| 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 | 720 | 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 | 557 | 2020 |
| Graph-based methods for analysing networks in cell biology T Aittokallio, B Schwikowski Briefings in bioinformatics 7 (3), 243-255, 2006 | 554 | 2006 |
| 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 | 537 | 2015 |
| 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 | 468 | 2017 |
| 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 | 430* | 2013 |
| 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 | 395 | 2015 |
| 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 | 350 | 2015 |
| 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 | 321 | 2014 |
| 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), 5193, 2014 | 296 | 2014 |
| Dealing with missing values in large-scale studies: microarray data imputation and beyond T Aittokallio Briefings in bioinformatics 11 (2), 253-264, 2010 | 191 | 2010 |
| What is synergy? The Saariselkä agreement revisited J Tang, K Wennerberg, T Aittokallio Frontiers in pharmacology 6, 181, 2015 | 176 | 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 | 168 | 2018 |
| 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 | 167 | 2010 |
| Machine learning and feature selection for drug response prediction in precision oncology applications M Ali, T Aittokallio Biophysical reviews 11 (1), 31-39, 2019 | 164 | 2019 |
| Quantitative subcellular proteome and secretome profiling of influenza A virus-infected human primary macrophages N Lietzen, T Öhman, J Rintahaka, I Julkunen, T Aittokallio, S Matikainen, ... PLoS pathogens 7 (5), e1001340, 2011 | 164 | 2011 |
| A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments TD Laajala, S Raghav, S Tuomela, R Lahesmaa, T Aittokallio, LL Elo BMC genomics 10 (1), 1-15, 2009 | 160 | 2009 |
| 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 | 157 | 2014 |
| Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open … J Guinney, T Wang, TD Laajala, KK Winner, JC Bare, EC Neto, SA Khan, ... The Lancet Oncology 18 (1), 132-142, 2017 | 150 | 2017 |
| 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 | 144 | 2018 |
