A
Attila Kertész-Farkas
Researcher at National Research University – Higher School of Economics
Publications - 41
Citations - 1221
Attila Kertész-Farkas is an academic researcher from National Research University – Higher School of Economics. The author has contributed to research in topics: Activity recognition & Support vector machine. The author has an hindex of 13, co-authored 41 publications receiving 935 citations. Previous affiliations of Attila Kertész-Farkas include Center for Devices and Radiological Health & University of Washington.
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Journal ArticleDOI
Nuclear architecture dictates HIV-1 integration site selection
Bruna Marini,Attila Kertész-Farkas,Hashim Ali,Bojana Lucic,Bojana Lucic,Kamil Lisek,Kamil Lisek,Lara Manganaro,Lara Manganaro,Sándor Pongor,Sándor Pongor,Roberto Luzzati,Alessandra Recchia,Fulvio Mavilio,Mauro Giacca,Marina Lusic,Marina Lusic +16 more
TL;DR: It is shown that HIV-1 integration occurs in the outer shell of the nucleus in close correspondence with the nuclear pore, indicating that nuclear topography is an essential determinant of the HIV- 1 life cycle.
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Crux: rapid open source protein tandem mass spectrometry analysis.
Sean J. McIlwain,Kaipo Tamura,Attila Kertész-Farkas,Charles E. Grant,Benjamin J. Diament,Barbara Frewen,J. Jeffry Howbert,Michael R. Hoopmann,Lukas Käll,Jimmy K. Eng,Michael J. MacCoss,William Stafford Noble +11 more
TL;DR: The Crux mass spectrometry analysis software toolkit is an open source project that aims to provide users with a cross-platform suite of analysis tools for interpreting protein mass Spectrometry data.
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Test-time augmentation for deep learning-based cell segmentation on microscopy images.
TL;DR: This paper describes how the test-time argumentation prediction method is incorporated into two major segmentation approaches utilized in the single-cell analysis of microscopy images, and shows that even if only simple test- time augmentations are applied, TTA can significantly improve prediction accuracy.
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Toward an automatic method for extracting cancer-and other disease-related point mutations from the biomedical literature
Emily Doughty,Attila Kertész-Farkas,Olivier Bodenreider,Gary Thompson,Asa Adadey,Thomas Peterson,Maricel G. Kann +6 more
TL;DR: This work introduces a high-throughput computational method for the identification of relevant disease mutations in PubMed abstracts applied to prostate (PCa) and breast cancer (BCa) mutations by significantly increasing the number of annotated mutations.
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Application of compression-based distance measures to protein sequence classification: a methodological study
TL;DR: Compression-based distance measures performed especially well on distantly related proteins where the performance of a combined measure, constructed from a CBM and a BLAST score, approached or even slightly exceeded that of the Smith-Waterman algorithm and two hidden Markov model-based algorithms.