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Phenotype Inference from Text and Genomic Data


Brbić, Maria; Piškorec, Matija; Vidulin, Vedrana; Kriško, Anita; Šmuc, Tomislav; Supek, Fran
Phenotype Inference from Text and Genomic Data // ECML-PKDD 2017, Lecture Notes in Computer Science / Altun, Yasemin ; Das, Kamalika ; et al. (ur.).
Skopje: Springer International Publishing, 2017. str. 373-377 doi:10.1007/978-3-319-71273-4_34 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Phenotype Inference from Text and Genomic Data

Autori
Brbić, Maria ; Piškorec, Matija ; Vidulin, Vedrana ; Kriško, Anita ; Šmuc, Tomislav ; Supek, Fran

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
ECML-PKDD 2017, Lecture Notes in Computer Science / Altun, Yasemin ; Das, Kamalika ; et al. - : Springer International Publishing, 2017, 373-377

ISBN
978-3-319-71273-4

Skup
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD)

Mjesto i datum
Skopje, 18-22.09.2017

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Phenotypic trait ; microbes ; comparative genomics ; late fusion ; text mining ; non-negative matrix factorization

Sažetak
We describe ProTraits, a machine learning pipeline that systematically annotates microbes with phenotypes using a large amount of textual data from scientific literature and other online resources, as well as genome sequencing data. Moreover, by relying on a multi-view nonnegative matrix factorization approach, ProTraits pipeline is also able to discover novel phenotypic concepts from unstructured text. We present the main components of the developed pipeline and outline challenges for the application to other fields.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Ustanove
Institut "Ruđer Bošković", Zagreb

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