Background:With the development of rapid and cheap sequencing techniques,the cost of whole-genome sequencing(WGS)has dropped significantly.However,the complexity of the human genome is not limited to the pure sequence...Background:With the development of rapid and cheap sequencing techniques,the cost of whole-genome sequencing(WGS)has dropped significantly.However,the complexity of the human genome is not limited to the pure sequenceand additional experiments are required to learn the human genome's influence on complex traits.One of the most exciting aspects for scientists nowadays is the spatial organisation of the genome,which can be discovered using spatial experiments(e.g.,Hi-C,ChIA-PET).The information about the spatial contacts helps in the analysis and brings new insights into our understanding of the disease developments.Methods:We have used an ensemble of deep learning with classical machine learning algorithms.The deep learning network we used was DNABERT,which utilises the BERT language model(based on transformers)for the genomic function.The classical machine learning models included support vector machines(SVMs),random forests(RFs),and K-nearest neighbor(KNN).The whole approach was wrapped together as deep hybrid learning(DHL).Results:We found that the DNABERT can be used to predict the ChIA-PET experiments with high precision.Additionally,the DHL approach has increased the metrics on CTCF and RNAPII sets.Conclusions:DHL approach should be taken into consideration for the models utilising the power of deep learning.While straightforward in the concept,it can improve the results significantly.展开更多
基金supported by National Science Centre,Poland(Nos.2019/35/O/ST6/02484 and 2020/37/B/NZ2/03757)Foundation for Polish Science,co-financed by the European Union under the European Regional Development Fund(TEAM to DP)The work has been co-supported by European Commission Horizon 2020 Marie Skodowska-Curie ITN Enhpathy grant“Molecular Basis of Human enhanceropathies”and National Institute of Health USA 4DNucleome grant 1U54DK107967-01“Nucleome Positioning System for Spatiotemporal Genome Organization and Regulation”:Research was co-funded by Warsaw University of Technology within the Excellence Initiative:Research University(IDUB)programme.Computations were performed thanks to the Laboratory of Bioinformatics and Computational Genomics,Faculty of Mathematics and Information Science,Warsaw University of Technology using the Artificial Intelligence HPC platform financed by Polish Ministry of Science and Higher Education(No.7054/IA/SP/2020 of 2020-08-28).
文摘Background:With the development of rapid and cheap sequencing techniques,the cost of whole-genome sequencing(WGS)has dropped significantly.However,the complexity of the human genome is not limited to the pure sequenceand additional experiments are required to learn the human genome's influence on complex traits.One of the most exciting aspects for scientists nowadays is the spatial organisation of the genome,which can be discovered using spatial experiments(e.g.,Hi-C,ChIA-PET).The information about the spatial contacts helps in the analysis and brings new insights into our understanding of the disease developments.Methods:We have used an ensemble of deep learning with classical machine learning algorithms.The deep learning network we used was DNABERT,which utilises the BERT language model(based on transformers)for the genomic function.The classical machine learning models included support vector machines(SVMs),random forests(RFs),and K-nearest neighbor(KNN).The whole approach was wrapped together as deep hybrid learning(DHL).Results:We found that the DNABERT can be used to predict the ChIA-PET experiments with high precision.Additionally,the DHL approach has increased the metrics on CTCF and RNAPII sets.Conclusions:DHL approach should be taken into consideration for the models utilising the power of deep learning.While straightforward in the concept,it can improve the results significantly.
