| Acidification of tumor at stromal boundaries drives transcriptome alterations associated with aggressive phenotypes N Rohani, L Hao, MS Alexis, BA Joughin, K Krismer, MN Moufarrej, ... Cancer research 79 (8), 1952-1966, 2019 | 186 | 2019 |
| An atlas of substrate specificities for the human serine/threonine kinome JL Johnson, TM Yaron, EM Huntsman, A Kerelsky, J Song, A Regev, ... Nature 613 (7945), 759-766, 2023 | 162 | 2023 |
| Transite: a computational motif-based analysis platform that identifies RNA-binding proteins modulating changes in gene expression K Krismer, MA Bird, S Varmeh, ED Handly, A Gattinger, T Bernwinkler, ... Cell reports 32 (8), 2020 | 34 | 2020 |
| Qualifying high-throughput immune repertoire sequencing N Niklas, J Pröll, J Weinberger, A Zopf, K Wiesinger, K Krismer, ... Cellular immunology 288 (1-2), 31-38, 2014 | 20 | 2014 |
| Identification of determinants of differential chromatin accessibility through a massively parallel genome-integrated reporter assay J Hammelman, K Krismer, B Banerjee, DK Gifford, RI Sherwood Genome Research 30 (10), 1468-1480, 2020 | 18 | 2020 |
| Approach to machine learning for extraction of real-world data variables from electronic health records B Adamson, M Waskom, A Blarre, J Kelly, K Krismer, S Nemeth, J Gippetti, ... Frontiers in Pharmacology 14, 1180962, 2023 | 12 | 2023 |
| A multivariate computational method to analyze high-content RNAi screening data J Rameseder, K Krismer, Y Dayma, T Ehrenberger, MK Hwang, ... Journal of biomolecular screening 20 (8), 985-997, 2015 | 11 | 2015 |
| A global atlas of substrate specificities for the human serine/threonine kinome JL Johnson, TM Yaron, EM Huntsman, A Kerelsky, J Song, A Regev, ... BioRxiv, 2022.05. 22.492882, 2022 | 10 | 2022 |
| IDR2D identifies reproducible genomic interactions K Krismer, Y Guo, DK Gifford Nucleic acids research 48 (6), e31-e31, 2020 | 10 | 2020 |
| High resolution discovery of chromatin interactions Y Guo, K Krismer, M Closser, H Wichterle, DK Gifford Nucleic acids research 47 (6), e35-e35, 2019 | 10 | 2019 |
| spatzie: an R package for identifying significant transcription factor motif co-enrichment from enhancer–promoter interactions J Hammelman, K Krismer, DK Gifford Nucleic Acids Research 50 (9), e52-e52, 2022 | 3 | 2022 |
| seqgra: principled selection of neural network architectures for genomics prediction tasks K Krismer, J Hammelman, DK Gifford Bioinformatics 38 (9), 2381-2388, 2022 | 1 | 2022 |
| Principled Methods and Models for Deep Learning Based Functional Genomics K Krismer Massachusetts Institute of Technology, 2021 | | 2021 |
| Identify reproducible genomic peaks from replicate ChIP-seq experiments K Krismer Nucleic Acids Research 48 (6), e31, 2020 | | 2020 |
| YY1 ChIA-PET motif analysis (single call) J Hammelman, K Krismer | | |
| Machine Learning Overview S Jain, K Krismer, GS Liu | | |
| SUPPlEmEntARY MAtERiAl IDR2D identifies reproducible genomic interactions K Krismer, Y Guo, DK Gifford | | |
Yuchun GuoResearch Scientist, CSAIL, MITVerified email at mit.edu
