Tero
Aittokallio
InFLAMES Lippulaiva
dosentti, matemaattis-luonnontieteellinen tiedekunta
PhD
Linkit
Julkaisut
TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples (2015)
Bioinformatics
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1))
High-throughput drug sensitivity and resistance testing of ovarian cancer cell lines provides useful strategy for assessing drug repositioning and therapeutic possibilities of emerging drugs (2015)
Clinical Cancer Research
(Muu (O2))
Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose-response data (2015)
Bioinformatics
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1))
Prediction of human population responses to toxic compounds by a collaborative competition (vol 33, pg 933, 2015) (2015)
Nature Biotechnology
(Muu (O2))
Systematic Mapping of Kinase Addiction Combinations in Breast Cancer Cells by Integrating Drug Sensitivity and Selectivity Profiles (2015)
Chemistry and Biology
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1))
MicroRNA-135b regulates ERα, AR and HIF1AN and affects breast and prostate cancer cell growth (2015)
Molecular Oncology
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1))
From drug response profiling to target addiction scoring in cancer cell models (2015)
Disease Models and Mechanisms
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1))
Protein phosphatase 2A activity is a major determinant of therapy response in cancer cells (2015)
Cancer Research
(Muu (O2))
Stroma- Derived Factors Significantly Impact the Drug Response Profiles of Patient- Derived Primary AML Cells: Implications for Drug Sensitivity Testing (2014)
Blood
(Muu (O2))
Predicting binding affinities between drug compounds and kinase targets (2014)
International workshop on machine learning in systems biology
(Muu (O2))