ArticleScientific data2024
Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Analysis of long-range contacts across cell types outlines a core sequence determinant of 3D genome organization.NAR genomics and bioinformatics · 2025Article
- Prior knowledge on context-driven DNA fragmentation probabilities can improve de novo genome assembly algorithms.BMC bioinformatics · 2025Article
- The emerging sequence grammar of 3D genome organisation.Human genetics · 2025Review
- Towards the genomic sequence code of DNA fragility for machine learning.Nucleic acids research · 2024Article
- Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning.Scientific data · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We are witnessing a steep increase in model development initiatives in genomics that employ high-end machine learning methodologies. Of particular interest are models that predict certain genomic characteristics based solely on DNA sequence. These models, however, treat the DNA as a mere collection of four, A, T, G and C, letters, dismissing the past advancements in science that can enable the use of more intricate information from nucleic acid sequences. Here, we provide a comprehensive database of quantum mechanical (QM) and geometric features for all the permutations of 7-meric DNA in their representative B, A and Z conformations. The database is generated by employing the applicable high-cost and time-consuming QM methodologies. This can thus make it seamless to associate a wealth of novel molecular features to any DNA sequence, by scanning it with a matching k-meric window and pulling the pre-computed values from our database for further use in modelling. We demonstrate the usefulness of our deposited features through their exclusive use in developing a model for A->C mutation rates.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.