| Dynamics of clonal evolution in myelodysplastic syndromes H Makishima, T Yoshizato, K Yoshida, MA Sekeres, T Radivoyevitch, ... Nature genetics 49 (2), 204, 2017 | 432 | 2017 |
| The human body at cellular resolution: the NIH Human Biomolecular Atlas Program S Lin, A Posgai, M Atkinson, A Regev, J Rood, O Rozenblatt-Rosen, ... Nature Publishing Group UK, 2019 | 388* | 2019 |
| Comparative analysis of algorithms for next-generation sequencing read alignment M Ruffalo, T LaFramboise, M Koyutürk Bioinformatics 27 (20), 2790-2796, 2011 | 356 | 2011 |
| Network-based integration of disparate omic data to identify" silent players" in cancer M Ruffalo, M Koyutürk, R Sharan PLoS computational biology 11 (12), e1004595, 2015 | 72 | 2015 |
| A web server for comparative analysis of single-cell RNA-seq data A Alavi, M Ruffalo, A Parvangada, Z Huang, Z Bar-Joseph Nature communications 9 (1), 4768, 2018 | 51 | 2018 |
| Accurate estimation of short read mapping quality for next-generation genome sequencing M Ruffalo, M Koyutürk, S Ray, T LaFramboise Bioinformatics 28 (18), i349-i355, 2012 | 48 | 2012 |
| Protein interaction disruption in cancer M Ruffalo, Z Bar-Joseph BMC cancer 19 (1), 370, 2019 | 14 | 2019 |
| Genome wide predictions of miRNA regulation by transcription factors M Ruffalo, Z Bar-Joseph Bioinformatics 32 (17), i746-i754, 2016 | 13 | 2016 |
| Whole-exome sequencing enhances prognostic classification of myeloid malignancies M Ruffalo, H Husseinzadeh, H Makishima, B Przychodzen, M Ashkar, ... Journal of biomedical informatics 58, 104-113, 2015 | 12 | 2015 |
| Construction of integrated microRNA and mRNA immune cell signatures to predict survival of patients with breast and ovarian cancer M Ray, MM Ruffalo, Z Bar‐Joseph Genes, Chromosomes and Cancer 58 (1), 34-42, 2019 | 10 | 2019 |
| Reconstructing cancer drug response networks using multitask learning M Ruffalo, P Stojanov, VK Pillutla, R Varma, Z Bar-Joseph BMC systems biology 11 (1), 96, 2017 | 10 | 2017 |
| CINS: Cell Interaction Network inference from Single cell expression data Y Yuan, C Cosme, TS Adams, J Schupp, K Sakamoto, N Xylourgidis, ... bioRxiv, 2021 | 7 | 2021 |
| Network-guided prediction of aromatase inhibitor response in breast cancer M Ruffalo, R Thomas, J Chen, AV Lee, S Oesterreich, Z Bar-Joseph PLoS computational biology 15 (2), e1006730, 2019 | 7 | 2019 |
| Using single cell atlas data to reconstruct regulatory networks Q Song, M Ruffalo, Z Bar-Joseph Nucleic Acids Research, gkad053, 2023 | 5 | 2023 |
| A Unified Pipeline for FISH Spatial Transcriptomics C Cisar, N Keener, M Ruffalo, B Paten bioRxiv, 2023.02. 17.529010, 2023 | 5 | 2023 |
| In analogy to AML, MDS can be sub-classified by ancestral mutations H Makishima, K Yoshida, T LaFramboise, BP Przychodzen, M Ruffalo, ... Blood 124 (21), 823-823, 2014 | 5 | 2014 |
| Clinical “MUTATOME” Of Myelodysplastic Syndrome; Comparison To Primary Acute Myelogenous Leukemia T LaFramboise, BP Przychodzen, K Yoshida, M Ruffalo, I Gómez-Seguí, ... Blood 122 (21), 518-518, 2013 | 5 | 2013 |
| Serial sequencing in myelodysplastic syndromes reveals dynamic changes in clonal architecture and allows for a new prognostic assessment of mutations detected in cross … H Makishima, K Yoshida, T LaFramboise, T Yoshizato, M Ruffalo, ... Blood 126 (23), 709-709, 2015 | 3 | 2015 |
| A unified analysis of atlas single cell data H Chen, ND Nguyen, M Ruffalo, Z Bar-Joseph bioRxiv, 2022.08. 06.503038, 2022 | 2 | 2022 |
| Whole exome sequencing to predict response to hypomethylating agents in MDS HD Husseinzadeh, EP Evans, K Yoshida, H Makishima, A Jerez, ... Blood 120 (21), 1698-1698, 2012 | 2 | 2012 |
