A compendium of promoter-centered long-range chromatin interactions in the human genome

Model-based Analysis of ChIP-Seq (MACS)

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

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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

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Integrative analysis of 111 reference human epigenomes

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The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans

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Master Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes

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Potential etiologic and functional implications of genome-wide association loci for human diseases and traits

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Systematic Localization of Common Disease-Associated Variation in Regulatory DNA

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The NHGRI GWAS Catalog, a curated resource of SNP-trait associations

Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1229 · Cited by 2,707

Genomics and epigenetics guided identification of tissue-specific genomic safe harbors

Shrestha, D., Bag, A., Wu, R., Zhang, Y., Tang, X., Qi, Q., Xing, J., & Cheng, Y. (2022) · Genome biology · doi:10.1186/s13059-022-02770-3 · Cited by 25

Single-cell transcriptome analyses reveal distinct gene expression signatures of severe COVID-19 in the presence of clonal hematopoiesis

Choi, B., Kang, C. K., Park, S., Lee, D., Lee, A., Ko, Y., Kang, S., Kang, K., Kim, S., Koh, Y., et al. (2022) · Experimental & Molecular Medicine · doi:10.1038/s12276-022-00866-1 · Cited by 27

African American Prostate Cancer Displays Quantitatively Distinct Vitamin D Receptor Cistrome-transcriptome Relationships Regulated by BAZ1A

Manjunath, S., Hussain, S., Wani, S. A., White, J. A., Tang, H., Gray, J. S., Jafari, H., Wu, H.-C., Long, M. D., Elhussin, I. A., et al. (2023) · Cancer Research Communications · doi:10.1158/2767-9764.crc-22-0389 · Cited by 24

Understanding the function of regulatory DNA interactions in the interpretation of non-coding GWAS variants

Zhong, W., Liu, W., Chen, J. C., Sun, Q., Hu, M., & Li, Y. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.957292 · Cited by 24

Genome-Wide Analysis of Left Ventricular Maximum Wall Thickness in the UK Biobank Cohort Reveals a Shared Genetic Background With Hypertrophic Cardiomyopathy

Aung, N., Lopes, L. R., van Duijvenboden, S., Harper, A. R., Goel, A., Grace, C., Ho, C. Y., Weintraub, W. S., Kramer, C. M., Neubauer, S., et al. (2023) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.122.003716 · Cited by 23

OpenXGR: a web-server update for genomic summary data interpretation

Bao, C., Wang, S., Jiang, L., Fang, Z., Zou, K., Lin, J. C.-J., Chen, S., & Fang, H. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad357 · Cited by 22

Integration of genetic fine-mapping and multi-omics data reveals candidate effector genes for hypertension

van Duijvenboden, S., Ramírez, J., Young, W. J., Olczak, K. J., Ahmed, F., Alhammadi, M. J. A. Y., Bell, C. G., Morris, A. P., & Munroe, P. B. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.08.009 · Cited by 25

Live-cell imaging and physical modeling reveal control of chromosome folding dynamics by cohesin and CTCFPreprint

Mach, P., Kos, P., Zhan, Y., Cramard, J., Gaudin, S., Tünnermann, J., Marchi, E., Eglinger, J., Zuin, J., Kryzhanovska, M., et al. (2022) · bioRxiv · doi:10.1101/2022.03.03.482826 · Cited by 18

TADA—a machine learning tool for functional annotation-based prioritisation of pathogenic CNVs

Hertzberg, J., Mundlos, S., Vingron, M., & Gallone, G. (2022) · Genome biology · doi:10.1186/s13059-022-02631-z · Cited by 21

Convergence of YAP/TAZ, TEAD and TP63 activity is associated with bronchial premalignant severity and progression

Ning, B., Tilston-Lünel, A., Simonetti, J., Hicks‐Berthet, J., Matschulat, A. M., Pfefferkorn, R., Spira, A. E., Edwards, M., Mazzilli, S. A., Lenburg, M. E., et al. (2023) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-023-02674-5 · Cited by 22

Open Targets Genetics: An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated lociPreprint

Mountjoy, E., Schmidt, E. M., Carmona, M., Peat, G., Miranda, A., Fumis, L., Hayhurst, J. D., Buniello, A., Schwartzentruber, J., Karim, M. A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.16.299271 · Cited by 21

Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome dataPreprint

Uzun, Y., Wu, H., & Tan, K. (2020) · Genome Research · doi:10.1101/gr.267047.120 · Cited by 22

Regulatory Variant rs2535629 in ITIH3 Intron Confers Schizophrenia Risk By Regulating CTCF Binding and SFMBT1 Expression

Li, Y., Ma, C., Li, S., Wang, J., Li, W., Yang, Y., Li, X., Liu, J., Yang, J., Liu, Y., et al. (2022) · Advanced Science · doi:10.1002/advs.202104786 · Cited by 21

Improving the informativeness of Mendelian disease-derived pathogenicity scores for common disease

Kim, S. S., Dey, K. K., Weissbrod, O., Márquez‐Luna, C., Gazal, S., & Price, A. L. (2020) · Nature Communications · doi:10.1038/s41467-020-20087-2 · Cited by 19

The importance of considering regulatory domains in genome-wide analyses – the nearest gene is often wrong!

Chua, E. H. Z., Yasar, S., & Harmston, N. (2022) · Biology Open · doi:10.1242/bio.059091 · Cited by 22

Integration of single-cell multi-omics data by regression analysis on unpaired observations

Yuan, Q., & Duren, Z. (2022) · Genome biology · doi:10.1186/s13059-022-02726-7 · Cited by 19

Low input capture Hi-C (liCHi-C) identifies promoter-enhancer interactions at high-resolution

Tomás-Daza, L., Rovirosa, L., López-Martí, P., Nieto-Aliseda, A., Serra, F., Planas‐Riverola, A., Molina, Ò., McDonald, R., Ghevaert, C., Cuatrecasas, E., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-35911-8 · Cited by 22

β-Catenin–Driven Differentiation Is a Tissue-Specific Epigenetic Vulnerability in Adrenal Cancer

Mohan, D. R., Borges, K. S., Finco, I., LaPensee, C. R., Rege, J., Solon, A. L., Little, D. W., Else, T., Almeida, M. Q., Dang, D., et al. (2023) · Cancer Research · doi:10.1158/0008-5472.can-22-2712 · Cited by 20

High-throughput functional dissection of noncoding SNPs with biased allelic enhancer activity for insulin resistance-relevant phenotypes

Duan, Y.-Y., Chen, X., Zhu, R., Jia, Y., Huang, X., Zhang, M., Yang, N., Dong, S., Zeng, M., Feng, Z., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.07.002 · Cited by 18

Normalization of Chromosome Contact Maps: Matrix Balancing and Visualization

Matthey-Doret, C., Baudry, L., Mortaza, S., Moreau, P., Koszul, R., & Cournac, A. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1390-0_1 · Cited by 18

Convergence of case-specific epigenetic alterations identify a confluence of genetic vulnerabilities tied to opioid overdose

Corradin, O., Sallari, R. C., Hoang, A. T., Kassim, B. S., Hutta, G. B., Cuoto, L., Quach, B. C., Lovrenert, K., Hays, C., Gryder, B. E., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01477-y · Cited by 19

Genetic architecture of spatial electrical biomarkers for cardiac arrhythmia and relationship with cardiovascular disease

Young, W. J., Haessler, J., Benjamins, J. W., Repetto, L., Yao, J., Isaacs, A. J., Harper, A. R., Ramírez, J., Garnier, S., van Duijvenboden, S., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36997-w · Cited by 18

Chromatin regulatory dynamics of early human small intestinal development using a directed differentiation model

Hung, Y.-H., Huang, S., Dame, M. K., Yu, Q., Yu, Q. C., Zeng, Y. A., Camp, J. G., Spence, J. R., & Sethupathy, P. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1204 · Cited by 18

Common variants contribute to intrinsic human brain functional networksPreprint

Zhao, B., Li, T., Smith, S. M., Xiong, D., Wang, X., Yang, Y., Luo, T., Zhu, Z., Shan, Y., Matoba, N., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.30.229914 · Cited by 18

Decoding the Cell Atlas and Inflammatory Features of Human Intracranial Aneurysm Wall by Single‐Cell RNA Sequencing

Ji, H., Yue, L., Sun, H., Chen, R., Zhou, R., Yang, Y., Wang, R., You, C., Xiao, A., & Liu, Y. (2024) · Journal of the American Heart Association · doi:10.1161/jaha.123.032456 · Cited by 19

Genetic coupling of enhancer activity and connectivity in gene expression control

Ray-Jones, H. F., Sung, C. K., Chan, L. T., Haglund, A., Artemov, P., Rosa, M. D., Ruje, L., Burden, F., Kreuzhuber, R., Litovskikh, A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-55900-3 · Cited by 17

Promoter-anchored chromatin interactions predicted from genetic analysis of epigenomic data

Wu, Y., Qi, T., Wang, H., Zhang, F., Zheng, Z., Phillips‐Cremins, J. E., Deary, I. J., McRae, A. F., Wray, N. R., Zeng, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15587-0 · Cited by 14

Functional and genetic determinants of mutation rate variability in regulatory elements of cancer genomes

Lee, C. A., Abd-Rabbo, D., & Reimand, J. (2021) · Genome biology · doi:10.1186/s13059-021-02318-x · Cited by 18

Common variants increase risk for congenital diaphragmatic hernia within the context of de novo variants

Qiao, L., Welch, C. L., Hernan, R., Wynn, J., Krishnan, U., Zalieckas, J. M., Buchmiller, T. L., Khlevner, J., De, A., Farkouh‐Karoleski, C., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.08.024 · Cited by 15

Cis-regulatory elements: systematic identification and horticultural applications

Li, T., Zeng, W., Zhu, F., & Lü, P. (2025) · aBIOTECH · doi:10.1007/s42994-025-00237-0 · Cited by 17

Machine learning for profile prediction in genomics

Schreiber, J. M., & Singh, R. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.008 · Cited by 15

Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data

Qin, T., Lee, C., Li, S., Cavalcante, R. G., Orchard, P., Yao, H., Zhang, H., Wang, S., Patil, S. B., Boyle, A. P., et al. (2022) · Genome biology · doi:10.1186/s13059-022-02668-0 · Cited by 19

Spatial and clonality-resolved 3D cancer genome alterations reveal enhancer-hijacking as a potential prognostic marker for colorectal cancer

Kim, K., Kim, M., Lee, A., Song, S., Kang, J., Eom, J., Kang, G. H., Bae, J. M., Min, S., Kim, Y. S., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112778 · Cited by 17

Genetic basis of right and left ventricular heart shape

Burns, R., Young, W. J., Aung, N., Lopes, L. R., Elliot, P. M., Syrris, P., Villa, R. B., Sohrabi, C., Petersen, S. E., Ramírez, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53594-7 · Cited by 18

Integration of functional genomics and statistical fine-mapping systematically characterizes adult-onset and childhood-onset asthma genetic associations

Zhong, X., Mitchell, R., Billstrand, C., Thompson, E. E., Sakabe, N. J., Aneas, I., Salamone, I. M., Gu, J., Sperling, A. I., Schoettler, N., et al. (2025) · Genome Medicine · doi:10.1186/s13073-025-01459-z · Cited by 18

Enhancers navigate the three-dimensional genome to direct cell fate decisions

Kim, J., & Dean, A. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.06.005 · Cited by 15

Integration of Multimodal Data for Deciphering Brain Disorders

Chen, J., Dong, G., Song, L., Zhao, X., Cao, J., Luo, X., Feng, J., & Zhao, X. (2021) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092820-020354 · Cited by 16

Epigenomics and the kidney

Wilson, P. C., Ledru, N., & Humphreys, B. D. (2020) · Current Opinion in Nephrology & Hypertension · doi:10.1097/mnh.0000000000000602 · Cited by 15

Regulatory landscape in brain development and disease

Spiess, K., & Won, H. (2020) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2020.05.007 · Cited by 14

From GWAS variant to function: A study of ∼148,000 variants for blood cell traits

Sun, Q., Crowley, C., Huang, L., Wen, J., Chen, J. C., Bao, E. L., Auer, P. L., Lettre, G., Reiner, A. P., Sankaran, V. G., et al. (2021) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2021.100063 · Cited by 14

Extended intergenic DNA contributes to neuron-specific expression of neighboring genes in the mammalian nervous system

Jaura, R., Yeh, S.-Y., Montanera, K. N., Ialongo, A., Anwar, Z., Lu, Y., Puwakdandawa, K., & Rhee, H. S. (2022) · Nature Communications · doi:10.1038/s41467-022-30192-z · Cited by 19

Seeing the forest through the trees: prioritising potentially functional interactions from Hi-C

Liu, N., Low, W. Y., Alinejad‐Rokny, H., Pederson, S., Sadlon, T. J., Barry, S. C., & Breen, J. (2021) · Epigenetics & Chromatin · doi:10.1186/s13072-021-00417-4 · Cited by 16

Epromoters are new players in the regulatory landscape with potential pleiotropic roles

Malfait, J., Wan, J., & Spicuglia, S. (2023) · BioEssays · doi:10.1002/bies.202300012 · Cited by 19

Liver regulatory mechanisms of noncoding variants at lipid and metabolic trait loci

Pandey, G. K., Vadlamudi, S., Currin, K. W., Moxley, A. H., Nicholas, J. C., McAfee, J. C., Broadaway, K. A., & Mohlke, K. L. (2024) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2024.100275 · Cited by 13

Evolutionary Innovations in Conserved Regulatory Elements Associate With Developmental Genes in Mammals

Uebbing, S., Kocher, A. A., Baumgartner, M., Ji, Y., Bai, S., Xing, X., Nottoli, T., & Noonan, J. P. (2024) · Molecular Biology and Evolution · doi:10.1093/molbev/msae199 · Cited by 15

Redundant and non-redundant cytokine-activated enhancers control Csn1s2b expression in the lactating mouse mammary gland

Lee, H., Willi, M., Kuhns, T. M., Liu, C., & Hennighausen, L. G. (2021) · Nature Communications · doi:10.1038/s41467-021-22500-w · Cited by 15

Molecular Basis of the Function of Transcriptional Enhancers

Ibragimov, A. N., Bylino, O. V., & Shidlovskii, Y. V. (2020) · Cells · doi:10.3390/cells9071620 · Cited by 15

CTCF Promotes Long-range Enhancer-promoter Interactions and Lineage-specific Gene Expression in Mammalian CellsPreprint

Kubo, N., Ishii, H., Xiong, X., Bianco, S., Meitinger, F., Hu, R., Hocker, J. D., Conte, M., Gorkin, D. U., Yu, M., et al. (2020) · bioRxiv · doi:10.1101/2020.03.21.001693 · Cited by 10

Trimodal single-cell profiling of transcriptome, epigenome, and 3D genome in complex tissues with scHiCAR

Wei, X., Xu, Y.-Y., Yang, D.-C., Kim, K., Yi, L., Luo, W.-Z., Lin, X., Xiang, Y., Williams, A. B., Wang, X.-T., et al. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03013-7 · Cited by 16

DeepLUCIA: predicting tissue-specific chromatin loops using Deep Learning-based Universal Chromatin Interaction Annotator

Yang, D., Chung, T., & Kim, D. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac373 · Cited by 13

Noncanonical functions of microRNAs in the nucleus

Gu, J., Li, Y., Tian, Y., Zhang, Y., Cheng, Y., & Tang, Y. (2023) · Acta Biochimica et Biophysica Sinica · doi:10.3724/abbs.2023268 · Cited by 13

Implicating type 2 diabetes effector genes in relevant metabolic cellular models using promoter-focused Capture-C

Wachowski, N. A., Pippin, J. A., Boehm, K. D., Lu, S., Leonard, M. E., Manduchi, E., Parlin, U. W., Wabitsch, M., Chesi, A., Wells, A. D., et al. (2024) · Diabetologia · doi:10.1007/s00125-024-06261-x · Cited by 13

Loop Interrupted: Dysfunctional Chromatin Relations in Neurological Diseases

Behrends, M., & Engmann, O. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.732033 · Cited by 13

Discover novel disease-associated genes based on regulatory networks of long-range chromatin interactions

Wang, H., Yang, J., Zhang, Y., & Wang, J. (2020) · Methods · doi:10.1016/j.ymeth.2020.10.010 · Cited by 12

Enhancer looping protein LDB1 regulates hepatocyte gene expression by cooperating with liver transcription factors.

Liu, G., Wang, L., Wess, J., & Dean, A. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac707 · Cited by 12

Advances in profiling chromatin architecture shed light on the regulatory dynamics underlying brain disorders

Pratt, B. M., & Won, H. (2021) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2021.08.013 · Cited by 13

A computational medicine framework integrating multi-omics, systems biology, and artificial neural networks for Alzheimer's disease therapeutic discovery

Yang, Y., Diao, Y., Jiang, L., Li, F., Chen, L., Ni, M., Wang, Z., & Fang, H. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.018 · Cited by 11

A cross-disease, pleiotropy-driven approach for therapeutic target prioritization and evaluation

Bao, C., Tan, T., Wang, S., Gao, C., Lu, C., Yang, S., Diao, Y., Jiang, L., Jing, D., Chen, L., et al. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100757 · Cited by 11

Predicting brain-regional gene regulatory networks from multi-omics for Alzheimer’s disease phenotypes and Covid-19 severity

Khullar, S., & Wang, D. (2023) · Human Molecular Genetics · doi:10.1093/hmg/ddad009 · Cited by 12

Genetic evidence for serum amyloid P component as a drug target in neurodegenerative disorders

Schmidt, A. F., Finan, C., Chopade, S., Ellmerich, S., Rossor, M. N., Hingorani, A. D., & Pepys, M. B. (2024) · Open Biology · doi:10.1098/rsob.230419 · Cited by 11

Transcriptome‐wide association study for persistent hepatitis B virus infection and related hepatocellular carcinoma

Han, J., Chen, C., Wang, C., Qin, N., Huang, M., Ma, Z., Zhu, M., Dai, J., Jiang, Y., Ma, H., et al. (2020) · Liver International · doi:10.1111/liv.14577 · Cited by 11

covNorm: An R package for coverage based normalization of Hi-C and capture Hi-C data

Kim, K., & Jung, I. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.05.041 · Cited by 11

Prioritization and functional analysis of GWAS risk loci for Barrett’s esophagus and esophageal adenocarcinoma

Chen, J., Ali, M. W., Yan, L., Dighe, S. G., Dai, J. Y., Vaughan, T. L., Casey, G., & Buas, M. F. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab259 · Cited by 10

Sex Significantly Impacts the Function of Major Depression–Linked Variants In Vivo

Mulvey, B., Selmanovic, D., & Dougherty, J. D. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.02.009 · Cited by 12

Cell-type specialization in the brain is encoded by specific long-range chromatin topologiesPreprint

Winick‐Ng, W., Kukalev, A., Harabula, I., Redondo, L. Z., Szabó, D., Meijer, M., Serebreni, L., Zhang, Y., Bianco, S., Chiariello, A. M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.02.020990 · Cited by 11

Neuronal and glial 3D chromatin architecture illustrates cellular etiology of brain disordersPreprint

Hu, B., Won, H., Mah, W., Park, R., Kassim, B. S., Spiess, K., Kozlenkov, A., Crowley, C., Pochareddy, S., Li, Y., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.14.096917 · Cited by 11

PiER: web-based facilities tailored for genetic target prioritisation harnessing human disease genetics, functional genomics and protein interactions

Fang, H. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac379 · Cited by 10

Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states

Murphy, A. E., Askarova, A., Lenhard, B., Skene, N. G., & Marzi, S. J. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1212 · Cited by 10

Complex Regulatory Interactions at GDF5 Shape Joint Morphology and Osteoarthritis Disease Risk

Coveney, C. R., Maridas, D. E., Chen, H., Muthuirulan, P., Liu, Z., Jagoda, E., Yarlagadda, S., Movahhedi, M., Proffen, B. L., Dashtdar, B., et al. (2025) · Arthritis & Rheumatology · doi:10.1002/art.43231 · Cited by 10

Loop Catalog: a comprehensive HiChIP database of human and mouse samples

Reyna, J., Fetter, K., Ignacio, R., Marandi, C. C. A., Ma, A., Rao, N., Jiang, Z., Figueroa, D. S., Bhattacharyya, S., & Ay, F. (2025) · Genome biology · doi:10.1186/s13059-025-03615-5 · Cited by 9

Shifting epigenetic contexts influence regulatory variation and disease risk

Richard, D. J., & Capellini, T. D. (2021) · Aging · doi:10.18632/aging.203194 · Cited by 10

Breaking paradigms: Long non-coding RNAs forming gene fusions with potential implications in cancer

Sánchez-Marín, D., Silva-Cázares, M. B., Porras-Reyes, F. I., García‐Román, R., & Campos-Parra, A. D. (2023) · Genes & Diseases · doi:10.1016/j.gendis.2023.101136 · Cited by 10

A network-biology approach for identification of key genes and pathways involved in malignant peritoneal mesothelioma

Mahfuz, A. M. U. B., Zubair-Bin-Mahfuj, A. M., & Podder, D. J. (2021) · Genomics & Informatics · doi:10.5808/gi.21019 · Cited by 10

Integrative analysis of the 3D genome and epigenome in mouse embryonic tissues

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Genome-wide analysis of heart failure yields insights into disease heterogeneity and enables prognostic prediction in the Japanese population

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