| Using classification to evaluate the output of confidence-based association rule mining S Mutter, M Hall, E Frank Australasian Joint Conference on Artificial Intelligence, 538-549, 2004 | 69 | 2004 |
| Meal timing, meal frequency, and breakfast skipping in adult individuals with type 1 diabetes–associations with glycaemic control AJ Ahola, S Mutter, C Forsblom, V Harjutsalo, PH Groop Scientific reports 9 (1), 20063, 2019 | 50 | 2019 |
| Urinary metabolite profiling and risk of progression of diabetic nephropathy in 2670 individuals with type 1 diabetes S Mutter, E Valo, V Aittomäki, K Nybo, L Raivonen, LM Thorn, C Forsblom, ... Diabetologia 65, 140-149, 2022 | 34 | 2022 |
| Waist-height ratio and waist are the best estimators of visceral fat in type 1 diabetes EB Parente, S Mutter, V Harjutsalo, AJ Ahola, C Forsblom, PH Groop Scientific Reports 10 (1), 18575, 2020 | 25 | 2020 |
| Numero: a statistical framework to define multivariable subgroups in complex population-based datasets S Gao, S Mutter, A Casey, VP Mäkinen International journal of epidemiology 48 (2), 369-374, 2019 | 18 | 2019 |
| Classification using association rules S Mutter A thesis of Diploma of computer science, Univeristy of Freiburg, Hamilton …, 2004 | 18 | 2004 |
| The relationship between body fat distribution and nonalcoholic fatty liver in adults with type 1 diabetes EB Parente, EH Dahlström, V Harjutsalo, J Inkeri, S Mutter, C Forsblom, ... Diabetes Care 44 (7), 1706-1713, 2021 | 15 | 2021 |
| Resistant hypertension and risk of adverse events in individuals with type 1 diabetes: a nationwide prospective study R Lithovius, V Harjutsalo, S Mutter, D Gordin, C Forsblom, PH Groop Diabetes Care 43 (8), 1885-1892, 2020 | 15 | 2020 |
| Statistical reporting of metabolomics data: experience from a high-throughput NMR platform and epidemiological applications S Mutter, C Worden, K Paxton, VP Mäkinen Metabolomics 16, 1-4, 2020 | 11 | 2020 |
| Multivariable Analysis of Nutritional and Socio-Economic Profiles Shows Differences in Incident Anemia for Northern and Southern Jiangsu in China S Mutter, AE Casey, S Zhen, S Zumin, VP Mäkinen Nutrients 9 (10), 1153, 2017 | 9 | 2017 |
| Genetic risk score enhances coronary artery disease risk prediction in individuals with type 1 diabetes R Lithovius, AA Antikainen, S Mutter, E Valo, C Forsblom, V Harjutsalo, ... Diabetes Care 45 (3), 734-741, 2022 | 4 | 2022 |
| Propositionalisation of profile hidden markov models for biological sequence analysis S Mutter, B Pfahringer, G Holmes AI 2008: Advances in Artificial Intelligence: 21st Australasian Joint …, 2008 | 4 | 2008 |
| Telomeres do not always shorten over time in individuals with type 1 diabetes A Syreeni, LM Carroll, S Mutter, AS Januszewski, C Forsblom, M Lehto, ... Diabetes Research and Clinical Practice 188, 109926, 2022 | 3 | 2022 |
| A discriminative approach to structured biological data S Mutter, B Pfahringer The University of Waikato, 2007 | 3 | 2007 |
| Telomeres in clinical diabetes research–Moving towards precision medicine in diabetes care? AJ Jenkins, A Syreeni, S Mutter, AS Januszewski, PH Groop Diabetes Research and Clinical Practice 194, 110178, 2022 | 2 | 2022 |
| Response to Comment on Parente et al. The Relationship Between Body Fat Distribution and Nonalcoholic Fatty Liver in Adults With Type 1 Diabetes. Diabetes Care 2021; 44: 1706–1713 EB Parente, EH Dahlström, V Harjutsalo, J Inkeri, S Mutter, C Forsblom, ... Diabetes Care 45 (1), e8-e9, 2022 | 2 | 2022 |
| The positive effects of negative information: Extending one-class classification models in binary proteomic sequence classification S Mutter, B Pfahringer, G Holmes AI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint …, 2009 | 2 | 2009 |
| Medication profiling in women with type 1 diabetes highlights the importance of adequate, guideline-based treatment in low-risk groups PHG Raija Lithovius, Stefan Mutter, Erika B. Parente, Ville-Petteri Mäkinen ... Scientific Reports 13, 2023 | 1 | 2023 |
| Urinary metabolite profiling identifies biomarkers for risk of progression of diabetic nephropathy in 2,670 individuals with type 1 diabetes S Mutter, E Valo, V Aittomäki, K Nybo, L Raivonen, LM Thorn, C Forsblom, ... medRxiv, 2020.10. 21.20215921, 2020 | 1 | 2020 |
| Waist-height ratio and waist circumference are the best estimators of visceral fat in type 1 diabetes independently of diabetic nephropathy S Mutter, EB Parente, V Harjutsalo, AJ Ahola, C Forsblom, PH Groop | 1 | 2020 |
