Format
Sort by
Items per page

Send to

Choose Destination

Search results

Items: 1 to 50 of 177

1.

Novel Screening Method Identifies PI3Kα, mTOR, and IGF1R as Key Kinases Regulating Cardiomyocyte Survival.

Elmadani M, Khan S, Tenhunen O, Magga J, Aittokallio T, Wennerberg K, Kerkelä R.

J Am Heart Assoc. 2019 Nov 5;8(21):e013018. doi: 10.1161/JAHA.119.013018. Epub 2019 Oct 16.

2.

Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies.

Ravikumar B, Timonen S, Alam Z, Parri E, Wennerberg K, Aittokallio T.

Cell Chem Biol. 2019 Sep 10. pii: S2451-9456(19)30272-7. doi: 10.1016/j.chembiol.2019.08.007. [Epub ahead of print]

PMID:
31521622
3.

Genome-wide off-targets of drugs: risks and opportunities.

Giri AK, Ianevski A, Aittokallio T.

Cell Biol Toxicol. 2019 Aug 20. doi: 10.1007/s10565-019-09491-7. [Epub ahead of print] No abstract available.

PMID:
31432301
4.

Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer.

Tang J, Gautam P, Gupta A, He L, Timonen S, Akimov Y, Wang W, Szwajda A, Jaiswal A, Turei D, Yadav B, Kankainen M, Saarela J, Saez-Rodriguez J, Wennerberg K, Aittokallio T.

NPJ Syst Biol Appl. 2019 Jul 8;5:20. doi: 10.1038/s41540-019-0098-z. eCollection 2019.

5.

DNMT Inhibitors Increase Methylation in the Cancer Genome.

Giri AK, Aittokallio T.

Front Pharmacol. 2019 Apr 24;10:385. doi: 10.3389/fphar.2019.00385. eCollection 2019.

6.

Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets.

Gautam P, Jaiswal A, Aittokallio T, Al-Ali H, Wennerberg K.

Cell Chem Biol. 2019 Jul 18;26(7):970-979.e4. doi: 10.1016/j.chembiol.2019.03.011. Epub 2019 May 2.

PMID:
31056464
7.

Publisher Correction: Pharmacological reactivation of MYC-dependent apoptosis induces susceptibility to anti-PD-1 immunotherapy.

Haikala HM, Anttila JM, Marques E, Raatikainen T, Ilander M, Hakanen H, Ala-Hongisto H, Savelius M, Balboa D, Von Eyss B, Eskelinen V, Munne P, Nieminen AI, Otonkoski T, Schüler J, Laajala TD, Aittokallio T, Sihto H, Mattson J, Heikkilä P, Leidenius M, Joensuu H, Mustjoki S, Kovanen P, Eilers M, Leverson JD, Klefström J.

Nat Commun. 2019 Feb 20;10(1):932. doi: 10.1038/s41467-019-08956-x.

8.

Pharmacological reactivation of MYC-dependent apoptosis induces susceptibility to anti-PD-1 immunotherapy.

Haikala HM, Anttila JM, Marques E, Raatikainen T, Ilander M, Hakanen H, Ala-Hongisto H, Savelius M, Balboa D, Von Eyss B, Eskelinen V, Munne P, Nieminen AI, Otonkoski T, Schüler J, Laajala TD, Aittokallio T, Sihto H, Mattson J, Heikkilä P, Leidenius M, Joensuu H, Mustjoki S, Kovanen P, Eilers M, Leverson JD, Klefström J.

Nat Commun. 2019 Feb 6;10(1):620. doi: 10.1038/s41467-019-08541-2. Erratum in: Nat Commun. 2019 Feb 20;10(1):932.

9.

Interactive visual analysis of drug-target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing.

Tanoli Z, Alam Z, Ianevski A, Wennerberg K, Vähä-Koskela M, Aittokallio T.

Brief Bioinform. 2018 Dec 18. doi: 10.1093/bib/bby119. [Epub ahead of print]

PMID:
30566623
10.

Integrated Analysis of Drug Sensitivity and Selectivity to Predict Synergistic Drug Combinations and Target Coaddictions in Cancer.

Jaiswal A, Yadav B, Wennerberg K, Aittokallio T.

Methods Mol Biol. 2019;1888:205-217. doi: 10.1007/978-1-4939-8891-4_12.

PMID:
30519949
11.

Adrenals Contribute to Growth of Castration-Resistant VCaP Prostate Cancer Xenografts.

Huhtaniemi R, Oksala R, Knuuttila M, Mehmood A, Aho E, Laajala TD, Nicorici D, Aittokallio T, Laiho A, Elo L, Ohlsson C, Kallio P, Mäkelä S, Mustonen MVJ, Sipilä P, Poutanen M.

Am J Pathol. 2018 Dec;188(12):2890-2901. doi: 10.1016/j.ajpath.2018.07.029. Epub 2018 Sep 28.

PMID:
30273606
12.

Drug Target Commons 2.0: a community platform for systematic analysis of drug-target interaction profiles.

Tanoli Z, Alam Z, Vähä-Koskela M, Ravikumar B, Malyutina A, Jaiswal A, Tang J, Wennerberg K, Aittokallio T.

Database (Oxford). 2018 Jan 1;2018:1-13. doi: 10.1093/database/bay083.

13.

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

Ali M, Aittokallio T.

Biophys Rev. 2019 Feb;11(1):31-39. doi: 10.1007/s12551-018-0446-z. Epub 2018 Aug 10. Review.

14.

PP2A inhibition is a druggable MEK inhibitor resistance mechanism in KRAS-mutant lung cancer cells.

Kauko O, O'Connor CM, Kulesskiy E, Sangodkar J, Aakula A, Izadmehr S, Yetukuri L, Yadav B, Padzik A, Laajala TD, Haapaniemi P, Momeny M, Varila T, Ohlmeyer M, Aittokallio T, Wennerberg K, Narla G, Westermarck J.

Sci Transl Med. 2018 Jul 18;10(450). pii: eaaq1093. doi: 10.1126/scitranslmed.aaq1093.

PMID:
30021885
15.

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

Yadav B, Wennerberg K, Aittokallio T, Tang J.

Comput Struct Biotechnol J. 2017 Jul 25;15:387. doi: 10.1016/j.csbj.2017.07.003. eCollection 2017.

16.

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

Ruuth M, Nguyen SD, Vihervaara T, Hilvo M, Laajala TD, Kondadi PK, Gisterå A, Lähteenmäki H, Kittilä T, Huusko J, Uusitupa M, Schwab U, Savolainen MJ, Sinisalo J, Lokki ML, Nieminen MS, Jula A, Perola M, Ylä-Herttula S, Rudel L, Öörni A, Baumann M, Baruch A, Laaksonen R, Ketelhuth DFJ, Aittokallio T, Jauhiainen M, Käkelä R, Borén J, Williams KJ, Kovanen PT, Öörni K.

Eur Heart J. 2018 Jul 14;39(27):2562-2573. doi: 10.1093/eurheartj/ehy319.

17.

Drug-Sensitivity Screening and Genomic Characterization of 45 HPV-Negative Head and Neck Carcinoma Cell Lines for Novel Biomarkers of Drug Efficacy.

Lepikhova T, Karhemo PR, Louhimo R, Yadav B, Murumägi A, Kulesskiy E, Kivento M, Sihto H, Grénman R, Syrjänen SM, Kallioniemi O, Aittokallio T, Wennerberg K, Joensuu H, Monni O.

Mol Cancer Ther. 2018 Sep;17(9):2060-2071. doi: 10.1158/1535-7163.MCT-17-0733. Epub 2018 Jul 3.

18.

Learning with multiple pairwise kernels for drug bioactivity prediction.

Cichonska A, Pahikkala T, Szedmak S, Julkunen H, Airola A, Heinonen M, Aittokallio T, Rousu J.

Bioinformatics. 2018 Jul 1;34(13):i509-i518. doi: 10.1093/bioinformatics/bty277.

19.

Immune cell contexture in the bone marrow tumor microenvironment impacts therapy response in CML.

Brück O, Blom S, Dufva O, Turkki R, Chheda H, Ribeiro A, Kovanen P, Aittokallio T, Koskenvesa P, Kallioniemi O, Porkka K, Pellinen T, Mustjoki S.

Leukemia. 2018 Jul;32(7):1643-1656. doi: 10.1038/s41375-018-0175-0. Epub 2018 Jun 20.

PMID:
29925907
20.

ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts.

Laajala TD, Murtojärvi M, Virkki A, Aittokallio T.

Bioinformatics. 2018 Nov 15;34(22):3957-3959. doi: 10.1093/bioinformatics/bty477.

21.

Novel activities of safe-in-human broad-spectrum antiviral agents.

Ianevski A, Zusinaite E, Kuivanen S, Strand M, Lysvand H, Teppor M, Kakkola L, Paavilainen H, Laajala M, Kallio-Kokko H, Valkonen M, Kantele A, Telling K, Lutsar I, Letjuka P, Metelitsa N, Oksenych V, Bjørås M, Nordbø SA, Dumpis U, Vitkauskiene A, Öhrmalm C, Bondeson K, Bergqvist A, Aittokallio T, Cox RJ, Evander M, Hukkanen V, Marjomaki V, Julkunen I, Vapalahti O, Tenson T, Merits A, Kainov D.

Antiviral Res. 2018 Jun;154:174-182. doi: 10.1016/j.antiviral.2018.04.016. Epub 2018 Apr 23. Review.

PMID:
29698664
22.

Aggressive natural killer-cell leukemia mutational landscape and drug profiling highlight JAK-STAT signaling as therapeutic target.

Dufva O, Kankainen M, Kelkka T, Sekiguchi N, Awad SA, Eldfors S, Yadav B, Kuusanmäki H, Malani D, Andersson EI, Pietarinen P, Saikko L, Kovanen PE, Ojala T, Lee DA, Loughran TP Jr, Nakazawa H, Suzumiya J, Suzuki R, Ko YH, Kim WS, Chuang SS, Aittokallio T, Chan WC, Ohshima K, Ishida F, Mustjoki S.

Nat Commun. 2018 Apr 19;9(1):1567. doi: 10.1038/s41467-018-03987-2.

23.

Combined ASRGL1 and p53 immunohistochemistry as an independent predictor of survival in endometrioid endometrial carcinoma.

Huvila J, Laajala TD, Edqvist PH, Mardinoglu A, Talve L, Pontén F, Grénman S, Carpén O, Aittokallio T, Auranen A.

Gynecol Oncol. 2018 Apr;149(1):173-180. doi: 10.1016/j.ygyno.2018.02.016. Epub 2018 Mar 2.

PMID:
29486992
24.

Patient-Customized Drug Combination Prediction and Testing for T-cell Prolymphocytic Leukemia Patients.

He L, Tang J, Andersson EI, Timonen S, Koschmieder S, Wennerberg K, Mustjoki S, Aittokallio T.

Cancer Res. 2018 May 1;78(9):2407-2418. doi: 10.1158/0008-5472.CAN-17-3644. Epub 2018 Feb 26.

25.

Secreted frizzled-related protein 2 (SFRP2) expression promotes lesion proliferation via canonical WNT signaling and indicates lesion borders in extraovarian endometriosis.

Heinosalo T, Gabriel M, Kallio L, Adhikari P, Huhtinen K, Laajala TD, Kaikkonen E, Mehmood A, Suvitie P, Kujari H, Aittokallio T, Perheentupa A, Poutanen M.

Hum Reprod. 2018 May 1;33(5):817-831. doi: 10.1093/humrep/dey026.

PMID:
29462326
26.

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

He L, Kulesskiy E, Saarela J, Turunen L, Wennerberg K, Aittokallio T, Tang J.

Methods Mol Biol. 2018;1711:351-398. doi: 10.1007/978-1-4939-7493-1_17.

27.

Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions.

Tang J, Tanoli ZU, Ravikumar B, Alam Z, Rebane A, Vähä-Koskela M, Peddinti G, van Adrichem AJ, Wakkinen J, Jaiswal A, Karjalainen E, Gautam P, He L, Parri E, Khan S, Gupta A, Ali M, Yetukuri L, Gustavsson AL, Seashore-Ludlow B, Hersey A, Leach AR, Overington JP, Repasky G, Wennerberg K, Aittokallio T.

Cell Chem Biol. 2018 Feb 15;25(2):224-229.e2. doi: 10.1016/j.chembiol.2017.11.009. Epub 2017 Dec 21.

28.

Improving the efficacy-safety balance of polypharmacology in multi-target drug discovery.

Ravikumar B, Aittokallio T.

Expert Opin Drug Discov. 2018 Feb;13(2):179-192. doi: 10.1080/17460441.2018.1413089. Epub 2017 Dec 12. Review.

PMID:
29233023
29.

Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.

Ali M, Khan SA, Wennerberg K, Aittokallio T.

Bioinformatics. 2018 Apr 15;34(8):1353-1362. doi: 10.1093/bioinformatics/btx766.

30.

Antiandrogens Reduce Intratumoral Androgen Concentrations and Induce Androgen Receptor Expression in Castration-Resistant Prostate Cancer Xenografts.

Knuuttila M, Mehmood A, Huhtaniemi R, Yatkin E, Häkkinen MR, Oksala R, Laajala TD, Ryberg H, Handelsman DJ, Aittokallio T, Auriola S, Ohlsson C, Laiho A, Elo LL, Sipilä P, Mäkelä SI, Poutanen M.

Am J Pathol. 2018 Jan;188(1):216-228. doi: 10.1016/j.ajpath.2017.08.036. Epub 2017 Nov 7.

PMID:
29126837
31.

A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines.

Gönen M, Weir BA, Cowley GS, Vazquez F, Guan Y, Jaiswal A, Karasuyama M, Uzunangelov V, Wang T, Tsherniak A, Howell S, Marbach D, Hoff B, Norman TC, Airola A, Bivol A, Bunte K, Carlin D, Chopra S, Deran A, Ellrott K, Gopalacharyulu P, Graim K, Kaski S, Khan SA, Newton Y, Ng S, Pahikkala T, Paull E, Sokolov A, Tang H, Tang J, Wennerberg K, Xie Y, Zhan X, Zhu F; Broad-DREAM Community, Aittokallio T, Mamitsuka H, Stuart JM, Boehm JS, Root DE, Xiao G, Stolovitzky G, Hahn WC, Margolin AA.

Cell Syst. 2017 Nov 22;5(5):485-497.e3. doi: 10.1016/j.cels.2017.09.004. Epub 2017 Oct 4.

32.

The inconvenience of data of convenience: computational research beyond post-mortem analyses.

Azencott CA, Aittokallio T, Roy S; DREAM Idea Challenge Consortium, Norman T, Friend S, Stolovitzky G, Goldenberg A.

Nat Methods. 2017 Sep 29;14(10):937-938. doi: 10.1038/nmeth.4457. No abstract available.

PMID:
28960198
33.

Antiviral Properties of Chemical Inhibitors of Cellular Anti-Apoptotic Bcl-2 Proteins.

Bulanova D, Ianevski A, Bugai A, Akimov Y, Kuivanen S, Paavilainen H, Kakkola L, Nandania J, Turunen L, Ohman T, Ala-Hongisto H, Pesonen HM, Kuisma MS, Honkimaa A, Walton EL, Oksenych V, Lorey MB, Guschin D, Shim J, Kim J, Than TT, Chang SY, Hukkanen V, Kulesskiy E, Marjomaki VS, Julkunen I, Nyman TA, Matikainen S, Saarela JS, Sane F, Hober D, Gabriel G, De Brabander JK, Martikainen M, Windisch MP, Min JY, Bruzzone R, Aittokallio T, Vähä-Koskela M, Vapalahti O, Pulk A, Velagapudi V, Kainov DE.

Viruses. 2017 Sep 25;9(10). pii: E271. doi: 10.3390/v9100271.

34.

MediSyn: uncertainty-aware visualization of multiple biomedical datasets to support drug treatment selection.

He C, Micallef L, Tanoli ZU, Kaski S, Aittokallio T, Jacucci G.

BMC Bioinformatics. 2017 Sep 13;18(Suppl 10):393. doi: 10.1186/s12859-017-1785-7.

35.

In Search of System-Wide Productivity Gains - The Role of Global Collaborations in Preclinical Translation.

Ussi AE, de Kort M, Coussens NP, Aittokallio T, Hajduch M.

Clin Transl Sci. 2017 Nov;10(6):423-425. doi: 10.1111/cts.12498. Epub 2017 Sep 19. No abstract available.

36.

Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression.

Ammad-Ud-Din M, Khan SA, Wennerberg K, Aittokallio T.

Bioinformatics. 2017 Jul 15;33(14):i359-i368. doi: 10.1093/bioinformatics/btx266.

37.

Discovery of novel drug sensitivities in T-PLL by high-throughput ex vivo drug testing and mutation profiling.

Andersson EI, Pützer S, Yadav B, Dufva O, Khan S, He L, Sellner L, Schrader A, Crispatzu G, Oleś M, Zhang H, Adnan-Awad S, Lagström S, Bellanger D, Mpindi JP, Eldfors S, Pemovska T, Pietarinen P, Lauhio A, Tomska K, Cuesta-Mateos C, Faber E, Koschmieder S, Brümmendorf TH, Kytölä S, Savolainen ER, Siitonen T, Ellonen P, Kallioniemi O, Wennerberg K, Ding W, Stern MH, Huber W, Anders S, Tang J, Aittokallio T, Zenz T, Herling M, Mustjoki S.

Leukemia. 2018 Mar;32(3):774-787. doi: 10.1038/leu.2017.252. Epub 2017 Aug 14.

PMID:
28804127
38.

Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors.

Cichonska A, Ravikumar B, Parri E, Timonen S, Pahikkala T, Airola A, Wennerberg K, Rousu J, Aittokallio T.

PLoS Comput Biol. 2017 Aug 7;13(8):e1005678. doi: 10.1371/journal.pcbi.1005678. eCollection 2017 Aug.

39.

JAK1/2 and BCL2 inhibitors synergize to counteract bone marrow stromal cell-induced protection of AML.

Karjalainen R, Pemovska T, Popa M, Liu M, Javarappa KK, Majumder MM, Yadav B, Tamborero D, Tang J, Bychkov D, Kontro M, Parsons A, Suvela M, Mayoral Safont M, Porkka K, Aittokallio T, Kallioniemi O, McCormack E, Gjertsen BT, Wennerberg K, Knowles J, Heckman CA.

Blood. 2017 Aug 10;130(6):789-802. doi: 10.1182/blood-2016-02-699363. Epub 2017 Jun 15.

PMID:
28619982
40.

Early metabolic markers identify potential targets for the prevention of type 2 diabetes.

Peddinti G, Cobb J, Yengo L, Froguel P, Kravić J, Balkau B, Tuomi T, Aittokallio T, Groop L.

Diabetologia. 2017 Sep;60(9):1740-1750. doi: 10.1007/s00125-017-4325-0. Epub 2017 Jun 8.

41.

Seed-effect modeling improves the consistency of genome-wide loss-of-function screens and identifies synthetic lethal vulnerabilities in cancer cells.

Jaiswal A, Peddinti G, Akimov Y, Wennerberg K, Kuznetsov S, Tang J, Aittokallio T.

Genome Med. 2017 Jun 1;9(1):51. doi: 10.1186/s13073-017-0440-2.

42.

Rapalogs can promote cancer cell stemness in vitro in a Galectin-1 and H-ras-dependent manner.

Posada IMD, Lectez B, Sharma M, Oetken-Lindholm C, Yetukuri L, Zhou Y, Aittokallio T, Abankwa D.

Oncotarget. 2017 Jul 4;8(27):44550-44566. doi: 10.18632/oncotarget.17819.

43.
44.

Matched preclinical designs for improved translatability.

Aittokallio T, Scherer A, Poutanen M, Freedman LP.

Sci Transl Med. 2017 May 10;9(389). pii: eaal4101. doi: 10.1126/scitranslmed.aal4101.

PMID:
28490671
45.

C-SPADE: a web-tool for interactive analysis and visualization of drug screening experiments through compound-specific bioactivity dendrograms.

Ravikumar B, Alam Z, Peddinti G, Aittokallio T.

Nucleic Acids Res. 2017 Jul 3;45(W1):W495-W500. doi: 10.1093/nar/gkx384.

46.

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

Ianevski A, He L, Aittokallio T, Tang J.

Bioinformatics. 2017 Aug 1;33(15):2413-2415. doi: 10.1093/bioinformatics/btx162.

47.

Whole-genome view of the consequences of a population bottleneck using 2926 genome sequences from Finland and United Kingdom.

Chheda H, Palta P, Pirinen M, McCarthy S, Walter K, Koskinen S, Salomaa V, Daly M, Durbin R, Palotie A, Aittokallio T, Ripatti S.

Eur J Hum Genet. 2017 Apr;25(4):477-484. doi: 10.1038/ejhg.2016.205. Epub 2017 Feb 1.

48.

Systematic drug sensitivity testing reveals synergistic growth inhibition by dasatinib or mTOR inhibitors with paclitaxel in ovarian granulosa cell tumor cells.

Haltia UM, Andersson N, Yadav B, Färkkilä A, Kulesskiy E, Kankainen M, Tang J, Bützow R, Riska A, Leminen A, Heikinheimo M, Kallioniemi O, Unkila-Kallio L, Wennerberg K, Aittokallio T, Anttonen M.

Gynecol Oncol. 2017 Mar;144(3):621-630. doi: 10.1016/j.ygyno.2016.12.016. Epub 2017 Jan 16.

PMID:
28104295
49.

Consistency in drug response profiling.

Mpindi JP, Yadav B, Östling P, Gautam P, Malani D, Murumägi A, Hirasawa A, Kangaspeska S, Wennerberg K, Kallioniemi O, Aittokallio T.

Nature. 2016 Nov 30;540(7631):E5-E6. doi: 10.1038/nature20171. No abstract available.

PMID:
27905421
50.

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.

Guinney J, Wang T, Laajala TD, Winner KK, Bare JC, Neto EC, Khan SA, Peddinti G, Airola A, Pahikkala T, Mirtti T, Yu T, Bot BM, Shen L, Abdallah K, Norman T, Friend S, Stolovitzky G, Soule H, Sweeney CJ, Ryan CJ, Scher HI, Sartor O, Xie Y, Aittokallio T, Zhou FL, Costello JC; Prostate Cancer Challenge DREAM Community.

Lancet Oncol. 2017 Jan;18(1):132-142. doi: 10.1016/S1470-2045(16)30560-5. Epub 2016 Nov 16.

Supplemental Content

Loading ...
Support Center