Yudi Pawitan

Yudi Pawitan

Professor
Visiting address: Nobels väg 12a, 17165 Solna
Postal address: C8 Medicinsk epidemiologi och biostatistik, C8 MEB Pawitan, 171 77 Stockholm
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About me

  • Education
    - 1982 BSc in Statistics from Bogor Agriculture Institute, Indonesia
    - 1984 MSc in Statistics from University of California at Davis
    - 1987 PhD in Statistics from University of California at Davis

Research

  • I work mostly in the development of statistical methods for analyses of high-throughput data as currently generated in genomic studies, including SNP and RNA arrays, and next-generation sequencing. 

Articles

All other publications

Grants

  • Swedish Research Council
    1 January 2024 - 31 December 2027
    Amyotrophic lateral sclerosis (ALS) is a rare but devastating neurodegenerative disease. There is currently no cure nor effective treatment for ALS, largely due to our limited understanding about disease etiology. Today, it is believed that the etiology of ALS, especially sporadic ALS, likely involves both genetic and non-genetic factors. Identification of non-genetic risk factors, especially the modifiable ones, is therefore important in providing new knowledge for disease prevention and identifying novel therapeutic targets. The overarching aim of this project is to use the extensive data and biospecimens collected in the unique Swedish national ALS registry and a case-control study of ALS in Stockholm, to evaluate infections as potential risk and prognostic factors for ALS, focusing on understanding their interactions with individual genetic susceptibility to ALS, inflammation, and gut microbiome. We will first design two epidemiological studies to assess the role of infections ascertained through registers or serology on the risk and prognosis of ALS, by comparing ALS patients with sibling controls, spouse controls, and unrelated population controls. We will then design an omics study to reveal the interactions between infections and genetic susceptibility to ALS, inflammation, and gut microbiome. Finally, we will design a systems biology study to understand the biological plausibility of an infectious etiology in ALS.
  • Swedish Research Council
    1 January 2023 - 31 December 2025
  • Swedish Research Council
    1 January 2023 - 31 December 2025
  • Swedish Research Council
    1 January 2020 - 31 December 2023
  • Detection of biomarkers by analysis of "Next generation" sequencing data.
    Swedish Cancer Society
    1 January 2018
    Molecular research is currently dominated by sequence-based technology, partly because the cost has decreased dramatically but also because the technology promises very detailed information on individual cancers. But many challenges remain
    Although we can produce a correct list of mutations in a single cancer, it is far from self-evident what they mean in terms of cancer biology or what one can clinically use it for. There is still a large gap between the list of mutations and clinical decisions for which treatment to use for the individual. The overall goals of our research include: (i) computer modeling and analysis of the large-scale molecular data currently dominated by DNA and RNA sequence data, and (ii) integration of multiple omics data to improve disease prognosis. As cancer cells develop, many genomic changes such as mutations or copy variation accumulate in the cell. An important step towards better biological understanding and treatment is to try to separate the genomic cause / effect changes from independent / accompanying ones. It is the effect changes that the cancer cells depend on for survival and growth. Our hope is to develop robust methods for identifying cancer cells by integrating omics data, not just the obvious genomics and transcriptomics data such as mutations, copying variability and RNA expression, but also by using biology databases and data networks for interaction genomics such as gene or protein interaction. With the same approach, we have previously shown improved prognoses for breast cancer survival, and we plan to continue the research and development by using the technique of omission data on other cancers.
  • Detection of biomarkers by analysis of "Next generation" sequencing data.
    Swedish Cancer Society
    1 January 2017
    Molecular research is currently dominated by sequence-based technology, partly because the cost has decreased dramatically but also because the technology promises very detailed information on individual cancers. But many challenges remain
    Although we can produce a correct list of mutations in a single cancer, it is far from self-evident what they mean in terms of cancer biology or what one can clinically use it for. There is still a large gap between the list of mutations and clinical decisions for which treatment to use for the individual. The overall goals of our research include: (i) computer modeling and analysis of the large-scale molecular data currently dominated by DNA and RNA sequence data, and (ii) integration of multiple omics data to improve disease prognosis. As cancer cells develop, many genomic changes such as mutations or copy variation accumulate in the cell. An important step towards better biological understanding and treatment is to try to separate the genomic cause / effect changes from independent / accompanying ones. It is the effect changes that the cancer cells depend on for survival and growth. Our hope is to develop robust methods for identifying cancer cells by integrating omics data, not just the obvious genomics and transcriptomics data such as mutations, copying variability and RNA expression, but also by using biology databases and data networks for interaction genomics such as gene or protein interaction. With the same approach, we have previously shown improved prognoses for breast cancer survival, and we plan to continue the research and development by using the technique of omission data on other cancers.
  • Swedish Research Council
    1 January 2017 - 31 December 2020
  • Swedish Research Council
    1 January 2017 - 31 December 2020
  • Detection of biomarkers by analysis of "Next generation" sequencing data.
    Swedish Cancer Society
    1 January 2016
    Molecular research is currently dominated by sequence-based technology, partly because the cost has decreased dramatically but also because the technology promises very detailed information on individual cancers. But many challenges remain
    Although we can produce a correct list of mutations in a single cancer, it is far from self-evident what they mean in terms of cancer biology or what one can clinically use it for. There is still a large gap between the list of mutations and clinical decisions for which treatment to use for the individual. The overall goals of our research include: (i) computer modeling and analysis of the large-scale molecular data currently dominated by DNA and RNA sequence data, and (ii) integration of multiple omics data to improve disease prognosis. As cancer cells develop, many genomic changes such as mutations or copy variation accumulate in the cell. An important step towards better biological understanding and treatment is to try to separate the genomic cause / effect changes from independent / accompanying ones. It is the effect changes that the cancer cells depend on for survival and growth. Our hope is to develop robust methods for identifying cancer cells by integrating omics data, not just the obvious genomics and transcriptomics data such as mutations, copying variability and RNA expression, but also by using biology databases and data networks for interaction genomics such as gene or protein interaction. With the same approach, we have previously shown improved prognoses for breast cancer survival, and we plan to continue the research and development by using the technique of omission data on other cancers.
  • Use next-generation sequence data to find biomarkers for cancer
    Swedish Cancer Society
    1 January 2015
    The growth of large-scale data sets continues unabated with the advent of next generation sequencing. Sequencing can reveal unsolicited information on a single tumor's mutation spectrum. An application of sequencing can be used to detect person-specific cancer biomarkers for treatment and follow-up decisions. This means that we have now reached the highly anticipated area of personal medication. Although the cost of sequencing is now considered affordable, around US $ 5,000 for the entire genome, it presents data that generates great challenges, both in basic IT infrastructure and statistical analysis. The primary objective of our research group is to develop statistical and bioinformatic tools and methods for analyzing large-scale data sets within molecular medicine. Currently, we focus on the challenges we face when analyzing the next generation of sequencing data. We pay special attention to searching for biomarkers for cancer. The specific problems we address are: (i) detection of mutations from sequence data, (ii) identification of so-called control genes and biological processes by means of integration of several different types of molecular data, (iii) identification of group-specific markers for breast cancer. We hope to answer the above problems by analyzing the next generation of sequencing data on about 400 breast cancer patients. The data set has already been collected via Cancer Genome Atlas, a large NIH-funded study in cancer genome where approximately 5,000 cancer samples of 20 different cancers have been sequenced. As this is one of the largest collections of ordered data for breast cancer, we hope, with our analyzes, to be able to identify new mutations of breast cancer, and among these mutations, determine which are the most likely mutations that drive the development of the individual cancer.
  • Use next-generation sequence data to find biomarkers for cancer
    Swedish Cancer Society
    1 January 2014
    The growth of large-scale data sets continues unabated with the advent of next generation sequencing. Sequencing can reveal unsolicited information on a single tumor's mutation spectrum. An application of sequencing can be used to detect person-specific cancer biomarkers for treatment and follow-up decisions. This means that we have now reached the highly anticipated area of personal medication. Although the cost of sequencing is now considered affordable, around US $ 5,000 for the entire genome, it presents data that generates great challenges, both in basic IT infrastructure and statistical analysis. The primary objective of our research group is to develop statistical and bioinformatic tools and methods for analyzing large-scale data sets within molecular medicine. Currently, we focus on the challenges we face when analyzing the next generation of sequencing data. We pay special attention to searching for biomarkers for cancer. The specific problems we address are: (i) detection of mutations from sequence data, (ii) identification of so-called control genes and biological processes by means of integration of several different types of molecular data, (iii) identification of group-specific markers for breast cancer. We hope to answer the above problems by analyzing the next generation of sequencing data on about 400 breast cancer patients. The data set has already been collected via Cancer Genome Atlas, a large NIH-funded study in cancer genome where approximately 5,000 cancer samples of 20 different cancers have been sequenced. As this is one of the largest collections of ordered data for breast cancer, we hope, with our analyzes, to be able to identify new mutations of breast cancer, and among these mutations, determine which are the most likely mutations that drive the development of the individual cancer.
  • Swedish Research Council
    1 January 2014 - 31 December 2016
  • Swedish Research Council
    1 January 2014 - 31 December 2018
  • Swedish Research Council
    1 January 2012 - 31 December 2015
  • Swedish Research Council
    1 January 2010 - 31 December 2012
  • Swedish Research Council
    1 January 2010 - 31 December 2012

Employments

  • Professor, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 2002-

Supervision

  • Supervision to doctoral degree

    • Ralf Kuja-Halkola, Twin and family studies on the development of cognitive and externalizing problems., 2014
    • Chuen Seng Tan, Statistical methodology for analysis of proteomics data, 2008
    • Linda Lindström, Familial studies on common cancers – a population based approach, 2008

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