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Tomi Suomi

Showing results (1-10 of 47) with videos related to

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Methods in Molecular Biology (Clifton, N.J.)|November 11, 2016
Accurate Detection of Differential Expression and Splicing Using Low-Level FeaturesTomi Suomi, Laura L Elo
Immunology Letters|April 5, 2022
Statistical and machine learning methods to study human CD4<sup>+</sup> T cell proteome profilesTomi Suomi, Laura L Elo
Scientific Reports|July 21, 2017
Enhanced differential expression statistics for data-independent acquisition proteomicsTomi Suomi, Laura L Elo
Briefings in Bioinformatics|June 3, 2017
A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputationTommi Välikangas, Tomi Suomi, Laura L Elo
Briefings in Bioinformatics|October 4, 2016
A systematic evaluation of normalization methods in quantitative label-free proteomicsTommi Välikangas, Tomi Suomi, Laura L Elo
ISME Communications|November 8, 2023
Introducing untargeted data-independent acquisition for metaproteomics of complex microbial samplesSami Pietilä, Tomi Suomi, Laura L Elo
Computational and Structural Biotechnology Journal|September 23, 2022
Computational solutions for spatial transcriptomicsIivari Kleino, Paulina Frolovaitė, Tomi Suomi, et al.
Methods in Molecular Biology (Clifton, N.J.)|October 3, 2018
A Data Analysis Protocol for Quantitative Data-Independent Acquisition ProteomicsSami Pietilä, Tomi Suomi, Juhani Aakko, et al.
Bioinformatics (Oxford, England)|May 24, 2026
Clarifying the scope and capabilities of ROTS in differential expression analysisTomi Suomi, Jalmari Kettunen, Taneli Pusa, et al.
Journal of Proteome Research|September 19, 2015
Using Peptide-Level Proteomics Data for Detecting Differentially Expressed ProteinsTomi Suomi, Garry L Corthals, Olli S Nevalainen, et al.
Pageof 5

Showing results (1-10 of 47) with videos related to

Sort By:
Pageof 5
Methods in Molecular Biology (Clifton, N.J.)|November 11, 2016
Accurate Detection of Differential Expression and Splicing Using Low-Level FeaturesTomi Suomi, Laura L Elo
Immunology Letters|April 5, 2022
Statistical and machine learning methods to study human CD4<sup>+</sup> T cell proteome profilesTomi Suomi, Laura L Elo
Scientific Reports|July 21, 2017
Enhanced differential expression statistics for data-independent acquisition proteomicsTomi Suomi, Laura L Elo
Briefings in Bioinformatics|June 3, 2017
A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputationTommi Välikangas, Tomi Suomi, Laura L Elo
Briefings in Bioinformatics|October 4, 2016
A systematic evaluation of normalization methods in quantitative label-free proteomicsTommi Välikangas, Tomi Suomi, Laura L Elo
ISME Communications|November 8, 2023
Introducing untargeted data-independent acquisition for metaproteomics of complex microbial samplesSami Pietilä, Tomi Suomi, Laura L Elo
Computational and Structural Biotechnology Journal|September 23, 2022
Computational solutions for spatial transcriptomicsIivari Kleino, Paulina Frolovaitė, Tomi Suomi, et al.
Methods in Molecular Biology (Clifton, N.J.)|October 3, 2018
A Data Analysis Protocol for Quantitative Data-Independent Acquisition ProteomicsSami Pietilä, Tomi Suomi, Juhani Aakko, et al.
Bioinformatics (Oxford, England)|May 24, 2026
Clarifying the scope and capabilities of ROTS in differential expression analysisTomi Suomi, Jalmari Kettunen, Taneli Pusa, et al.
Journal of Proteome Research|September 19, 2015
Using Peptide-Level Proteomics Data for Detecting Differentially Expressed ProteinsTomi Suomi, Garry L Corthals, Olli S Nevalainen, et al.
Pageof 5