Full list of publications by Pekka Marttinen

Articles in Journals and Proceedings

  1. Kumar, Y., Ilin, A., Salo, H., Kulathinal, S., Leinonen, M.K., and Marttinen, P. (2024). Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models. IEEE Transactions on Artificial Intelligence, accepted. Preprint (Best Findings Paper at ML4H2023)

  2. Ji, S., Gao, Y., and Marttinen, P. (2024). Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), accepted. Preprint

  3. Kytö, M., Hotta, S., Niinistö, S., Marttinen, P., Korhonen, T.E., Markussen, L.T., Jacucci, G., Sievänen, H., Vähä-Ypyä, H., Korhonen, I., Virtanen, S., Heinonen, S., and Koivusalo, S. (2024). Periodic mobile application (eMOM) with self-tracking of glucose and lifestyle improves treatment of diet-controlled gestational diabetes without human guidance: a randomized controlled trial. American Journal of Obstetrics and Gynecology, accepted.

  4. Dainese, N., Ilin, A., and Marttinen, P. (2024). Can docstring reformulation with an LLM improve code generation? In Proceedings of the 2024 EACL Student Research Workshop, accepted.

  5. He, C., Raj, V., Moen, H., Gröhn, T., Chen, W., Peltonen, L., Koivusalo, S., Marttinen, P., and Jacucci, G. (2024). VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models. The 29th Annual ACM Conference on Intelligent User Interfaces. (IUI2024), accepted.

  6. Holster, T., Ji, S., and Marttinen, P. (2023). Risk adjustment for regional healthcare funding allocations with ensemble methods: an empirical study and interpretation. The European Journal of Health Economics, accepted.

  7. Odnoblyudova, A., Hızlı, C., John, S.T., Cognolato, A., Juuti, A., Särkkä, S., Pietiläinen, K., and Marttinen, P. (2023). Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics. Proceedings of the 3rd Machine Learning for Health Symposium 2023, PMLR 225:428-444 (ML4H 2023). Available online

  8. Poyraz, O. and Marttinen, P. (2023). Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series. In Proceedings of the 3rd Machine Learning for Health Symposium 2023, PMLR 225:461-479 (ML4H 2023). Available online

  9. Ojala, F.*, Abdul Sater, M.R.*, Miller, L.G., McKinnell, J.A., Hayden, M.K., Huang, S.S., Grad, Y.H.+, and Marttinen, P.+ (2023). Bayesian modeling of the impact of antibiotic resistance on the efficiency of MRSA decolonization. PLOS Computational Biology, 19(10): e1010898. (*/+ equal contribution) Available online

  10. Dainese, N., Marttinen, P., and Ilin, A. (2023). Reader: Model-based language-instructed reinforcement learning. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023), pages 16583–16599. Available online

  11. Hizli, C., John, S.T., Juuti, A., Saarinen, T., Pietiläinen, K., and Marttinen, P. (2023). Temporal causal mediation through a point process: direct and indirect effects of healthcare interventions. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023), accepted. Preprint

  12. Kytö, M., Koivusalo, S., Tuomonen, H., Strömberg, L., Ruonala, A., Marttinen, P., Heinonen, S., and Jacucci, G. (2023). Supporting Management of Gestational Diabetes with Comprehensive Self-Tracking: Mixed-Method Study of Wearable Sensors. JMIR Diabetes, accepted. Preprint

  13. Wharrie, S., Yang, Z., Raj, V., Monti, R., Gupta, R., Wang, Y., Martin, A., O'Connor, L.J., Kaski, S., Marttinen, P., Palamara, P.F., Lippert, C., and Ganna, A. (2023). HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes. Bioinformatics, 39(9): btad535. Available online

  14. Honkamaa, J., Khan, U., Koivukoski, S., Valkonen, M., Latonen, L., Ruusuvuori, P., and Marttinen, P. (2023). Deformation equivariant cross-modality image synthesis with paired non-aligned training data. Medical Image Analysis, 90:102940. Available online

  15. Hizli, C., John, S.T., Juuti, A., Saarinen, T., Pietiläinen, K., and Marttinen, P. (2023). Causal Modeling of Policy Interventions From Treatment–Outcome Sequences. In Proceedings of the 40th International Conference on Machine Learning, PMLR 202. (ICML 2023). Available online

  16. Raj, V., Cui, T., Heinonen, M., and Marttinen, P. (2023). Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach. The Proceedings of 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:6741-6763. (AISTATS 2023) Available online

  17. Karami, S., Saberi-Movahed, F., Tiwari, P., Marttinen, P., and Vahdati, S. (2023). Unsupervised Feature Selection Based on Variance-Covariance Subspace Distance. Neural Networks, 166:188-203. Available online

  18. Ji, S. and Marttinen, P. (2023). Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023). Available online

  19. Fritzsche, M., Akyüz, K., Abadía, M.C., McLennan, S., Marttinen, P. Mayrhofer, M.T., and Buyx, A.M. (2023). Ethical layering in AI-driven polygenic risk scores – new complexities, new challenges. Frontiers in Genetics, 14. Available online

  20. Sun, W., Ji, S., Denti, T., Moen, H., Kerro, O., Rannikko, A., Marttinen, P., and Koskinen, M. (2023). Weak Supervision and Clustering-based Sample Selection for Clinical Named Entity Recognition. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Lecture Notes in Computer Science, 14174. (ECML PKDD 2023). Available online

  21. Oghabian, A., van der Kolk, B.W., Marttinen, P., Valsesia, A., Langin, D., Saris, W.H., Astrup, A., Blaak, E.E., and Pietiläinen, K.H. (2023). Baseline gene expression in subcutaneous adipose tissue predicts diet-induced weight loss in individuals with obesity. PeerJ, 11:e15100. Available online

  22. Gröhn, T., Liikkanen, S., Huttunen, T., Mäkinen, M., Liljeberg, P., and Marttinen, P. (2022). Quantifying movement behaviour of chronic low back pain patients in virtual reality. ACM Transactions on Computing for Healthcare, 4(2), 1-24. Available online

  23. Kytö, M., Markussen, L., Marttinen, P., Jacucci, G., Niinistö, S., Virtanen, S.M., Korhonen, T., Sievänen, H., Vähä-Ypyä, H., Korhonen, I., Heinonen, S., and Koivusalo, S. (2022). Comprehensive self-tracking of blood glucose and lifestyle with a mobile application in the management of gestational diabetes: a study protocol for a randomized controlled trial (eMOM GDM study). BMJ Open, 12(11):e066292. Available online

  24. Cui, T., Mekkaoui, K.E., Reinvall, J., Havulinna, A.S., Marttinen, P., and Kaski, S. (2022). Gene-Gene Interaction Detection with Deep Learning. Communications Biology, 5(1), 1238. Available online

  25. Cui, T., Kumar, Y., Marttinen, P., and Kaski, S. (2022). Deconfounded Representation Similarity for Comparison of Neural Networks. Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022). Available online

  26. Sun, W., Ji, S., Cambria, E., and Marttinen, P. (2022). Multitask Balanced and Recalibrated Network for Medical Code Prediction. ACM Transactions on Intelligent Systems and Technology, 14(1):1-20. Available online

  27. Tiwari, P., Dehdashti, S., Obeid, A.K., Marttinen, P., and Bruza, P. (2022). Kernel method based on non-linear coherent states in quantum feature space. Journal of Physics A: Mathematical and Theoretical, 55(35), 355301. Available online

  28. Gao, Y., Ji, S., Zhang, T., Tiwari, P., and Marttinen, P. (2022). Contextualized graph embeddings for adverse drug event detection. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022). Available online

  29. Poyraz, O., Sater, M.R.A., Miller, L.G., McKinnell, J.A., Huang, S.S., Grad, Y.H.*, and Marttinen, P.* (2022). Modeling methicillin-resistant Staphylococcus aureus decolonization: Interactions between body sites and the impact of site-specific clearance. Journal of The Royal Society Interface, 19: 20210916. (* equal contribution) Available online

  30. Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., and Marttinen, P. (2022). EEG based emotion recognition: A tutorial and review. ACM Computing Surveys, 55(4): 1-57. Available online

  31. Cui, T., Havulinna, A., Marttinen, P.*, and Kaski, S.* (2022). Informative Bayesian Neural Network Priors for Weak Signals. Bayesian Analysis, 17(4):1121-1511. (*equal contribution) Available online

  32. Wee, B., Alves, J., Lindsay, D., Klatt, A., Sargison, F., Cameron, R., Pickering, A., Gorzynski, J., Corander, J., Marttinen, P., Opitz, B., Smith, A., and Fitzgerald, J.R. (2021). Population analysis of Legionella pneumophila reveals a basis for resistance to complement-mediated killing. Nature Communications, 12(1):1-13. Available online

  33. Rissanen, S. and Marttinen, P. (2021). A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models. Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), pp. 4207-4217. Available online

  34. Hiremath, S.*, Wittke, S.*, Palosuo, T., Kaivosoja, J., Tao, F., Proll, M., Puttonen, E., Peltonen-Sainio, P.**, Marttinen, P.**, and Mamitsuka, H.** (2021). Crop loss identification at field parcel scale using satellite remote sensing and machine learning. PLOS ONE. (*/** equal contribution) Available online

  35. He, L., Niu, M., Tiwari, P., Marttinen, P., Su, R., Jiang, J., Guo, C., Wang, H., Ding, S., Wang, Z., Dang, W., and Pan, X. (2021). Deep Learning for Depression Recognition with Audiovisual Cues: A Review. Information Fusion, 80(C): 56-86. Available online

  36. He, L., Tiwari, P., Su, R., Xiuying, S., Marttinen, P., and Kumar, N. (2021). COVIDNet: An Automatic Framework to Detect COVID-19 with Deep Learning from Chest X-ray Images. IEEE Internet of Things Journal, 9(13), pp. 11376-11384. Available online

  37. Ji, S., Hölttä, M., and Marttinen, P. (2021). Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study. Computers in Biology and Medicine, 139(C). Preprint

  38. Ashrafi, R., Ahola, Ai., Rosengård-Bärlund, M., Saarinen, T., Heinonen, S., Juuti, A., Marttinen, P., and Pietiläinen, K. (2021). Computational modelling of self-reported dietary carbohydrate intake on glucose concentrations in patients undergoing Roux-en-Y gastric bypass versus one-anastomosis gastric bypass. Annals of Medicine, 53(1): 1885-1895.

  39. Sun, W, Ji, S., Cambria, E., and Marttinen, P. (2021). Multitask recalibrated aggregation network for medical code prediction. In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021), pp. 367-383. Preprint

  40. Ji, S., Pan, S., and Marttinen, P. (2021). Medical Code Assignment with Gated Convolution and Note-Code Interaction. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 1034-1043. Available online

  41. Järvenpää, M., Gutmann, M.U., Vehtari, A., and Marttinen, P. (2021). Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations. Bayesian Analysis, 16(1):147-178. Available online

  42. Ji, S., Pan, S., Cambria, E., Marttinen, P., and Yu, P.S. (2021). A Survey on Knowledge Graphs: Representation, Acquisition and Applications. IEEE Transactions on Neural Networks and Learning Systems. Preprint

  43. Ji, S., Cambria, E., and Marttinen, P. (2020). Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text. Proceedings of the 3rd Clinical Natural Language Processing Workshop at EMNLP 2020. Available online

  44. Järvenpää, M., Vehtari, A., and Marttinen, P. (2020). Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI 2020). Available online

  45. Zhang, G., Ashrafi, R.A., Juuti, A., Pietiläinen, K., and Marttinen, P. (2020). Errors-in-variables modeling of personalized treatment-response trajectories. IEEE Journal of Biomedical and Health Informatics, 25(1):201-208. Available online. (Supplement)

  46. Cui, T., Marttinen, P.*, and Kaski, S.* (2020). Learning Global Pairwise Interactions with Bayesian Neural Networks. Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020). (*equal contribution) Available online

  47. Kumar, Y., Salo, H., Nieminen, T., Vepsäläinen, K., Kulathinal, S., and Marttinen, P. (2020). Predicting utilization of healthcare services from individual disease trajectories using RNNs with multi-headed attention. Proceedings of Machine Learning Research: Machine Learning for Health (ML4H) at NeurIPS 2019, 116:93-111. Available online

  48. Arredondo-Alonso, S., Top, J., McNally, A., Puranen, S., Pesonen, M., Pensar, J., Marttinen, P., Braat, J., Rogers, M., Van Schaik, W., Kaski, S., Willems, R., Corander, J., and Schürch, A. (2020). Plasmids shaped the recent emergence of the major nosocomial pathogen Enterococcus faecium. mBio, 11(1). Available online

  49. Gillberg, J., Marttinen, P., Mamitsuka, H., and Kaski, S. (2019). Modelling GxE with historical weather information improves genomic prediction in new environments. Bioinformatics, 35(20):4045-4052. Available online

  50. Järvenpää, M., Abdul Sater, M.R., Lagoudas, G.K., Blainey, P.C., Miller, L.G., McKinnell, J.A., Huang, S.S., Grad, Y.H.*, and Marttinen, P.* (2019). A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation. PLoS Computational Biology, 15(4):e1006534. (*equal contribution) Available online

  51. Gladstone, R.A., Lo, S.W., Lees, J.A., Croucher, N.J., van Tonder, A.J., Corander, J., Page, A.J., Marttinen, P., Bentley, L.J., Ochoa, T.J., Ho, P.L., du Plessis, M., Cornick, J.E., Kwambana-Adams, B., Benisty, R., Nzenze, S.A., Madhi, S.A., Hawkins, P.A., Everett, D.B., Antonio, M., Dagan, R., Klugman, K.P., von Gottberg, A., McGee, L., Breiman, R.F., Bentley, S.D., and The Global Pneumococcal Sequencing Consortium (2019). International genomic definition of pneumococcal lineages, to contextualise disease, antibiotic resistance and vaccine impact. EBioMedicine, 43:338-346. Available online

  52. Järvenpää, M., Gutmann, M.U., Pleska, A., Vehtari, A., and Marttinen, P. (2019). Efficient acquisition rules for model-based approximate Bayesian computation. Bayesian Analysis, 14(2):595-622. Available online

  53. Sundin, I.*, Peltola, T.*, Micallef, L., Afrabandpey, H., Soare, M., Majumder, M.M., Daee, P., He, C., Serim, B., Havulinna, A., Heckman, C., Jacucci, G., Marttinen, P., and Kaski, S. (2018). Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge. Bioinformatics, 34(13):i395-i403. (*equal contribution) Available online

  54. Lintusaari, J., Vuollekoski, H., Kangasrääsiö, A., Skytén, K., Järvenpää, M., Marttinen, P., Gutmann, M., Vehtari, A., Corander, J., and Kaski, S. (2018). ELFI: Engine for Likelihood Free Inference. Journal of Machine Learning Research, 19(16):1-7. Available online

  55. Sipola, A., Marttinen, P., and Corander, J. (2018). Bacmeta: simulator for genomic evolution in bacterial metapopulations. Bioinformatics, 1:3. Available online

  56. Järvenpää, M., Gutmann, M., Vehtari, A., and Marttinen, P. (2018). Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria. Annals of Applied Statistics, 12(4):2228-2251. Available online

  57. Micallef, L.*, Sundin, I.*, Marttinen, P.*, Ammad-ud-din. M., Peltola, T., Soare, M., Jacucci, G., and Kaski, S. (2017). Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets. Proceedings of the 22nd International Conference on Intelligent User Interfaces (IUI '17). (*equal contribution) Pre-print

  58. Marttinen, P. and Hanage, W.P. (2017). Speciation trajectories in recombining bacterial species. PLOS Computational Biology, 13(7):e1005640. Available online

  59. David, S., Sanchez-Buso, L., Harris, S.R., Marttinen, P., Rusniok, C., Buchrieser, C., Harrison, T.G., and Parkhill, J. (2017). Dynamics and impact of homologous recombination on the evolution of Legionella pneumophila. PLOS Genetics, 13(6):e1006855. Available online

  60. Pirinen, M., Benner, C., Marttinen, P., Järvelin, M.-R., Rivas, M.A., and Ripatti, S. (2017). biMM: Efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements. Bioinformatics, 33(15):2405-2407. Available online

  61. Villa, P.M., Marttinen, P., Gillberg, L., Lokki, A.I., Majander, K., Taipale, P., Pesonen, A., Räikkönen, K., Hämäläinen, E., Kajantie, E., and Laivuori, H. (2017). Cluster Analysis to Estimate the Risk of Preeclampsia in the High-Risk Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction (PREDO) Study. PLoS ONE, 12(3): e0174399. Available online

  62. Mostowy, R., Croucher, N.J., Andam, C.P., Corander, J., Hanage, W.P., and Marttinen, P. (2017). Efficient inference of recent and ancestral recombination within bacterial populations. Molecular Biology and Evolution, 34(5):1167-1182. Available online

  63. Harms, K., Lunnan, A., Hülter, N., Mourier, T., Vinner, L., Andam, C.P., Marttinen, P., Fridholm, H., Hansen, A.J., Hanage, W.P., Nielsen, K.M., Willerslev, E., and Johnsen, P.J. (2016). Substitutions of short heterologous DNA segments of intra- or extragenomic origins produce clustered genomic polymorphisms. Proceedings of the National Academy of Sciences of the United States of America, 113(52):15066-15071. doi:10.1073/pnas.1615819114. Available online

  64. Lees, J.A., Vehkala, M., Välimäki, N., Harris, S.R., Chewapreecha, C., Croucher, N.J., Marttinen, P., Davies, M.R., Steer, A.C., Tong, S.Y.C., Honkela, A., Parkhill, J., Bentley, S.D., and Corander, J. (2016). Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes. Nature Communications, 7:12797, doi:10.1038/ncomms12797. Available online

  65. Gillberg, J., Marttinen, P., Pirinen, M., Kangas, A.-J., Soininen, P., Ali, M., Havulinna, A. S., Järvelin, M.-R., Ala-Korpela, M., and Kaski, S. (2016). Multiple output regression with latent noise. Journal of Machine Learning Research, 17:1-35. Available online

  66. Sieberts, S., Zhu, F., García-García, J., Stahl, E., Pratap, A., Pandey, G., Pappas, D., Aguilar, D., Anton, B., Bonet, J., Eksi, R., Fornés, O., Guney, E., Li, H., Marín, M., Panwar, B., Planas-Iglesias, J., Poglayen, D., Cui, J., Falcao, A., Suver, C., Hoff, B., Balagurusamy, V., Dillenberger, D., Chaibub Neto, E., Norman, T., Aittokallio, T., Ammad-ud-din, M., Azencott, C.-A., Bellón, V., Boeva, V., Bunte, K., Chheda, H., Cheng, L., Corander, J., Dumontier, M., Goldenberg, A., Gopalacharyulu, P., Hajiloo, M., Hidru, D., Jaiswal, A., Kaski, S., Khalfaoui, B., Khan, S., Kramer, E., Marttinen, P., Mezlini, A., Molparia, B., Pirinen, M., Saarela, J., Samwald, M., Stoven, V., Tang, H., Tang, J., Torkamani, A., Vert, J.P., Wang, B., Wang, T., Wennerberg, K., Wineinger, N., Xiao, G., Xie, Y., Yeung, R., Zhan, X., Zhao, C., Greenberg, J., Kremer, J., Michaud, K., Barton, A., Coenen, M., Mariette, X., Miceli, C., Shadick, N., Weinblatt, M., de Vries, N, Tak, P., Gerlag, D., Huizinga, T.W.J., Kurreeman, F., Allaart, C., Bridges, S., Criswell, L., Moreland, L., Klareskog, L., Saevarsdottir, S., Padyukov, L., Gregersen, P., Friend, S., Plenge, R., Stolovitzky, G., Oliva, B., Guan, Y., and Mangravite, L. (2016). Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nature Communications, 7:12460, doi:10.1038/ncomms12460 Available online

  67. Numminen, E., Gutmann, M., Shubin, M., Marttinen, P., Meric, G.,van Schaik, W., Coque, T., Baquero, F., Willems, R., Sheppard, S., Feil, E., Hanage, W.P., and Corander, J. (2016). The impact of host metapopulation structure on the population genetics of colonizing bacteria. Journal of Theoretical Biology, 396: 53-62. Pre-print

  68. Cichonska, A., Rousu, J., Marttinen, P., Kangas, A.J., Soininen, P., Lehtimäki, T., Raitakari, O.T., Järvelin, M.-R., Salomaa, V., Ala-Korpela, M., Ripatti, S. and Pirinen, M. (2016). metaCCA: Summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. Bioinformatics, 32(13):1981-1989, doi: 10.1093/bioinformatics, Available online

  69. Marttinen, P., Croucher, N.J., Gutmann, M.U., Corander, J. and Hanage, W.P. (2015). Recombination produces coherent bacterial species clusters in both core and accessory genomes. Microbial Genomics, 1, doi:10.1099/mgen.0.000038, Available online (Supplement)

  70. Chewapreecha, C., Marttinen, P., Croucher, N.J.,Salter, S.J., Harris, S.R., Mather, A.E.,Hanage, W.P., Goldblatt, D., Nosten, F.H., Turner, C., Turner, P., Bentley, S.D. and Parkhill, J. (2014). Comprehensive identification of single nucleotide polymorphisms associated with beta-lactam resistance within pneumococcal mosaic genes. PLoS Genetics, 10(8):e1004547. doi:10.1371/journal.pgen.1004547

  71. Sheppard, S.K., Cheng, L., Méric, G., de Haan, C.P.A., Llarena, A.-K., Marttinen, P., Vidal, A., Ridley, A., Clifton-Hadley, F., Connor, T.R., Strachan, N.J.C, Forbes, K., Colles, F.M., Jolley, K.A., Bentley, S.D., Maiden, M.C.J., Hänninen, M.-L., Parkhill, J., Hanage, W.P. and Corander, J. (2014). Cryptic ecology among host generalist Campylobacter jejuni in domestic animals. Molecular Ecology, 23(10):2442-51. doi: 10.1111/mec.12742

  72. Kashtan, N., Roggensack, S.E., Rodrigue, S., Thompson, J.W., Biller, S.J., Coe, A., Ding, H., Marttinen, P., Malmstrom, R.R., Stocker, R., Follows, M.J., Stepanauskas, R. and Chisholm, S.W. (2014). Single-cell genomics reveals hundreds of coexisting subpopulations in wild Prochlorococcus. Science, 344(6182): 416-420.

  73. Marttinen, P., Pirinen, M., Sarin, A.P., Gillberg, J., Kettunen, J., Surakka, I., Kangas, A.J., Soininen, P., O’Reilly, P.F., Kaakinen, M., Kähönen, M., Lehtimäki, T., Ala-Korpela, M., Raitakari, O.T., Salomaa, V., Järvelin, M.-R., Ripatti, S. and Kaski, S. (2014). Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression. Bioinformatics, 30(14):2026-34. doi: 10.1093/bioinformatics/btu140

  74. Chewapreecha, C., Harris, S.R., Croucher, N.J., Turner, C., Marttinen, P., Cheng, L., Pessia, A., Aanensen, D.M., Mather, A.E., Page, A.J., Salter, S.J., Harris, D., Nosten, F., Goldblatt, D., Corander, J., Parkhill, J., Turner, P. and Bentley, S.D. (2014). Dense genomic sampling identifies highways of pneumococcal recombination. Nature Genetics, 46: 305-309.

  75. Marttinen, M., Pajari, A.-M., Päivärinta, E., Storvik, M., Marttinen, P., Nurmi, T., Niku, M., Piironen, V. and Mutanen, M. (2014). Plant sterol feeding induces tumor formation and alters sterol metabolism in the intestine of ApcMin mice. Nutrition and Cancer: An International Journal, 66(2). doi: 10.1080/01635581.2014.865244

  76. Marttinen, P., Gillberg, J., Havulinna, A., Corander, J. and Kaski, S. (2013). Genome-wide association studies with high-dimensional phenotypes. Statistical Applications in Genetics and Molecular Biology, 12(4): 413-431.

  77. Castillo-Ramírez, S., Corander, J., Marttinen, P., Aldeljawi, M., Hanage, W.P., Westh, H., Boye, K.,Gulay, Z., Bentley, S.D., Parkhill, J., Holden M.T. and Feil, E.J. (2012). Phylogeographic variation in recombination rates within a global clone of Methicillin-Resistant Staphylococcus aureus (MRSA). Genome Biology, 13(12):R126. doi:10.1186/gb-2012-13-12-r126

  78. Peltola, T., Marttinen P., and Vehtari, A. (2012). Finite Adaptation and Multistep Moves in the Metropolis-Hastings Algorithm for Variable Selection in Genome-Wide Association Analysis. PLoS ONE, 7(11): e49445. doi:10.1371/journal.pone.0049445

  79. Delezuch, W., Marttinen, P., Kokki, H., Heikkinen, M., Lintula, H., Vanamo, K., Pulkki, K. and Matinlauri, I. (2012). Serum and CSF soluble CD26 and CD30 concentrations in healthy pediatric surgical outpatients. Tissue antigens, doi: 10.1111/j.1399-0039.2012.01938.x.

  80. Peltola, T., Marttinen, P., Jula, A., Salomaa, V., Perola, M. and Vehtari, A. (2012). Bayesian variable selection in searching for additive and dominant effects in genome-wide data. PLoS ONE, 7(1): e29115. doi:10.1371/journal.pone.0029115.

  81. Marttinen, P., Hanage, W.P., Nicholas, J.C., Connor, T.C., Harris, S.R., Bentley, S.D. and Corander, J. (2012). Detection of recombination events in bacterial genomes from large population samples. Nucleic Acids Research, 40(1): e6. doi: 10.1093/nar/gkr928.

  82. Sirén, J., Marttinen, P. and Corander, J. (2010). Reconstructing population histories from single-nucleotide polymorphism data. Molecular Biology and Evolution, 28(1):673-683.

  83. Marttinen, P. and Corander, J. (2010). Efficient Bayesian approach for multilocus association mapping including gene-gene interactions. BMC Bioinformatics, 11:443.

  84. Törönen, P., Ojala, P.J., Marttinen, P. and Holm, L. (2009). Robust extraction of functional signals from gene set analysis using a generalized threshold free scoring function. BMC Bioinformatics, 10:307.

  85. Marttinen, P., Myllykangas, S. and Corander, J. (2009). Bayesian clustering and feature selection for cancer tissue samples. BMC Bioinformatics, 10:90.

  86. Marttinen, P. and Corander, J. (2009). Bayesian learning of graphical vector autoregressions with unequal lag-lengths. Machine Learning, 75:217-243.

  87. Marttinen, P., Tang, J., De Baets, B., Dawyndt, P. and Corander, J. (2009). Bayesian clustering of fuzzy feature vectors using a quasi-likelihood approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31:74-85.

  88. Corander, J., Marttinen, P., Sirén, J. and Tang, J. (2008). Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations. BMC Bioinformatics, 9:539.

  89. Marttinen, P., Baldwin, A., Hanage, W.P., Dowson, C., Mahenthiralingam, E. and Corander, J. (2008). Bayesian modeling of recombination events in bacterial populations. BMC Bioinformatics, 9:421.

  90. Marttinen, P., Corander, J., Törönen, P. and Holm, L. (2006). Bayesian search of functionally divergent protein subgroups and their function specific residues. Bioinformatics, 22:2466-2474.

  91. Corander, J. and Marttinen, P. (2006). Bayesian identification of admixture events using multi-locus molecular markers. Molecular Ecology, 15:2833-2843.

  92. Corander, J., Marttinen, P. and Mäntyniemi, S. (2006). Bayesian identification of stock mixtures from molecular marker data. Fishery Bulletin, 104:550-558.

  93. Corander, J. and Marttinen, P. (2006). Bayesian model learning based on predictive entropy. Journal of Logic, Language and Information, 15:5-20.

  94. Corander, J., Waldmann, P., Marttinen, P. and Sillanpää, M.J. (2004). BAPS 2: enhanced possibilities for the analysis of genetic population structure. Bioinformatics, 20:2363-2369.

Extended Abstracts in Workshops, etc.

  1. Honkamaa, J., et al. (2023). SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration. Medical Imaging Meets NeurIPS 2023 (Med-NeurIPS 2023).

  2. Kumar, Y., et al. (2023). SANSformers: Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models. Machine Learning for Health Symposium 2023 (ML4H 2023).

  3. Hizli, C., et al. (2023). Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions. Machine Learning for Health Symposium 2023 (ML4H 2023).

  4. Wharrie, S., et al. (2022). HAPNEST: An efficient tool for generating large-scale genetics datasets from limited training data. Synthetic Data for Empowering ML Research (SyntheticData4ML) Workshop at NeurIPS 2022.

  5. Poyraz, C. et al. (2022). Modeling MRSA decolonization: Interactions between body sites and the impact of site-specific clearance. Symposium on Machine Learning for Health (ML4H 2022).

  6. Hizli, C. et al. (2022). Joint Point Process Model for Counterfactual Treatment--Outcome Trajectories Under Policy Interventions. Symposium on Machine Learning for Health (ML4H 2022).

  7. Järvenpää, M. et al. (2019). Batch simulations and uncertainty quantification in Gaussian process surrogate-based approximate Bayesian computation. 2nd Symposium on Advances in Approximate Bayesian Inference.

  8. Cui, T. et al. (2019). Learning pairwise global interactions using Bayesian Neural Networks. Bayesian Deep Learning, Workshop at NeurIPS 2019.

  9. Zhang, G. et al. (2019). Errors-in-variables modeling of personalized treatment-response trajectories. ML4H: Machine Learning for Health, Workshop at NeurIPS 2019.

  10. Järvenpää, M. et al. (2018). A Bayesian model of acquisition and clearance of bacterial colonization. ML4H: Machine Learning for Health, Workshop at NeurIPS 2018.

  11. Sundin, I. et al. (2017). Ask the doctor - Improving drug sensitivity predictions through active expert knowledge elicitation. ML4H: Machine Learning for Health, Workshop at NIPS 2017.

  12. Järvenpää, M. et al. (2017). Efficient acquisition rules for model-based approximate Bayesian computation. Advances in Approximate Bayesian Inference, Workshop at NIPS 2017.

  13. Järvenpää, M. et al. (2017). Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria. Machine Learning in Computational Biology (MLCB), Workshop at NIPS 2017.

  14. Gillberg, J. et al. (2016). Multiple output regression with latent noise. Machine Learning in Computational Biology (MLCB), Workshop at NIPS 2016.

  15. Marttinen, P. et al. (2015). Assessing multivariate gene-metabolome associations with the Bayesian reduced rank regression. Machine Learning in Computational Biology (MLCB), Workshop at NIPS 2015.

  16. Cichonska, A. et al. (2014). Meta-analysis of genome-wide association studies with multivariate traits. International Workshop on Machine Learning and Systems Biology, MLSB14.

  17. Marttinen, P. et al. (2012). Genome-wide association studies with high-dimensional phenotypes. Machine Learning in Computational Biology (MLCB), Workshop at NIPS 2012.