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Tero Aittokallio

Tero Aittokallio

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Engineering and Technology
Finland
2026

D-Index & Metrics

Engineering and Technology

D-Index
62
Citations
16254
World Ranking
1874
National Ranking
8

Biology and Biochemistry

D-Index
75
Citations
20160
World Ranking
5312
National Ranking
42

Research.com Recognitions

  • 2026 - Research.com Engineering and Technology in Finland Leader Award
  • 2025 - Research.com Engineering and Technology in Finland Leader Award

Overview

Tero Aittokallio is affiliated with the University of Helsinki in Finland and has conducted extensive research primarily in the fields of Biochemistry, Genetics and Molecular Biology, as well as Medicine. Their work encompasses multiple subfields, notably Molecular Biology, Computational Theory and Mathematics, Genetics, Immunology, and Cancer Research.

The scientist's research focus covers key topics such as Computational Drug Discovery Methods, Bioinformatics and Genomic Networks, Cell Image Analysis Techniques, Acute Myeloid Leukemia Research, Chronic Lymphocytic Leukemia Research, Cancer Genomics and Diagnostics, and Gene expression and cancer classification.

Among recent scholarly publications are:

  • SynergyFinder 2.0: visual analytics of multi-drug combination synergies, 2020, Nucleic Acids Research
  • Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data, 2022, Nature Communications
  • SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples, 2022, Nucleic Acids Research
  • Artificial intelligence, machine learning, and drug repurposing in cancer, 2021, Expert Opinion on Drug Discovery
  • Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia, 2021, Cancer Discovery

Frequent co-authors in research projects include:

  • Aleksandr Ianevski
  • Krister Wennerberg
  • Satu Mustjoki
  • Kimmo Porkka
  • Heikki Kuusanmäki

The primary venues for publication reflect a focus on biomedical and cancer research disciplines:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Cancer Research
  • Zenodo (CERN European Organization for Nuclear Research)
  • Bioinformatics
  • Leukemia

Best Publications

  • SynergyFinder 2.0: visual analytics of multi-drug combination synergies.

    Aleksandr Ianevski;Aleksandr Ianevski;Anil K. Giri;Tero Antero Aittokallio

  • Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data

    Unknown

  • Searching for Drug Synergy in Complex Dose–Response Landscapes Using an Interaction Potency Model

    Bhagwan Yadav;Krister Wennerberg;Tero Aittokallio;Jing Tang

  • SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples

    Unknown

  • Graph-based methods for analysing networks in cell biology

    Tero Aittokallio;Benno Schwikowski

  • Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis.

    Jing Tang;Agnieszka Szwajda;Sushil Shakyawar;Tao Xu

  • Toward more realistic drug–target interaction predictions

    Tapio Pahikkala;Antti Airola;Sami Pietilä;Sushil Shakyawar

  • SynergyFinder: a web application for analyzing drug combination dose-response matrix data.

    Aleksandr Ianevski;Liye He;Tero Aittokallio;Tero Aittokallio;Jing Tang;Jing Tang

  • Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products

    Milla Kibble;Niina Saarinen;Jing Tang;Krister Wennerberg

  • Individualized Systems Medicine Strategy to Tailor Treatments for Patients with Chemorefractory Acute Myeloid Leukemia

    Tea Pemovska;Mika Kontro;Bhagwan Yadav;Henrik Edgren

  • Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies

    Bhagwan Yadav;Tea Pemovska;Agnieszka Szwajda;Evgeny Kulesskiy

  • Methods for High-throughput Drug Combination Screening and Synergy Scoring.

    Liye He;Evgeny Kulesskiy;Jani Saarela;Laura Turunen

  • Machine learning and feature selection for drug response prediction in precision oncology applications

    Mehreen Ali;Tero Aittokallio;Tero Aittokallio;Tero Aittokallio

  • What is synergy? The Saariselkä agreement revisited.

    Jing Tang;Krister Wennerberg;Tero Aittokallio

  • Integrated drug profiling and CRISPR screening identify essential pathways for CAR T-cell cytotoxicity.

    Olli Dufva;Jan Koski;Pilvi Maliniemi;Aleksandr Ianevski;Aleksandr Ianevski

  • Dealing with missing values in large-scale studies: microarray data imputation and beyond

    Tero Aittokallio

  • Susceptibility of low-density lipoprotein particles to aggregate depends on particle lipidome, is modifiable, and associates with future cardiovascular deaths

    Maija Ruuth;Su Duy Nguyen;Terhi Marjut Vihervaara;Mika Hilvo

  • Regularized Machine Learning in the Genetic Prediction of Complex Traits

    Sebastian Okser;Tapio Pahikkala;Antti Airola;Tapio Salakoski

  • Artificial intelligence, machine learning, and drug repurposing in cancer.

    Ziaurrehman Tanoli;Markus Vähä-Koskela;Tero Aittokallio;Tero Aittokallio;Tero Aittokallio

  • Implementing a functional precision medicine tumor board for acute myeloid leukemia.

    Disha Malani;Ashwini Kumar;Oscar Bruck;Mika Kontro

  • Genome-wide profiling of interleukin-4 and STAT6 transcription factor regulation of human Th2 cell programming.

    Laura L. Elo;Henna Järvenpää;Soile Tuomela;Sunil Raghav

  • Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data

    Justin Guinney;Tao Wang;Teemu D Laajala;Teemu D Laajala;Kimberly Kanigel Winner

  • Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

    Solveig K. Sieberts;Fan Zhu;Javier García-García;Eli Stahl

  • Corrigendum to "Searching for drug synergy in complex dose-response landscapes using an interaction potency model" [Comput. Struct. Biotechnol. J. 13 (2015) 504-513].

    Bhagwan Yadav;Krister Wennerberg;Tero Aittokallio;Jing Tang

Frequent Co-Authors

Olli Kallioniemi
Olli Kallioniemi Karolinska Institute
Kimmo Porkka
Kimmo Porkka University of Helsinki
Riitta Lahesmaa
Riitta Lahesmaa Åbo Akademi University
Matti Poutanen
Matti Poutanen University of Turku
Satu Mustjoki
Satu Mustjoki University of Helsinki
Tapio Pahikkala
Tapio Pahikkala University of Turku
Jukka Westermarck
Jukka Westermarck Åbo Akademi University
Mats Gyllenberg
Mats Gyllenberg University of Helsinki
Samuel Kaski
Samuel Kaski Aalto University
Sampsa Matikainen
Sampsa Matikainen University of Helsinki

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