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DynaOmics SIGNED

From longitudinal proteomics to dynamic individualized diagnostics

Total Cost €

0

EC-Contrib. €

0

Partnership

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 DynaOmics project word cloud

Explore the words cloud of the DynaOmics project. It provides you a very rough idea of what is the project "DynaOmics" about.

detect    detector    predictive    conventional    computational    clinically    techniques    feasible    innovative    individualized    precision    class    reproducible    learning    proteomic    datasets    made    insights    strategies    unmet    proteome    promise    prediction    relatively    hold    classification    machine    technological    statistical    longitudinal    detection    create    undetectable    models    predict    single    types    t1d    time    clinical    medicine    attractive    biomedical    abundance    sectional    unconventional    option    cross    develops    data    builds    dynamic    diseases    rare    samples    context    dynaomics    joint    risk    roadmap    omics    predictions    symptom    fundamentally    preventive    model    individuals    period    involve    diabetes    avenues    free    underdeveloped    power    biomarker    disease    dynamically    protein    introduces    optimization    event    markers    tools    proteomics    assist    validates    individual    utility    restricted    therapeutic    previously    associations    diagnosis    treatment   

Project "DynaOmics" data sheet

The following table provides information about the project.

Coordinator
TURUN YLIOPISTO 

Organization address
address: YLIOPISTONMAKI
city: Turku
postcode: 20014
website: www.utu.fi

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Finland [FI]
 Project website https://elolab.utu.fi
 Total cost 1˙499˙869 €
 EC max contribution 1˙499˙869 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-STG
 Funding Scheme ERC-STG
 Starting year 2016
 Duration (year-month-day) from 2016-06-01   to  2021-05-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TURUN YLIOPISTO FI (Turku) coordinator 1˙499˙869.00

Map

 Project objective

Longitudinal omics data hold great promise to improve biomarker detection and enable dynamic individualized predictions. Recent technological advances have made proteomics an increasingly attractive option but clinical longitudinal proteomic datasets are still rare and computational tools for their analysis underdeveloped. The objective of this proposal is to create a roadmap to detect clinically feasible protein markers using longitudinal data and effective computational tools. A biomedical focus is on early detection of Type 1 diabetes (T1D). Specific objectives are:

1) Novel biomarker detector using longitudinal data. DynaOmics introduces novel types of multi-level dynamic markers that are undetectable in conventional single-time cross-sectional studies (e.g. within-individual changes in abundance or associations), develops optimization methods for their robust and reproducible detection within and across individuals, and validates their utility in well-defined samples. 2) Individualized disease risk prediction dynamically. DynaOmics develops dynamic individualized predictive models using the multi-level longitudinal proteome features and novel statistical and machine learning methods that have previously not been used in this context, including joint models of longitudinal and time-to-event data, and one-class classification type techniques. 3) Dynamic prediction of T1D. DynaOmics builds a predictive model of dynamic T1D risk to assist early detection of the disease, which is crucial for developing future therapeutic and preventive strategies. T1D typically involves a relatively long symptom-free period before clinical diagnosis but current tools to predict early T1D risk have restricted power.

The objectives involve innovative and unconventional approaches and address major unmet challenges in the field, having high potential to open new avenues for diagnosis and treatment of complex diseases and fundamentally novel insights towards precision medicine.

 Publications

year authors and title journal last update
List of publications.
2019 Subhash K. Tripathi, Tommi Välikangas, Ankitha Shetty, Mohd Moin Khan, Robert Moulder, Santosh D. Bhosale, Elina Komsi, Verna Salo, Rafael Sales De Albuquerque, Omid Rasool, Sanjeev Galande, Laura L. Elo, Riitta Lahesmaa
Quantitative Proteomics Reveals the Dynamic Protein Landscape during Initiation of Human Th17 Cell Polarization
published pages: 334-355, ISSN: 2589-0042, DOI: 10.1016/j.isci.2018.12.020
iScience 11 2020-04-23
2017 Tomi Suomi, Fatemeh Seyednasrollah, Maria K. Jaakkola, Thomas Faux, Laura L. Elo
ROTS: An R package for reproducibility-optimized statistical testing
published pages: e1005562, ISSN: 1553-7358, DOI: 10.1371/journal.pcbi.1005562
PLOS Computational Biology 13/5 2020-04-23
2017 Fatemeh Seyednasrollah, Johanna Mäkelä, Niina Pitkänen, Markus Juonala, Nina Hutri-Kähönen, Terho Lehtimäki, Jorma Viikari, Tanika Kelly, Changwei Li, Lydia Bazzano, Laura L. Elo, Olli T. Raitakari
Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns StudyCLINICAL PERSPECTIVE
published pages: e001554, ISSN: 1942-3268, DOI: 10.1161/CIRCGENETICS.116.001554
Circulation: Cardiovascular Genetics 10/3 2020-04-23
2018 Niina Lietzen, Le T. T. An, Maria K. Jaakkola, Henna Kallionpää, Sami Oikarinen, Juha Mykkänen, Mikael Knip, Riitta Veijola, Jorma Ilonen, Jorma Toppari, Heikki Hyöty, Riitta Lahesmaa, Laura L. Elo
Enterovirus-associated changes in blood transcriptomic profiles of children with genetic susceptibility to type 1 diabetes
published pages: 381-388, ISSN: 0012-186X, DOI: 10.1007/s00125-017-4460-7
Diabetologia 61/2 2020-04-23
2018 Ubaid Ullah, Syed Bilal Ahmad Andrabi, Subhash Kumar Tripathi, Obaiah Dirasantha, Kartiek Kanduri, Sini Rautio, Catharina C. Gross, Sari Lehtimäki, Kanchan Bala, Johanna Tuomisto, Urvashi Bhatia, Deepankar Chakroborty, Laura L. Elo, Harri Lähdesmäki, Heinz Wiendl, Omid Rasool, Riitta Lahesmaa
Transcriptional Repressor HIC1 Contributes to Suppressive Function of Human Induced Regulatory T Cells
published pages: 2094-2106, ISSN: 2211-1247, DOI: 10.1016/j.celrep.2018.01.070
Cell Reports 22/8 2020-04-23
2017 Tomi Suomi, Laura L. Elo
Enhanced differential expression statistics for data-independent acquisition proteomics
published pages: , ISSN: 2045-2322, DOI: 10.1038/s41598-017-05949-y
Scientific Reports 7/1 2020-04-23
2018 Maria K. Jaakkola, Aidan J. McGlinchey, Riku Klén, Laura L. Elo
PASI: A novel pathway method to identify delicate group effects
published pages: e0199991, ISSN: 1932-6203, DOI: 10.1371/journal.pone.0199991
PLOS ONE 13/7 2020-04-23
2017 Tommi Välikangas, Tomi Suomi, Laura L. Elo
A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation
published pages: , ISSN: 1467-5463, DOI: 10.1093/bib/bbx054
Briefings in Bioinformatics 2020-04-23
2017 Sohrab Saraei, Tomi Suomi, Otto Kauko, Laura L Elo
Phosphonormalizer: an R package for normalization of MS-based label-free phosphoproteomics
published pages: , ISSN: 1367-4803, DOI: 10.1093/bioinformatics/btx573
Bioinformatics 2020-04-23
2018 Tommi Välikangas, Tomi Suomi, Laura L. Elo
A systematic evaluation of normalization methods in quantitative label-free proteomics
published pages: bbw095, ISSN: 1467-5463, DOI: 10.1093/bib/bbw095
Briefings in Bioinformatics 2020-04-23

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