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

Patrick Pflughaupt

Showing results (1-10 of 4) with videos related to

Pageof 1
Sort By:
BMC Bioinformatics|October 14, 2025
Prior knowledge on context-driven DNA fragmentation probabilities can improve de novo genome assembly algorithmsPatrick Pflughaupt, Aleksandr B Sahakyan
Nucleic Acids Research|June 9, 2023
Generalised interrelations among mutation rates drive the genomic compliance of Chargaff's second parity rulePatrick Pflughaupt, Aleksandr B Sahakyan
Scientific Data|August 22, 2024
Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learningKairi Masuda, Adib A Abdullah, Patrick Pflughaupt, et al.
Nucleic Acids Research|October 23, 2024
Towards the genomic sequence code of DNA fragility for machine learningPatrick Pflughaupt, Adib A Abdullah, Kairi Masuda, et al.
Pageof 1

Showing results (1-10 of 4) with videos related to

Sort By:
Pageof 1
BMC Bioinformatics|October 14, 2025
Prior knowledge on context-driven DNA fragmentation probabilities can improve de novo genome assembly algorithmsPatrick Pflughaupt, Aleksandr B Sahakyan
Nucleic Acids Research|June 9, 2023
Generalised interrelations among mutation rates drive the genomic compliance of Chargaff's second parity rulePatrick Pflughaupt, Aleksandr B Sahakyan
Scientific Data|August 22, 2024
Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learningKairi Masuda, Adib A Abdullah, Patrick Pflughaupt, et al.
Nucleic Acids Research|October 23, 2024
Towards the genomic sequence code of DNA fragility for machine learningPatrick Pflughaupt, Adib A Abdullah, Kairi Masuda, et al.
Pageof 1