frans Ek, Tampere University.
Development of an electronic frailty index for Finnish healthcare using artificial intelligence. Juulia Jylhävä and Jake Lin, Tampere University.
Prediction of adverse outcomes associated with ageing. Pedro Moreno Sanchez and Prof. Mark van Gils, Tampere University.
Patient Stratification and Decision Support for Prostate Cancer Diagnostics via Statistical and Neural Machine Learning. Alessio Moro, Prof. Matti Nykter, Prof. Jaakko Peltonen, and Hyon-Jung Kim-Ollila, Tampere University.
Algorithm-based detection of patients suitable for clinical oncological trials. Prof. Teemu Murtola, Olli Tasso, and Julle Kuusisto, Tampere University Hospital and Tampere University.
Bioinformatics study of cell membrane transporter proteins in schizophrenia. Tuomo Mäki-Marttunen, Tampere University.
Spatial Analysis of Functionally Relevant Cancer and Immune Cells. Kirsi Rautajoki, Tampere University.
Towards large-scale utilization of digital pathology image data from clinical diagnostics: building access infrastructure to PIRHA data lake. Pekka Ruusuvuori and Teemu Tolonen, Tampere University and Fimlab.
Navigating the different dynamical regimes of tissue behaviors: from mathematical modeling to in vitro study. Huy Tran, Prof. Jari Hyttinen, Teemu Ihalainen, and Soile Nymark, Tampere University.
Modelling oncogenic miRNA production. Minna-Liisa Änkö and Mohamed Bahrudeen, Tampere University.
Year 2022
Prostate cancer grading with DNA-based deep learning methods. Anssi Nurminen, Tampere University.
Predicting delayed cerebral ischemia from EEG signals. Narayan Subramaniyam, Tampere University.
Workflow showcase for glutamate transporter data and models in Virtual Brain. Marja-Leena Linne, Tampere University.
Perceiver networks in multi-modal health data analysis – how does it work? Prof. Mark van Gils, Tampere University.
Prediction of Cardiac Events through Novel Analysis of FINCAVAS Data. Esa Räsänen and Jussi Hernesniemi, Tampere University and Tampere University Hospital.
Title for reporting: Cohort study of mobile phone use and health (COSMOS). Prof. Anssi Auvinen, Tampere University.
“Python for future Health Data Scientists” course. Prof. Mark van Gils and Milla Juutinen, Tampere University.
Development of exercises and exercise sessions of the course “BBT.021 Bioinformatics”. Reetta Nätkin, Tampere University.
Establishment of a highly efficient CRISPR-Cas9-based gene editing platform. Mikael Marttinen, Tampere University.
Probing single-cell calcium responses to periodic light-activated mechanical stimulations in MDCK tissues. Huy Tran and Teemu Ihalainen, Tampere University.
Optimization of two-step method for freshly frozen tumor tissue samples single nuclei isolation for chromatin and RNA sequencing profiling of single cells. Alfonso Urbanucci, Tampere University.
(one not shown due to novelty)
Year 2021
Novel time series analysis methods for computational cardiology. Prof. Esa Räsänen, Tampere University.
Unleashing the unique value of datasets for data-driven decision support in Cardiovascular Disease. Prof. (Tenure track) Antti Vehkaoja, Prof. Mark Van Gils, and Jari Viik, Tampere University.
Deep learning assisted classification of IDH mutant diffuse astrocytoma based on H&E staining. Adj. Prof. Kirsi Granberg, Tampere University.
Applying Random Survival Forest to Estimate Vaccine Effectiveness against New SARS-CoV-2 Variants in Finland.Huu Thuan Vo, MD, PhD, Tampere University.
Delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage: a multidisciplinary study combining machine learning with clinical data. Heikki Kiiski, MD, PhD, Tampere University Hospital/Tampere University.
Text mining and weakly supervised AI for diagnostic support. Adj. Prof. Pekka Ruusuvuori, Tampere University, and Adj. Prof. Teemu Tolonen, Fimlab.
Development of diagnostics decision support with data lakes. Prof. Matti Nykter, Tampere University.
Microbiome as a risk factor for hemorrhagic stroke. Prof. Juhana Frösen, Tampere University Hospital, Tampere University.