Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Mattias Rantalainen

Showing results (1-10 of 69) with videos related to

Pageof 7
Sort By:
Methods in Molecular Biology (Clifton, N.J.)|February 14, 2015
Combining metabonomics and other -omics dataMattias Rantalainen
Briefings in Functional Genomics|November 7, 2017
Application of single-cell sequencing in human cancerMattias Rantalainen
Journal of Proteome Research|October 21, 2011
Accounting for control mislabeling in case-control biomarker studiesMattias Rantalainen, Chris C Holmes
BMC Medical Imaging|October 14, 2025
A mixture of experts (MoE) model to improve AI-based computational pathology prediction performance under variable levels of image blurYujie Xiang, Bojing Liu, Mattias Rantalainen
Plos One|May 21, 2015
Robust Linear Models for Cis-eQTL AnalysisMattias Rantalainen, Cecilia M Lindgren, Christopher C Holmes
Scientific Reports|February 3, 2016
Study design requirements for RNA sequencing-based breast cancer diagnosticsArvind Singh Mer, Daniel Klevebring, Henrik Grönberg, et al.
Heliyon|July 18, 2024
Ensemble-based deep learning improves detection of invasive breast cancer in routine histopathology imagesLeslie Solorzano, Stephanie Robertson, Balazs Acs, et al.
Modern Pathology : an Official Journal of the United States and Canadian Academy of Pathology, Inc|July 27, 2025
Histological Grade Has Clinical Validity in Neoadjuvant-Treated Breast Cancer: A Multicenter StudySanna Steen, Constance Boissin, Mattias Rantalainen, et al.
BMC Cancer|December 11, 2024
Prediction of gene expression-based breast cancer proliferation scores from histopathology whole slide images using deep learningAndreas Ekholm, Yinxi Wang, Johan Vallon-Christersson, et al.
BMC Bioinformatics|February 21, 2008
K-OPLS package: kernel-based orthogonal projections to latent structures for prediction and interpretation in feature spaceMax Bylesjö, Mattias Rantalainen, Jeremy K Nicholson, et al.
Pageof 7

Showing results (1-10 of 69) with videos related to

Sort By:
Pageof 7
Methods in Molecular Biology (Clifton, N.J.)|February 14, 2015
Combining metabonomics and other -omics dataMattias Rantalainen
Briefings in Functional Genomics|November 7, 2017
Application of single-cell sequencing in human cancerMattias Rantalainen
Journal of Proteome Research|October 21, 2011
Accounting for control mislabeling in case-control biomarker studiesMattias Rantalainen, Chris C Holmes
BMC Medical Imaging|October 14, 2025
A mixture of experts (MoE) model to improve AI-based computational pathology prediction performance under variable levels of image blurYujie Xiang, Bojing Liu, Mattias Rantalainen
Plos One|May 21, 2015
Robust Linear Models for Cis-eQTL AnalysisMattias Rantalainen, Cecilia M Lindgren, Christopher C Holmes
Scientific Reports|February 3, 2016
Study design requirements for RNA sequencing-based breast cancer diagnosticsArvind Singh Mer, Daniel Klevebring, Henrik Grönberg, et al.
Heliyon|July 18, 2024
Ensemble-based deep learning improves detection of invasive breast cancer in routine histopathology imagesLeslie Solorzano, Stephanie Robertson, Balazs Acs, et al.
Modern Pathology : an Official Journal of the United States and Canadian Academy of Pathology, Inc|July 27, 2025
Histological Grade Has Clinical Validity in Neoadjuvant-Treated Breast Cancer: A Multicenter StudySanna Steen, Constance Boissin, Mattias Rantalainen, et al.
BMC Cancer|December 11, 2024
Prediction of gene expression-based breast cancer proliferation scores from histopathology whole slide images using deep learningAndreas Ekholm, Yinxi Wang, Johan Vallon-Christersson, et al.
BMC Bioinformatics|February 21, 2008
K-OPLS package: kernel-based orthogonal projections to latent structures for prediction and interpretation in feature spaceMax Bylesjö, Mattias Rantalainen, Jeremy K Nicholson, et al.
Pageof 7