Towards the genomic sequence code of DNA fragility for machine learning

Towards the genomic sequence code of DNA fragility for machine learning

Genomic DNA breakages and the subsequent insertion and deletion mutations are important contributors to genome instability and linked diseases. Unlike the research in point mutations, the relationship between DNA sequence context and the propensity for strand breaks remains elusive. Here, by analyzi...

Bibliografiset tiedot
Päätekijät:	Pflughaupt, P, Abdullah, A, Masuda, K, Sahakyan, A
Aineistotyyppi:	Journal article
Kieli:	English
Julkaistu:	Oxford University Press 2024

Samankaltaisia teoksia

Towards the genomic sequence code of DNA fragility
Tekijä: Pflughaupt, PK
Julkaistu: (2024)

Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning
Tekijä: Masuda, K, et al.
Julkaistu: (2024)

Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning
Tekijä: Kairi Masuda, et al.
Julkaistu: (2024-08-01)

Generalised interrelations among mutation rates drive the genomic compliance of Chargaff's second parity rule
Tekijä: Pflughaupt, P, et al.
Julkaistu: (2023)

Machine learning model for sequence-driven DNA G-quadruplex formation
Tekijä: Sahakyan, A, et al.
Julkaistu: (2017)

Machine learning model for sequence-driven DNA G-quadruplex formation
Tekijä: Aleksandr B. Sahakyan, et al.
Julkaistu: (2017-11-01)

Decoding the Human Genome: Machine Learning Techniques for DNA Sequencing Analysis
Tekijä: C. Sravani, et al.
Julkaistu: (2023-01-01)

Whole genome experimental maps of DNA G-quadruplexes in multiple species
Tekijä: Marsico, G, et al.
Julkaistu: (2019)

A Fragile Image Watermarking Scheme in DWT Domain Using Chaotic Sequences and Error-Correcting Codes
Tekijä: Andy M. Ramos, et al.
Julkaistu: (2023-03-01)

Classification of fragile states based on machine learning
Tekijä: Li Yuquan, et al.
Julkaistu: (2018-01-01)

BLM prevents instability of structure-forming DNA sequences at common fragile sites.
Tekijä: Hailong Wang, et al.
Julkaistu: (2018-11-01)

Machine Learning and Safe and Inclusive Architecture for Fragile Users
Tekijä: Antonio Magarò, et al.
Julkaistu: (2019-06-01)

Prediction of subsequent fragility fractures: application of machine learning
Tekijä: Mozhdeh Zabihiyeganeh, et al.
Julkaistu: (2024-06-01)

SPLICE: A technique for generating in vitro spliced coding sequences from genomic DNA
Tekijä: Wayne L. Davies, et al.
Julkaistu: (2007-12-01)

Sequencing of Genomic DNA
Tekijä: Ulrich Mahlknecht, et al.
Julkaistu: (1999-09-01)

Mitigating Temporal Fragility in the XY Surface Code
Tekijä: Pei-Kai Tsai, et al.
Julkaistu: (2024-07-01)

Universal Features for the Classification of Coding and Non-Coding DNA Sequences
Tekijä: Nicolas Carels, et al.
Julkaistu: (2009-06-01)

Universal Features for the Classification of Coding and Non-coding DNA Sequences
Tekijä: Nicolas Carels, et al.
Julkaistu: (2009-01-01)

Fragile DNA contributes to repeated evolution
Tekijä: Claudius F. Kratochwil, et al.
Julkaistu: (2019-02-01)

Strand-specific Single-stranded DNA Sequencing (4S-seq) of E. coli genomes
Tekijä: Takahiro Masuda, et al.
Julkaistu: (2019-08-01)

REPETITIVE DNA SEQUENCES IN THE GENOMES OF PLANTS
Tekijä: A. B. Shcherban
Julkaistu: (2015-01-01)

DNA watermarks in non-coding regulatory sequences
Tekijä: Pyka Martin, et al.
Julkaistu: (2009-07-01)

Functions of human non-coding dna sequences
Tekijä: Stupar Milanko, et al.
Julkaistu: (2011-01-01)

Genome-wide identification of coding and non-coding conserved sequence tags in human and mouse genomes
Tekijä: Maggi Giorgio P, et al.
Julkaistu: (2008-06-01)

A machine learning approach for accurate and real-time DNA sequence identification
Tekijä: Yiren Wang, et al.
Julkaistu: (2021-07-01)

Inconsistencies in Neanderthal genomic DNA sequences.
Tekijä: Jeffrey D Wall, et al.
Julkaistu: (2007-10-01)

A New Method to Convert the DNA Sequence of Human to a QR Code
Tekijä: Sadoon Abdullah, et al.
Julkaistu: (2019-12-01)

DNA sequence driven machine learning for modelling replication timing
Tekijä: Ashford, J
Julkaistu: (2023)

A spontaneous ring-opening reaction leads to a repair‐resistant thymine oxidation product in genomic DNA
Tekijä: Sahakyan, A, et al.
Julkaistu: (2019)

Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data
Tekijä: Yang, Y, et al.
Julkaistu: (2017)

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA
Tekijä: Nathan Wan, et al.
Julkaistu: (2019-08-01)

Seismic Fragility Estimation Based on Machine Learning and Particle Swarm Optimization
Tekijä: Qingzhao Kong, et al.
Julkaistu: (2024-04-01)

Rapid in vitro splicing of coding sequences from genomic DNA by isothermal recombination reaction-based PCR
Tekijä: Wenxuan Chen, et al.
Julkaistu: (2016-09-01)

Genetic Code Analysis Toolkit: A novel tool to explore the coding properties of the genetic code and DNA sequences
Tekijä: K. Kraljić, et al.
Julkaistu: (2018-01-01)

DNA topoisomerases participate in fragility of the oncogene RET.
Tekijä: Laura W Dillon, et al.
Julkaistu: (2013-01-01)

Cataloging coding sequence variations in human genome databases.
Tekijä: Hong-Hee Won, et al.
Julkaistu: (2008-01-01)

Machine learning-based detection of immune-mediated diseases from genome-wide cell-free DNA sequencing datasets
Tekijä: Huiwen Che, et al.
Julkaistu: (2022-09-01)

Towards a Measure of Financial Fragility
Tekijä: Tsomocos, D, et al.
Julkaistu: (2007)

Towards a measure of financial fragility
Tekijä: Aspachs, O, et al.
Julkaistu: (2006)

Detecting overlapping coding sequences in virus genomes
Tekijä: Brown Chris M, et al.
Julkaistu: (2006-02-01)