ArticleBMC bioinformatics2018
Dense neural networks for predicting chromatin conformation.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Learning Micro-C from Hi-C with diffusion models.PLoS computational biology · 2024Article
- Article
- Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation.Nature communications · 2022Article
- Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.Briefings in bioinformatics · 2021Article
- Interpretation of deep learning in genomics and epigenomics.Briefings in bioinformatics · 2021Review
- The Road Not Taken with Pyrrole-Imidazole Polyamides: Off-Target Effects and Genomic Binding.Biomolecules · 2020Review
- An integrative approach for fine-mapping chromatin interactions.Bioinformatics (Oxford, England) · 2020Article
- A machine learning framework for the prediction of chromatin folding inPeerJ. Computer science · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundDNA inside eukaryotic cells wraps around histones to form the 11nm chromatin fiber that can further fold into higher-order DNA loops, which may depend on the binding of architectural factors. Predicting how the DNA will fold given a distribution of bound factors, here viewed as a type of sequence, is currently an unsolved problem and several heterogeneous polymer models have shown that many features of the measured structure can be reproduced from simulations. However a model that determines the optimal connection between sequence and structure and that can rapidly assess the effects of varying either one is still lacking.
resultsHere we train a dense neural network to solve for the local folding of chromatin, connecting structure, represented as a contact map, to a sequence of bound chromatin factors. The network includes a convolutional filter that compresses the large number of bound chromatin factors into a single 1D sequence representation that is optimized for predicting structure. We also train a network to solve the inverse problem, namely given only structural information in the form of a contact map, predict the likely sequence of chromatin states that generated it.
conclusionsBy carrying out sensitivity analysis on both networks, we are able to highlight the importance of chromatin contexts and neighborhoods for regulating long-range contacts, along with critical alterations that affect contact formation. Our analysis shows that the networks have learned physical insights that are informative and intuitive about this complex polymer problem.
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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 OpenQuestion graph.