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Tero Aittokallio
Tero Aittokallio
University of Helsinki, University of Turku, University of Oslo
Verified email at utu.fi - Homepage
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Cited by
Cited by
Year
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
7862014
SynergyFinder 2.0: visual analytics of multi-drug combination synergies
A Ianevski, AK Giri, T Aittokallio
Nucleic acids research 48 (W1), W488-W493, 2020
6872020
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
6652015
Graph-based methods for analysing networks in cell biology
T Aittokallio, B Schwikowski
Briefings in bioinformatics 7 (3), 243-255, 2006
5872006
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
5272017
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
4732015
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
4472014
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
3962015
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
3342014
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
2882022
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
2642022
Dealing with missing values in large-scale studies: microarray data imputation and beyond
T Aittokallio
Briefings in bioinformatics 11 (2), 253-264, 2010
2142010
What is synergy? The Saariselkä agreement revisited
J Tang, K Wennerberg, T Aittokallio
Frontiers in pharmacology 6, 181, 2015
2072015
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
2012018
Machine learning and feature selection for drug response prediction in precision oncology applications
M Ali, T Aittokallio
Biophysical reviews 11 (1), 31-39, 2019
1902019
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
1892020
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
1762010
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
1712018
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
1702014
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