Molnár C - Search Results

1

Human listeners are able to classify dog (Canis familiaris) barks recorded in different situations.

Pongrácz P, Molnár C, Miklósi A, Csányi V. Pongrácz P, et al. Among authors: molnar c. J Comp Psychol. 2005 May;119(2):136-44. doi: 10.1037/0735-7036.119.2.136. J Comp Psychol. 2005. PMID: 15982157

2

Seeing with ears: Sightless humans' perception of dog bark provides a test for structural rules in vocal communication.

Molnár C, Pongrácz P, Miklósi A. Molnár C, et al. Q J Exp Psychol (Hove). 2010 May;63(5):1004-13. doi: 10.1080/17470210903168243. Q J Exp Psychol (Hove). 2010. PMID: 19760535

3

Dogs discriminate between barks: the effect of context and identity of the caller.

Molnár C, Pongrácz P, Faragó T, Dóka A, Miklósi A. Molnár C, et al. Behav Processes. 2009 Oct;82(2):198-201. doi: 10.1016/j.beproc.2009.06.011. Epub 2009 Jul 9. Behav Processes. 2009. PMID: 19596426

4

Barking in family dogs: an ethological approach.

Pongrácz P, Molnár C, Miklósi A. Pongrácz P, et al. Among authors: molnar c. Vet J. 2010 Feb;183(2):141-7. doi: 10.1016/j.tvjl.2008.12.010. Epub 2009 Jan 31. Vet J. 2010. PMID: 19181546 Review.

5

Classification of dog barks: a machine learning approach.

Molnár C, Kaplan F, Roy P, Pachet F, Pongrácz P, Dóka A, Miklósi A. Molnár C, et al. Anim Cogn. 2008 Jul;11(3):389-400. doi: 10.1007/s10071-007-0129-9. Epub 2008 Jan 15. Anim Cogn. 2008. PMID: 18197442 Free article.

6

Can humans discriminate between dogs on the base of the acoustic parameters of barks?

Molnár C, Pongrácz P, Dóka A, Miklósi A. Molnár C, et al. Behav Processes. 2006 Jul;73(1):76-83. doi: 10.1016/j.beproc.2006.03.014. Epub 2006 Mar 29. Behav Processes. 2006. PMID: 16678361

7

Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

Piccinini F, Balassa T, Szkalisity A, Molnar C, Paavolainen L, Kujala K, Buzas K, Sarazova M, Pietiainen V, Kutay U, Smith K, Horvath P. Piccinini F, et al. Among authors: molnar c. Cell Syst. 2017 Jun 28;4(6):651-655.e5. doi: 10.1016/j.cels.2017.05.012. Epub 2017 Jun 21. Cell Syst. 2017. PMID: 28647475 Free article.

8

Environmental properties of cells improve machine learning-based phenotype recognition accuracy.

Toth T, Balassa T, Bara N, Kovacs F, Kriston A, Molnar C, Haracska L, Sukosd F, Horvath P. Toth T, et al. Among authors: molnar c. Sci Rep. 2018 Jul 4;8(1):10085. doi: 10.1038/s41598-018-28482-y. Sci Rep. 2018. PMID: 29973621 Free PMC article.

9

Intelligent image-based in situ single-cell isolation.

Brasko C, Smith K, Molnar C, Farago N, Hegedus L, Balind A, Balassa T, Szkalisity A, Sukosd F, Kocsis K, Balint B, Paavolainen L, Enyedi MZ, Nagy I, Puskas LG, Haracska L, Tamas G, Horvath P. Brasko C, et al. Among authors: molnar c. Nat Commun. 2018 Jan 15;9(1):226. doi: 10.1038/s41467-017-02628-4. Nat Commun. 2018. PMID: 29335532 Free PMC article.

10

Data-analysis strategies for image-based cell profiling.

Caicedo JC, Cooper S, Heigwer F, Warchal S, Qiu P, Molnar C, Vasilevich AS, Barry JD, Bansal HS, Kraus O, Wawer M, Paavolainen L, Herrmann MD, Rohban M, Hung J, Hennig H, Concannon J, Smith I, Clemons PA, Singh S, Rees P, Horvath P, Linington RG, Carpenter AE. Caicedo JC, et al. Among authors: molnar c. Nat Methods. 2017 Aug 31;14(9):849-863. doi: 10.1038/nmeth.4397. Nat Methods. 2017. PMID: 28858338 Free PMC article.

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