| A community effort to assess and improve drug sensitivity prediction algorithms JC Costello, LM Heiser, E Georgii, M Gönen, MP Menden, NJ Wang, ... Nature biotechnology 32 (12), 1202-1212, 2014 | 787 | 2014 |
| SynergyFinder 2.0: visual analytics of multi-drug combination synergies A Ianevski, AK Giri, T Aittokallio Nucleic acids research 48 (W1), W488-W493, 2020 | 686 | 2020 |
| Searching for drug synergy in complex dose–response landscapes using an interaction potency model B Yadav, K Wennerberg, T Aittokallio, J Tang Computational and structural biotechnology journal 13, 504-513, 2015 | 665 | 2015 |
| Graph-based methods for analysing networks in cell biology T Aittokallio, B Schwikowski Briefings in bioinformatics 7 (3), 243-255, 2006 | 587 | 2006 |
| SynergyFinder: a web application for analyzing drug combination dose–response matrix data A Ianevski, L He, T Aittokallio, J Tang Bioinformatics 33 (15), 2413-2415, 2017 | 528 | 2017 |
| Toward more realistic drug–target interaction predictions T Pahikkala, A Airola, S Pietilä, S Shakyawar, A Szwajda, J Tang, ... Briefings in bioinformatics 16 (2), 325-337, 2015 | 473 | 2015 |
| Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia T Pemovska, M Kontro, B Yadav, H Edgren, S Eldfors, A Szwajda, ... Cancer discovery 3 (12), 1416-1429, 2013 | 452* | 2013 |
| Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis J Tang, A Szwajda, S Shakyawar, T Xu, P Hintsanen, K Wennerberg, ... Journal of Chemical Information and Modeling 54 (3), 735-743, 2014 | 446 | 2014 |
| Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products M Kibble, N Saarinen, J Tang, K Wennerberg, S Mäkelä, T Aittokallio Natural product reports 32 (8), 1249-1266, 2015 | 397 | 2015 |
| Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies B Yadav, T Pemovska, A Szwajda, E Kulesskiy, M Kontro, R Karjalainen, ... Scientific reports 4 (1), 1-10, 2014 | 334 | 2014 |
| Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data A Ianevski, AK Giri, T Aittokallio Nature communications 13 (1), 1246, 2022 | 289 | 2022 |
| SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples A Ianevski, AK Giri, T Aittokallio Nucleic acids research 50 (W1), W739-W743, 2022 | 264 | 2022 |
| Dealing with missing values in large-scale studies: microarray data imputation and beyond T Aittokallio Briefings in bioinformatics 11 (2), 253-264, 2010 | 214 | 2010 |
| What is synergy? The Saariselkä agreement revisited J Tang, K Wennerberg, T Aittokallio Frontiers in pharmacology 6, 181, 2015 | 207 | 2015 |
| Methods for high-throughput drug combination screening and synergy scoring L He, E Kulesskiy, J Saarela, L Turunen, K Wennerberg, T Aittokallio, ... Cancer systems biology: methods and protocols, 351-398, 2018 | 201 | 2018 |
| Machine learning and feature selection for drug response prediction in precision oncology applications M Ali, T Aittokallio Biophysical reviews 11 (1), 31-39, 2019 | 190 | 2019 |
| Integrated drug profiling and CRISPR screening identify essential pathways for CAR T-cell cytotoxicity O Dufva, J Koski, P Maliniemi, A Ianevski, J Klievink, J Leitner, P Pölönen, ... Blood, The Journal of the American Society of Hematology 135 (9), 597-609, 2020 | 189 | 2020 |
| Genome-wide profiling of interleukin-4 and STAT6 transcription factor regulation of human Th2 cell programming LL Elo, H Järvenpää, S Tuomela, S Raghav, H Ahlfors, K Laurila, B Gupta, ... Immunity 32 (6), 852-862, 2010 | 176 | 2010 |
| Susceptibility of low-density lipoprotein particles to aggregate depends on particle lipidome, is modifiable, and associates with future cardiovascular deaths M Ruuth, SD Nguyen, T Vihervaara, M Hilvo, TD Laajala, PK Kondadi, ... European heart journal 39 (27), 2562-2573, 2018 | 172 | 2018 |
| Regularized machine learning in the genetic prediction of complex traits S Okser, T Pahikkala, A Airola, T Salakoski, S Ripatti, T Aittokallio PLoS genetics 10 (11), e1004754, 2014 | 170 | 2014 |
