Sequence-based modeling of three-dimensional genome architecture from kilobase to chromosome scale

A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping

Rao, S. S. P., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., Sanborn, A. L., Machol, I., Omer, A. D., Lander, E. S., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.021 · Cited by 9,212

Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome

Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898

Topological domains in mammalian genomes identified by analysis of chromatin interactions

Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J. S., & Ren, B. (2012) · Nature · doi:10.1038/nature11082 · Cited by 6,829

Capturing Chromosome Conformation

Dekker, J., Rippe, K., Dekker, M., & Kleckner, N. (2002) · Science · doi:10.1126/science.1067799 · Cited by 3,652

Spatial partitioning of the regulatory landscape of the X-inactivation centre

Nora, E. P., Lajoie, B. R., Schulz, E. G., Giorgetti, L., Okamoto, I., Servant, N., Piolot, T., van Berkum, N. L., Meisig, J., Sedat, J., et al. (2012) · Nature · doi:10.1038/nature11049 · Cited by 2,895

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3300 · Cited by 2,873

Juicebox Provides a Visualization System for Hi-C Contact Maps with Unlimited Zoom

Durand, N. C., Robinson, J., Shamim, M. S., Machol, I., Mesirov, J. P., Lander, E. S., & Aiden, E. L. (2016) · Cell Systems · doi:10.1016/j.cels.2015.07.012 · Cited by 2,421

Predicting effects of noncoding variants with deep learning–based sequence model

Zhou, J., & Troyanskaya, O. G. (2015) · Nature Methods · doi:10.1038/nmeth.3547 · Cited by 2,338

Disruptions of Topological Chromatin Domains Cause Pathogenic Rewiring of Gene-Enhancer Interactions

Lupiáñez, D. G., Kraft, K., Heinrich, V., Krawitz, P., Brancati, F., Klopocki, E., Horn, D., Kayserili, H., Opitz, J. M., Laxova, R., et al. (2015) · Cell · doi:10.1016/j.cell.2015.04.004 · Cited by 2,132

Formation of chromosomal domains by loop extrusion

Fudenberg, G., Imakaev, M., Lu, C., Goloborodko, A., Abdennur, N., & Mirny, L. A. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.04.085 · Cited by 2,003

AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model

Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2025) · doi:10.1101/2025.06.25.661532 · Cited by 395

Advancing regulatory variant effect prediction with AlphaGenome

Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2026) · Nature · doi:10.1038/s41586-025-10014-0 · Cited by 373

Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant’s Abiotic Stress Tolerance Responses

Roychowdhury, R., Das, S. P., Gupta, A., Parihar, P., Chandrasekhar, K., Sarker, U., Kumar, A., Ramrao, D. P., & Sudhakar, C. (2023) · Genes · doi:10.3390/genes14061281 · Cited by 333

Chromatin alternates between A and B compartments at kilobase scale for subgenic organization

Harris, H. L., Gu, H., Olshansky, M., Wang, A., Farabella, I., Eliaz, Y., Kalluchi, A., Krishna, A., Jacobs, M., Cauer, G., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38429-1 · Cited by 184

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings

Sasse, A., Ng, B., Spiro, A. E., Tasaki, S., Bennett, D. A., Gaiteri, C., De Jager, P. L., Chikina, M., & Mostafavi, S. (2023) · Nature Genetics · doi:10.1038/s41588-023-01524-6 · Cited by 139

Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers

Karollus, A., Mauermeier, T., & Gagneur, J. (2023) · Genome Biology · doi:10.1186/s13059-023-02899-9 · Cited by 128

Personal transcriptome variation is poorly explained by current genomic deep learning models

Huang, C., Shuai, R. W., Baokar, P., Chung, R., Rastogi, R., Kathail, P., & Ioannidis, N. M. (2023) · Nature Genetics · doi:10.1038/s41588-023-01574-w · Cited by 127

Cell-type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening

Tan, J., Shenker-Tauris, N., Rodriguez-Hernaez, J., Wang, E., Sakellaropoulos, T., Boccalatte, F. E., Thandapani, P., Skok, J. A., Aifantis, I., Fenyö, D., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01612-8 · Cited by 123

A foundation model of transcription across human cell types

(2025) · doi:10.1038/s41586-024-08391-z · Cited by 130

Sequence basis of transcription initiation in the human genome

Dudnyk, K., Cai, D., Shi, C., Xu, J., & Zhou, J. (2024) · Science · doi:10.1126/science.adj0116 · Cited by 115

Structural variants drive context-dependent oncogene activation in cancer

Xu, Z., Lee, D.-S., Chandran, S., Le, V. T., Bump, R., Yasis, J., Dallarda, S., Marcotte, S., Clock, B., Haghani, N. K., et al. (2022) · Nature · doi:10.1038/s41586-022-05504-4 · Cited by 98

Transformers and genome language models

Consens, M. E., Dufault, C., Wainberg, M., Forster, D., Karimzadeh, M., Goodarzi, H., Theis, F. J., Moses, A. M., & Wang, B. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01007-9 · Cited by 170

The 3D architecture of the pepper genome and its relationship to function and evolution

Liao, Y., Wang, J., Zhu, Z., Liu, Y., Chen, J., Zhou, Y., Liu, F., Lei, J., Gaut, B. S., Cao, B., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31112-x · Cited by 88

Computational methods for analysing multiscale 3D genome organization

Zhang, Y., Boninsegna, L., Yang, M., Misteli, T., Alber, F., & Ma, J. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00638-1 · Cited by 97

Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo

de Almeida, B. P., Schaub, C., Pagani, M., Secchia, S., Furlong, E. E. M., & Stark, A. (2024) · Nature · doi:10.1038/s41586-023-06905-9 · Cited by 87

Massively parallel characterization of regulatory elements in the developing human cortex

Deng, C., Whalen, S., Steyert, M., Ziffra, R., Przytycki, P. F., Inoue, F., Pereira, D. A., Capauto, D., Norton, S., Vaccarino, F. M., et al. (2024) · Science · doi:10.1126/science.adh0559 · Cited by 79

Applications of deep learning in understanding gene regulation

Li, Z., Gao, E., Zhou, J., Han, W., Xu, X., & Gao, X. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100384 · Cited by 73

Evaluating deep learning for predicting epigenomic profiles

Toneyan, S., Tang, Z., & Koo, P. K. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00570-9 · Cited by 64

An integrated view of the structure and function of the human 4D nucleome

Dekker, J., Oksuz, B. A., Zhang, Y., Wang, Y., Minsk, M. K., Kuang, S., Yang, L., Gibcus, J. H., Krietenstein, N., Rando, O. J., et al. (2025) · Nature · doi:10.1038/s41586-025-09890-3 · Cited by 64

Three-dimensional genome architecture persists in a 52,000-year-old woolly mammoth skin sample

Sandoval‐Velasco, M., Dudchenko, O., Rodríguez, J. A., Estrada, C. P., Dehasque, M., Fontsere, C., Mak, S. S. T., Khan, R., Contessoto, V. G., Junior, A. B. D. O., et al. (2024) · Cell · doi:10.1016/j.cell.2024.06.002 · Cited by 52

Genomics enters the deep learning era

Routhier, E., & Mozziconacci, J. (2022) · PeerJ · doi:10.7717/peerj.13613 · Cited by 45

A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome

Zhang, Z., Feng, F., Qiu, Y., & Liu, J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad436 · Cited by 40

Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models

Seitz, E. E., McCandlish, D. M., Kinney, J. B., & Koo, P. K. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00851-5 · Cited by 42

Deep learning the cis-regulatory code of chromatin dynamics during cellular reprogrammingPreprint

Nair, S., Ameen, M., Sundaram, L., Pampari, A., Schreiber, J. M., Balsubramani, A., Wang, Y. X., Burns, D., Blau, H. M., Karakikes, I., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.04.560808 · Cited by 38

EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations

Lee, N. K., Tang, Z., Toneyan, S., & Koo, P. K. (2023) · Genome biology · doi:10.1186/s13059-023-02941-w · Cited by 41

Plant Biochemistry in the Era of Omics: Integrated Omics Approaches to Unravel the Genetic Basis of Plant Stress Tolerance

Sarfraz, Z., Zarlashat, Y., Ambreen, A., Mujahid, M., Iqbal, M. S., Fatima, S. A., Iqbal, M. S., Iqbal, R., & Fiaz, S. (2025) · Plant Breeding · doi:10.1111/pbr.13277 · Cited by 38

Unlocking gene regulation with sequence-to-function models

Sasse, A., Chikina, M. D., & Mostafavi, S. (2024) · Nature Methods · doi:10.1038/s41592-024-02331-5 · Cited by 40

Structural variants in the 3D genome as drivers of disease

Sreenivasan, V. K. A., Yumiceba, V., & Spielmann, M. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00862-x · Cited by 45

Deep Learning Sequence Models for Transcriptional Regulation

Sokolova, K., Chen, K., Hao, Y., Zhou, J., & Troyanskaya, O. G. (2024) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-021623-024727 · Cited by 39

Hijacked enhancer–promoter and silencer–promoter loops in cancer

Wang, X., & Yue, F. (2024) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2024.102199 · Cited by 32

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects

Wang, X., Li, F., Zhang, Y., Imoto, S., Shen, H., Li, S., Guo, Y., Yang, J., & Song, J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae446 · Cited by 35

ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

Gao, V. R., Yang, R., Das, A., Luo, R., Luo, H., McNally, D. R., Karagiannidis, I., Rivas, M. A., Wang, Z.-M., Barišić, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53628-0 · Cited by 29

Predicting 3D chromatin interactions from DNA sequence using Deep Learning

Piecyk, R. S., Schlegel, L., & Johannes, F. M. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.06.047 · Cited by 27

Structural variation cooperates with permissive chromatin to control enhancer hijacking–mediated oncogenic transcription

Botten, G., Zhang, Y., Dudnyk, K., Kim, Y., Liu, X., Sanders, J. T., Imanci, A., Droin, N., Cao, H., Kaphle, P., et al. (2022) · Blood · doi:10.1182/blood.2022017555 · Cited by 29

Machine Learning Methods for Exploring Sequence Determinants of 3D Genome Organization

Yang, M., & Ma, J. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167666 · Cited by 27

Exploring the roles of RNAs in chromatin architecture using deep learning

Kuang, S., & Pollard, K. S. (2024) · Nature Communications · doi:10.1038/s41467-024-50573-w · Cited by 26

ChromoGen: Diffusion model predicts single-cell chromatin conformations

Schuette, G., Lao, Z., & Zhang, B. (2025) · Science Advances · doi:10.1126/sciadv.adr8265 · Cited by 27

A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles

Wang, Y., Kong, S., Zhou, C., Zhang, Y., Zhang, Y., Fang, Y., & Li, G. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae651 · Cited by 25

RepliChrom: Interpretable machine learning predicts cancer‐associated enhancer‐promoter interactions using DNA replication timing

Dao, F., Lebeau, B., Ling, C. C. Y., Yang, M., Xie, X., Fullwood, M. J., Lin, H., & Lyu, H. (2025) · iMeta · doi:10.1002/imt2.70052 · Cited by 23

Reconstructing the 3D genome organization of Neanderthals reveals that chromatin folding shaped phenotypic and sequence divergencePreprint

McArthur, E., Rinker, D. C., Cheng, Y., Wang, Q., Wang, J., Gilbertson, E. N., Fudenberg, G., Pittman, M., Keough, K. C., Yue, F., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.07.479462 · Cited by 22

In silico discovery of repetitive elements as key sequence determinants of 3D genome folding

Gunsalus, L. M., Keiser, M. J., & Pollard, K. S. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100410 · Cited by 22

Predictive analyses of regulatory sequences with EUGENe

Klie, A., Laub, D., Talwar, J. V., Stites, H., Jores, T., Solvason, J. J., Farley, E. K., & Carter, H. K. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00544-w · Cited by 24

EpiGePT: a Pretrained Transformer model for epigenomicsPreprint

Gao, Z., Liu, Q., Zeng, W., Jiang, R., & Wong, W. H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.15.549134 · Cited by 20

Machine and Deep Learning Methods for Predicting 3D Genome Organization

Wall, B. P. G., Nguyen, M., Harrell, J. C., & Dozmorov, M. G. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4136-1_22 · Cited by 20

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution

Özçelik, F., Dündar, M. S., Yildirim, A. B., HENEHAN, G. T. M., Vicente, O., Sánchez-Alcázar, J. A., Gökçe, N., Yildirim, D. T., Bingol, N. N., Plaseska‐Karanfilska, D., et al. (2024) · Functional & Integrative Genomics · doi:10.1007/s10142-024-01417-9 · Cited by 21

Efficient Hi-C inversion facilitates chromatin folding mechanism discovery and structure prediction

Schuette, G., Ding, X., & Zhang, B. (2023) · Biophysical Journal · doi:10.1016/j.bpj.2023.07.017 · Cited by 15

Genomics and bioinformatics applications in crop stress: Unraveling molecular mechanisms for sustainable agriculture

Ebeed, H. T., & Ceasar, S. A. (2024) · South African Journal of Botany · doi:10.1016/j.sajb.2024.05.015 · Cited by 16

A comprehensive survey of genome language models in bioinformatics

Shu, L., Tang, J., Guan, X., & Zhang, D. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf724 · Cited by 22

Tangermeme: a toolkit for understanding cis-regulatory logic using deep learning models

Schreiber, J. M. (2026) · Nature Methods · doi:10.1038/s41592-026-03254-z · Cited by 16

Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases

Lan, A. Y., & Corces, M. R. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1027224 · Cited by 16

Decoding polygenic diseases: advances in noncoding variant prioritization and validation

Chin, I. M., Gardell, Z. A., & Corces, M. R. (2024) · Trends in Cell Biology · doi:10.1016/j.tcb.2024.03.005 · Cited by 18

Uncertainty-aware genomic deep learning with knowledge distillation

Zhou, J., Rizzo, K., Christensen, T. A., Tang, Z., & Koo, P. K. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-025-00053-3 · Cited by 22

Mechanistic drivers of chromatin organization into compartments

Harris, H. L., & Rowley, M. J. (2024) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2024.102193 · Cited by 13

Perspective on recent developments and challenges in regulatory and systems genomics

Zeitlinger, J., Roy, S., Ay, F., Mathelier, A., Medina-Rivera, A., Mahony, S., Sinha, S. K., & Ernst, J. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf106 · Cited by 18

Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancersPreprint

Karollus, A., Mauermeier, T., & Gagneur, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.15.508087 · Cited by 14

ChatNT: A Multimodal Conversational Agent for DNA, RNA and Protein TasksPreprint

Richard, G., de Almeida, B. P., Dalla-Torre, H., Blum, C. F., Hexemer, L., Pandey, P., Laurent, S., Lopez, M., Laterre, A., Lang, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.30.591835 · Cited by 14

Hi-C, a chromatin 3D structure technique advancing the functional genomics of immune cells

Liu, R., Xu, R., Yan, S., Li, P., Jia, C., Sun, H., Sheng, K., Wang, Y., Zhang, Q., Guo, J., et al. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1377238 · Cited by 15

Machine Learning Reveals the Diversity of Human 3D Chromatin Contact Patterns

Gilbertson, E. N., Brand, C. M., McArthur, E., Rinker, D. C., Kuang, S., Pollard, K. S., & Capra, J. A. (2024) · Molecular Biology and Evolution · doi:10.1093/molbev/msae209 · Cited by 13

ChIPr: accurate prediction of cohesin-mediated 3D genome organization from 2D chromatin features

Abbas, A., Chandratre, K., Gao, Y., Yuan, J., Zhang, M. Q., & Mani, R. S. (2024) · Genome Biology · doi:10.1186/s13059-023-03158-7 · Cited by 13

SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models

Gjoni, K., & Pollard, K. S. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae340 · Cited by 11

Genome structure mapping with high-resolution 3D genomics and deep learningPreprint

Hong, C. K., Feng, F., Ramanathan, V., Liu, J., & Hansen, A. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.06.650874 · Cited by 10

Interpreting the CTCF-mediated sequence grammar of genome folding with AkitaV2

Smaruj, P. N., Kamulegeya, F., Kelley, D. R., & Fudenberg, G. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012824 · Cited by 9

A scalable approach to investigating sequence-to-function predictions from personal genomes

Spiro, A. E., Tu, X., Sheng, Y., Sasse, A., Hosseini, R., Chikina, M. D., & Mostafavi, S. (2026) · Nature Methods · doi:10.1038/s41592-026-03124-8 · Cited by 15

A Lightweight Framework For Chromatin Loop Detection at the Single‐Cell Level

Wang, F., Alinejad‐Rokny, H., Lin, J., Gao, T., Chen, X., Zheng, Z., Meng, L., Li, X., & Wong, K. (2023) · Advanced Science · doi:10.1002/advs.202303502 · Cited by 9

An oligodendrocyte silencer element underlies the pathogenic impact of lamin B1 structural variants

Nmezi, B., Bey, G. R., Oranburg, T. D., Dudnyk, K., Lardo, S. M., Herdman, N., Jacko, A. M., Rubio, S., Loeza‐Alcocer, E., Kofler, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56378-9 · Cited by 11

Leveraging Deep Learning for Genomics Analysis

Gandhewar, N., Pimpalkar, A. P., Jadhav, A. R., Shelke, N. M., & Jain, R. (2024) · Wiley eBooks · doi:10.1002/9781394268832.ch9 · Cited by 10

Regulation associated modules reflect 3D genome modularity associated with chromatin activity

Zheng, L., & Wang, W. (2022) · Nature Communications · doi:10.1038/s41467-022-32911-y · Cited by 10

HiCDiffusion - diffusion-enhanced, transformer-based prediction of chromatin interactions from DNA sequences

Chiliński, M., & Plewczyński, D. (2024) · BMC Genomics · doi:10.1186/s12864-024-10885-z · Cited by 9

Recipes and ingredients for deep learning models of 3D genome folding

Smaruj, P. N., Xiao, Y., & Fudenberg, G. (2025) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2024.102308 · Cited by 8

De novo design of synthetic microbial genomes

Koster, C. C., Oliveira, H. D. C., van Beveren, F., Houkes, J., van Hooff, J. J. E., Ettema, T. J. G., John van der, O. O. S. T., & Claassens, N. J. (2026) · Nature Reviews Bioengineering · doi:10.1038/s44222-026-00410-0 · Cited by 8

Enhancer selectivity across cell types delineates three functionally distinct enhancer-promoter regulation patterns

Wu, C., & Huang, J. (2024) · BMC Genomics · doi:10.1186/s12864-024-10408-w · Cited by 8

Sequence-Based Machine Learning Reveals 3D Genome Differences between Bonobos and Chimpanzees

Brand, C. M., Kuang, S., Gilbertson, E. N., McArthur, E., Pollard, K. S., Webster, T. H., & Capra, J. A. (2024) · Genome Biology and Evolution · doi:10.1093/gbe/evae210 · Cited by 8

Deep learning for regulatory genomics: a survey of models, challenges, and applications

Ismail, F. N., Sengupta, A., & Amarasoma, S. (2025) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf271 · Cited by 8

Three-dimensional genome reorganization foreshadows zygotic genome activation in Drosophila

Maziak, N., Zhang, Y., Groll, F., Brown, H. E., Madich, A., Kaur, Y., Harrison, M. M., Zhou, J., & Vaquerizas, J. M. (2026) · Nature Genetics · doi:10.1038/s41588-026-02503-3 · Cited by 8

Multimodal learning of noncoding variant effects using genome sequence and chromatin structure

Tan, W., & Shen, Y. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad541 · Cited by 7

UNADON: transformer-based model to predict genome-wide chromosome spatial position

Yang, M., & Ma, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad246 · Cited by 9

Quick and effective approximation of in silico saturation mutagenesis experiments with first-order taylor expansion

Sasse, A., Chikina, M. D., & Mostafavi, S. (2024) · iScience · doi:10.1016/j.isci.2024.110807 · Cited by 8

A generalizable Hi-C foundation model for chromatin architecture, single-cell and multiomics analysis across species

Wang, X., Zhang, Y., Ray, S., Jha, A., Fang, T., Hang, S., Doulatov, S. R., Noble, W. S., & Wang, S. (2026) · Nature Methods · doi:10.1038/s41592-026-03097-8 · Cited by 9

Unveiling chromatin dynamics with virtual epigenome

Lin, M.-Y., Lo, Y.-C., & Hung, J. (2025) · Nature Communications · doi:10.1038/s41467-025-58481-3 · Cited by 5

AI and the next medical revolution: deep learning’s uncharted healthcare promise

Krithika, L. B., Vishnu, S., Kotei, E., Gadde, A., Kothamasu, G. A., Nallabantu, S. C., & Jayabalasamy, G. (2024) · Engineering Research Express · doi:10.1088/2631-8695/ad56fc · Cited by 8

Genetic and Epigenetic Dysregulation of Transcriptional Enhancers in Cancer

Botten, G. A., & Xu, J. (2024) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-060324-114306 · Cited by 7

Hypothesis-driven probabilistic modelling enables a principled perspective of genomic compartments

Kariti, H., Feld, T., & Kaplan, N. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1258 · Cited by 5

Organization and Dynamics of Chromosomes

Thirumalai, D., Shi, G., Shin, S., & Hyeon, C. (2025) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-082423-024123 · Cited by 5

Computational Approaches for Understanding Sequence Variation Effects on the 3D Genome Architecture

Avdeyev, P., & Zhou, J. (2022) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-102521-012018 · Cited by 5

DARDN: A Deep-Learning Approach for CTCF Binding Sequence Classification and Oncogenic Regulatory Feature Discovery

Cho, H. J., Wang, Z., Cong, Y., Bekiranov, S., Zhang, A., & Zang, C. (2024) · Genes · doi:10.3390/genes15020144 · Cited by 5

Polymer Physics Models Reveal Structural Folding Features of Single-Molecule Gene Chromatin Conformations

Conte, M., Abraham, A., Esposito, A., Yang, L., Gibcus, J. H., Parsi, K. M., Vercellone, F., Fontana, A., Pierno, F. D., Dekker, J., et al. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms251810215 · Cited by 6

iEnhance: a multi-scale spatial projection encoding network for enhancing chromatin interaction data resolution

Li, K., Zhang, P., Wang, Z., Shen, W., Sun, W., Xu, J., Wen, Z., & Li, L. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad245 · Cited by 5

Omics-based large language models: A new engine for drug discovery innovation

Sheng, X., Zhang, X., Xing, Y., Shi, Y., Zeng, C., Tong, X., Zheng, M., & Li, X. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.10.034 · Cited by 5

Learning Micro-C from Hi-C with diffusion models

Liu, T., Zhu, H., & Wang, Z. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012136 · Cited by 5

The synergy between compartmentalization and motorization in chromatin architecture

de Oliveira, R. J., Junior, A. B. D. O., Contessoto, V. G., & Onuchic, J. N. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0239634 · Cited by 4

Deciphering the regulatory syntax of genomic DNA with deep learning

Lal, A. (2022) · Journal of Biosciences · doi:10.1007/s12038-022-00291-6 · Cited by 6

Training deep learning models on personalized genomic sequences improves variant effect predictionPreprint

He, A. Y., Palamuttam, N. P., & Danko, C. G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.15.618510 · Cited by 6

Shift augmentation improves DNA convolutional neural network indel effect predictionsPreprint

Korsakova, A., Srivastava, D., & Kelley, D. R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.07.647656 · Cited by 7

Decoding silent regulators: Integrating multi-omics and machine learning reveal non-coding drivers of arsenic stress tolerance in rice

Chakraborty, J., Saha, R., Roy, S., Mal, C., & Bhattacharya, S. (2026) · Plant Gene · doi:10.1016/j.plgene.2026.100587 · Cited by 3

HYENA detects oncogenes activated by distal enhancers in cancer

Yu, A., Yesilkanal, A. E., Thakur, A., Wang, F., Yang, Y., Phillips, W., Wu, X., Muir, A., He, X., Spitz, F., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae646 · Cited by 2

GenVarLoader: an accelerated dataloader for applying deep learning to personalized genomics

Laub, D., Ho, A., Raina, L., Jaureguy, J., Klie, A., Salem, R. M., McVicker, G., & Carter, H. K. (2026) · BMC Genomics · doi:10.1186/s12864-026-13225-5 · Cited by 2

Unraveling the three-dimensional genome structure using machine learning

Lee, J., Mo, H.-L., Ha, Y., Nam, D. Y., Lim, G., Park, J.-W., Park, S., Choi, W.-Y., Lee, H., & Rhee, J. (2025) · BMB Reports · doi:10.5483/bmbrep.2024-0020 · Cited by 2

Hierarchical Prediction and Perturbation of Chromatin Organization Reveal How Loop Domains Mediate Higher‐Order Architectures

Wei, J., Xue, Y., & Gao, Y. Q. (2025) · Advanced Science · doi:10.1002/advs.202504799 · Cited by 2

DNA shape and epigenomics distinguish the mechanistic origin of human genomic structural variations

Boev, N. B., Gerstein, M., & Kumar, S. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1325 · Cited by 2

Deciphering the 3D genome organization across species from Hi-C data

Shkolikov, A., Galitsyna, A. A., & Gelfand, M. S. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1516 · Cited by 3

Orca: Sequence-based modeling of genome 3D architecture from kilobase to chromosome-scale (Part3)

Zhou, J. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4624513

Machine Learning Methods for Exploring Sequence Determinants of 3D Genome Organization

Yang, M., & Ma, J. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167666 · Cited by 27

DeepC: predicting 3D genome folding using megabase-scale transfer learning

Schweßinger, R., Gosden, M. E., Downes, D. J., Brown, R. C., Oudelaar, A. M., Telenius, J. M., Teh, Y. W., Lunter, G. A., & Hughes, J. R. (2020) · Nature Methods · doi:10.1038/s41592-020-0960-3 · Cited by 223

Computational Approaches for Understanding Sequence Variation Effects on the 3D Genome Architecture

Avdeyev, P., & Zhou, J. (2022) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-102521-012018 · Cited by 5

Predicting Chromatin Interactions from DNA Sequence Using DeepC

Schweßinger, R. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-2962-8_3 · Cited by 1