| The International Human Epigenome Consortium: a blueprint for scientific collaboration and discovery HG Stunnenberg, S Abrignani, D Adams, M de Almeida, L Altucci, V Amin, ... Cell 167 (5), 1145-1149, 2016 | 522 | 2016 |
| Epigenomic profiling of human CD4+ T cells supports a linear differentiation model and highlights molecular regulators of memory development P Durek, K Nordström, G Gasparoni, A Salhab, C Kressler, M De Almeida, ... Immunity 45 (5), 1148-1161, 2016 | 200 | 2016 |
| Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction F Schmidt, N Gasparoni, G Gasparoni, K Gianmoena, C Cadenas, ... Nucleic acids research 45 (1), 54-66, 2017 | 110 | 2017 |
| DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data B Ranjan, W Sun, J Park, K Mishra, F Schmidt, R Xie, F Alipour, V Singhal, ... Nature Communications 12 (1), 5849, 2021 | 46 | 2021 |
| TEPIC 2—an extended framework for transcription factor binding prediction and integrative epigenomic analysis F Schmidt, F Kern, P Ebert, N Baumgarten, MH Schulz Bioinformatics 35 (9), 1608-1609, 2019 | 40 | 2019 |
| Unique and assay specific features of NOMe-, ATAC-and DNase I-seq data KJV Nordström, F Schmidt, N Gasparoni, A Salhab, G Gasparoni, K Kattler, ... Nucleic acids research 47 (20), 10580-10596, 2019 | 35 | 2019 |
| Integrative prediction of gene expression with chromatin accessibility and conformation data F Schmidt, F Kern, MH Schulz Epigenetics & chromatin 13 (1), 4, 2020 | 31 | 2020 |
| RegulatorTrail: a web service for the identification of key transcriptional regulators T Kehl, L Schneider, F Schmidt, D Stöckel, N Gerstner, C Backes, ... Nucleic acids research 45 (W1), W146-W153, 2017 | 26 | 2017 |
| EpiRegio: analysis and retrieval of regulatory elements linked to genes N Baumgarten, D Hecker, S Karunanithi, F Schmidt, M List, MH Schulz Nucleic Acids Research 48 (W1), W193-W199, 2020 | 25 | 2020 |
| Machine learning for deciphering cell heterogeneity and gene regulation M Scherer, F Schmidt, O Lazareva, J Walter, J Baumbach, MH Schulz, ... Nature Computational Science 1 (3), 183-191, 2021 | 22 | 2021 |
| scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data B Ranjan, F Schmidt, W Sun, J Park, MA Honardoost, J Tan, N Arul Rayan, ... BMC bioinformatics 22, 1-15, 2021 | 20 | 2021 |
| Temporal enhancer profiling of parallel lineages identifies AHR and GLIS1 as regulators of mesenchymal multipotency D Gérard, F Schmidt, A Ginolhac, M Schmitz, R Halder, P Ebert, ... Nucleic acids research 47 (3), 1141-1163, 2019 | 20 | 2019 |
| Integrative analysis of epigenetics data identifies gene-specific regulatory elements F Schmidt, A Marx, N Baumgarten, M Hebel, M Wegner, M Kaulich, ... Nucleic acids research 49 (18), 10397-10418, 2021 | 19 | 2021 |
| An ontology-based method for assessing batch effect adjustment approaches in heterogeneous datasets F Schmidt, M List, E Cukuroglu, S Köhler, J Göke, MH Schulz Bioinformatics 34 (17), i908-i916, 2018 | 16 | 2018 |
| On the problem of confounders in modeling gene expression F Schmidt, MH Schulz Bioinformatics 35 (4), 711-719, 2019 | 13 | 2019 |
| A single-cell atlas identifies pretreatment features of primary imatinib resistance in chronic myeloid leukemia V Krishnan, F Schmidt, Z Nawaz, PN Venkatesh, KL Lee, X Ren, ZE Chan, ... Blood, The Journal of the American Society of Hematology 141 (22), 2738-2755, 2023 | 12 | 2023 |
| RCA2: a scalable supervised clustering algorithm that reduces batch effects in scRNA-seq data F Schmidt, B Ranjan, QXX Lin, V Krishnan, I Joanito, MA Honardoost, ... Nucleic Acids Research 49 (15), 8505-8519, 2021 | 11 | 2021 |
| Prediction of single-cell gene expression for transcription factor analysis F Behjati Ardakani, K Kattler, T Heinen, F Schmidt, D Feuerborn, ... GigaScience 9 (11), giaa113, 2020 | 11 | 2020 |
| Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models J Grau, F Schmidt, MH Schulz Nucleic Acids Research 51 (18), e95-e95, 2023 | 9 | 2023 |
| Predicting transcription factor binding using ensemble random forest models FB Ardakani, F Schmidt, MH Schulz F1000Research 7, 2018 | 9 | 2018 |
Marcel H SchulzProfessor of Computational Biology, Goethe University Frankfurt, Germany在 em.uni-frankfurt.de 的电子邮件经过验证
