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

Sima Ranjbari

Showing results (1-10 of 3) with videos related to

Pageof 1
Sort By:
Journal of Biomedical Informatics|October 9, 2023
Integration of incomplete multi-omics data using Knowledge Distillation and Supervised Variational Autoencoders for disease progression predictionSima Ranjbari, Suzan Arslanturk
Computers in Biology and Medicine|April 10, 2025
From Silos to Synthesis: A comprehensive review of domain adaptation strategies for multi-source data integration in healthcareShelia Rahman Tuly, Sima Ranjbari, Ekrem Alper Murat, et al.
BMC Medical Informatics and Decision Making|January 3, 2021
CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the featuresSima Ranjbari, Toktam Khatibi, Ahmad Vosough Dizaji, et al.
Pageof 1

Showing results (1-10 of 3) with videos related to

Sort By:
Pageof 1
Journal of Biomedical Informatics|October 9, 2023
Integration of incomplete multi-omics data using Knowledge Distillation and Supervised Variational Autoencoders for disease progression predictionSima Ranjbari, Suzan Arslanturk
Computers in Biology and Medicine|April 10, 2025
From Silos to Synthesis: A comprehensive review of domain adaptation strategies for multi-source data integration in healthcareShelia Rahman Tuly, Sima Ranjbari, Ekrem Alper Murat, et al.
BMC Medical Informatics and Decision Making|January 3, 2021
CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the featuresSima Ranjbari, Toktam Khatibi, Ahmad Vosough Dizaji, et al.
Pageof 1