Mattias Rantalainen

Mattias Rantalainen

Senior Lecturer | Docent
Telephone: +46852482465
Visiting address: ,
Postal address: C8 Medicinsk epidemiologi och biostatistik, C8 MEB II Rantalainen, 171 77 Stockholm

About me

  • The Predictive Medicine group focus on medical research that is driven by
    statistical machine learning, artificial intelligence and big data. Currently
    our main focus is on computational pathology.
    I am an associate professor (docent) at the Department of Medical
    Epidemiology and Biostatistics where I lead the Predictive Medicine group.
    I was previously a research fellow (2009-2013) with a joint affiliation at
    the Department of Statistics (University of Oxford) and at the Wellcome Trust
    Centre for Human Genetics (University of Oxford) where I worked with
    Professor Chris Holmes and Dr. Cecilia Lindgren. I was awarded a MRC
    Centenary Early Career Award (2012-2013) and a Medical Research Council (MRC)
    Special Training Fellowship in Biomedical Informatics (2009-2012). I had a
    postdoctoral position working on the data analysis work package for the
    MolPAGE consortium in Prof Chris Holmes’ group at the Department of
    Statistics in Oxford (2008). I completed my PhD at Imperial College London
    where I developed novel multivariate pattern recognition methods with
    applications in metabonomics, working together with Professor Elaine Holmes
    and Professor Jeremy Nicholson. I have an undergraduate degree in
    Engineering Biology (combined BSc/MSc) from Umeå University in Sweden.

Research

  • *The Predictive Medicine group focus on medical research that is driven by
    statistical machine learning, artificial intelligence (AI) and large
    population representative data sets. Currently our research is centered on
    projects in the computational pathology domain.*
    We develop and apply statistical and machine learning methodologies for
    predictive modelling in biomedical applications with a particular interest in
    precision medicine and cancer research.
    Our mission is to drive the development and application of data-driven
    approaches in cancer precision medicine and to develop and validate novel
    patient stratification models, including prognostic and treatment predictive
    applications. To achieve this we develop methods and models that allow us to
    transform large biomedical data into clinically relevant predictions at the
    individual level. Some of our research is focused on clinical translation and
    implementation.
    Our research is based on large datasets (big-data) across multiple modalities
    including comprehensive molecular profiling (e.g. DNA- and RNA-sequencing),
    clinical information and medical imaging data (histopathology).
    *Main research areas*
    * *Computational pathology:* AI (deep learning) driven research focusing on
    developing and validating models in the domain of cancer histopathology.
    The research is based on large population representative studies (we have
    >70, 000 WSIs collected in-house). The main focus is on breast, prostate
    and colorectal cancer (www.chimestudy.se [1] & www.abcap.org [2]). I
    coordinate the Swedish AI Precision Pathology (SwAIPP) consortium,
    focusing on translation and implementation of AI-based pathology
    (www.swaipp.org [3])
    * *Cancer precision medicine:* Development and validation of predictive
    models for improved patient stratification, based on comprehensive
    molecular phenotyping data (e.g. DNA- and RNA-sequencing)
    *Current research grants (as PI)*
    Our work is supported by Swedish Research Council (VR), Swedish Cancer
    Society, ERA PerMed, MEDTECHLABS, VINNOVA, SWElife, SeRC and Karolinska
    Institutet.
    *Doctoral students*
    * Philippe Weitz (main supervisor)
    * Abhinav Sharma (main supervisor)
    * Dusan Rasic (co-supervisor)
    * Sandra Kristiane Sinius Pouplier (co-supervisor)
    * Tewodros Yalew (co-supervisor)
    * Quang Thinh Trac (co-supervisor)
    * Venkatesh Chellappa Patel (co-supervisor)
    * Xiaoyang Du (co-supervisor)
    *Other group members*
    * Yanbo Feng (Postdoc)
    * Leslie Solorzano Vargas (Postdoc)
    * Bojing Liu (Postdoc)
    * Constance Boissin (Postdoc)
    * Duong Tran (Project assistant)
    * Yujie Xiang (Research assistant)
    * Ariane Buckenmeyer (Research technician)
    * Viktoria Sartor (MSc student)
    *Former group members / supervised students*
    * Yinxi Wang (PhD student, main supervisor)
    * Peter Ström (PhD student, co-supervisor)
    * Pablo Gonzalez Ginestet (PhD student, co-supervisor)
    * Charlotte Von Heijne Widlund (visiting scientist)
    * Mei Wang (Postdoc
  • currently Research Assistant Professor, School of life
    science, Peking University, China)
    * Arvind Mer (Postdoc, currently Assistant Professor, University of Ottawa,
    Canada)
    * Nghia Vu (Postdoc
  • currently Assistant Professor, Karolinska Institutet,
    MEB)
    * Daniel Garcia León (Postdoc)
    * Astrid Helzen (Technician)
    * Balasz Acs (Associated postdoc)
    * Kajsa Ledesma Eriksson (MSc student)
    * Sandy Kang Lövgren (MSc student)
    * Anton Normelius (MSc student)
    * Boxi Zhang (MSc student)
    * Youcheng Zhang (MSc student)
    *Examples of publications*
    Wang Y, Acs B, Robertson S, Liu B, Solorzano L, Wählby C, Hartman J,
    *Rantalainen M.* Improved breast cancer histological grading using deep
    learning, Annals of Oncology. 2022 33 (1), 89-98
    Wang Y, Kartasalo K, Valkonen M, Larsson C, Ruusuvuori P, Hartman J,
    *Rantalainen M.* Predicting molecular phenotypes from histopathology images:
    a transcriptome-wide expression-morphology analysis in breast cancer. Cancer
    Research. 2021 81 (19), 5115-5126
    Weitz P, Wang Y, Hartman J, *Rantalainen M*. An investigation of attention
    mechanisms in histopathology whole-slide-image analysis for regression
    objectives. In Proceedings of the IEEE/CVF International Conference on
    Computer Vision 2021 (pp. 611-619).
    Acs B, *Rantalainen M*, Hartman J., Artificial intelligence as the next step
    towards precision pathology. Journal of Internal Medicine. 2020 Mar 3.
    Liu B, Wang Y, Weitz P, Lindberg J, Egevad L, Grönberg H, Eklund M,
    *Rantalainen M.* Using deep learning to detect patients at risk for prostate
    cancer despite benign biopsies. iScience. 2022 25 (7) 104663
    Weitz P, Wang Y, Kartasalo K, Egevad L, Lindberg J, Grönberg H, Eklund M,
    *Rantalainen M.* Transcriptome-wide prediction of prostate cancer gene
    expression from histopathology images using co-expression based convolutional
    neural networks. Bioinformatics. 2022 38 (13), 3462-3469
    Ström, P., Kartasalo, K., Olsson, H., Solorzano, L., Delahunt, B., Berney,
    D.M., Bostwick, D.G., Evans, A.J., Grignon, D.J., Humphrey, P.A., Iczkowski,
    K.A.. Kench, J.G., Kristiansen, G., van der Kwast, T.H., Leite, K.R.M.,
    McKenneym, J.K., Oxley, J., Pan, C.C., Samaratunga, H., Srigley, J.R.,
    Takahashi, H., Tsuzuki, T., Varma, M., Zhou, M., Lindberg, J., Lindeskog,
    C., Ruusuvuori, P, Wählby, C., Grönberg, H., *Rantalainen, M.*, Egevad,
    L., Eklund, M., Artificial intelligence for diagnosis and grading of
    prostate cancer in biopsies: a population-based, diagnostic study. /The
    Lancet Oncology/. 2020 Jan 8.
    Wang, M., Lindberg, J., Klevebring, D., Nilsson, C., Lehmann, S., Grönberg,
    H., *Rantalainen, M.*, Development and validation of a novel RNA
    sequencing-based prognostic score for acute myeloid leukemia, J Natl Cancer
    Inst, 2018 Mar 18
    Vu, T.N., Wills, Q.F., Kalari, K.R., Niu, N., Wang, L., Pawitan,
    Y., *Rantalainen M.*, Isoform-level gene expression patterns in single-cell
    RNA-sequencing data, Bioinformatics, 2018 Feb 1
    Wang, M., Klevebring, D., Lindberg, K., Czene, K., Grönberg,
    H., *Rantalainen M.*
  • Determining breast cancer histological grade from RNA
    sequencing data. Breast Cancer Research, 2016, 18(1).
    Vu, N.T., Wills, Q.F., Kalari, K.R., Niu, N., Wang, L., *Rantalainen M.*§,
    Pawitan Y.§. Beta-Poisson model for single-cell RNA-seq data analyses.
    Bioinformatics, 2016, btw202.
    [1] http://www.chimestudy.se
    [2] http://www.abcap.org
    [3] www.swaipp.org

Teaching

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Employments

  • Senior Lecturer, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 2020-

Degrees and Education

  • Docent, Karolinska Institutet, 2020

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