| Discovering microRNAs from deep sequencing data using miRDeep MR Friedländer, W Chen, C Adamidi, J Maaskola, R Einspanier, ... Nature biotechnology 26 (4), 407-415, 2008 | 1316 | 2008 |
| Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity E Berglund, J Maaskola, N Schultz, S Friedrich, M Marklund, ... Nature communications 9 (1), 2419, 2018 | 435 | 2018 |
| Spatially resolved transcriptomics enables dissection of genetic heterogeneity in stage III cutaneous malignant melanoma K Thrane, H Eriksson, J Maaskola, J Hansson, J Lundeberg Cancer research 78 (20), 5970-5979, 2018 | 264 | 2018 |
| Integrating spatial gene expression and breast tumour morphology via deep learning B He, L Bergenstråhle, L Stenbeck, A Abid, A Andersson, Å Borg, ... Nature biomedical engineering 4 (8), 827-834, 2020 | 248 | 2020 |
| doRiNA: a database of RNA interactions in post-transcriptional regulation G Anders, SD Mackowiak, M Jens, J Maaskola, A Kuntzagk, N Rajewsky, ... Nucleic acids research 40 (D1), D180-D186, 2012 | 233 | 2012 |
| Large-scale sorting of C. elegans embryos reveals the dynamics of small RNA expression M Stoeckius, J Maaskola, T Colombo, HP Rahn, MR Friedländer, N Li, ... Nature methods 6 (10), 745-751, 2009 | 117 | 2009 |
| Integrative analysis revealed the molecular mechanism underlying RBM 10‐mediated splicing regulation Y Wang, A Gogol‐Döring, H Hu, S Fröhler, Y Ma, M Jens, J Maaskola, ... EMBO molecular medicine 5 (9), 1431-1442, 2013 | 116 | 2013 |
| Spatially resolved clonal copy number alterations in benign and malignant tissue A Erickson, M He, E Berglund, M Marklund, R Mirzazadeh, N Schultz, ... Nature 608 (7922), 360-367, 2022 | 103 | 2022 |
| The SNF2‐like helicase HELLS mediates E2F3‐dependent transcription and cellular transformation B Von Eyss, J Maaskola, S Memczak, K Möllmann, A Schuetz, ... The EMBO journal 31 (4), 972-985, 2012 | 97 | 2012 |
| Super-resolved spatial transcriptomics by deep data fusion L Bergenstråhle, B He, J Bergenstråhle, X Abalo, R Mirzazadeh, K Thrane, ... Nature biotechnology 40 (4), 476-479, 2022 | 78 | 2022 |
| Select microRNAs are essential for early development in the sea urchin JL Song, M Stoeckius, J Maaskola, M Friedländer, N Stepicheva, ... Developmental biology 362 (1), 104-113, 2012 | 68 | 2012 |
| Spatial detection of fetal marker genes expressed at low level in adult human heart tissue M Asp, F Salmén, PL Ståhl, S Vickovic, U Felldin, M Löfling, ... Scientific reports 7 (1), 12941, 2017 | 66 | 2017 |
| Binding site discovery from nucleic acid sequences by discriminative learning of hidden Markov models J Maaskola, N Rajewsky Nucleic acids research 42 (21), 12995-13011, 2014 | 37 | 2014 |
| Reexamining microRNA Site Accessibility in Drosophila: A Population Genomics Study K Chen, J Maaskola, ML Siegal, N Rajewsky PLoS One 4 (5), e5681, 2009 | 19 | 2009 |
| Super-resolved spatial transcriptomics by deep data fusion L Bergenstråhle, B He, J Bergenstråhle, A Andersson, J Lundeberg, J Zou, ... BioRxiv, 2020.02. 28.963413, 2020 | 17 | 2020 |
| Charting tissue expression anatomy by spatial transcriptome decomposition J Maaskola, L Bergenstråhle, A Jurek, JF Navarro, J Lagergren, ... BioRxiv, 362624, 2018 | 15 | 2018 |
| The spatial landscape of clonal somatic mutations in benign and malignant tissue A Erickson, E Berglund, M He, M Marklund, R Mirzazadeh, N Schultz, ... bioRxiv, 2021.07. 12.452018, 2021 | 7 | 2021 |
| Discriminative learning for probabilistic sequence analysis J Maaskola | 1 | 2015 |
