Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials

Celia Diez Lopez; Athina Vidaki; Arwin Ralf; Diego Montiel Gonzalez; Djawad Radjabzadeh; Robert Kraaij; André Uitterlinden; C Haas; O Lao; Manfred Kayser

doi:10.1016/j.fsigen.2019.03.015

Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials

Celia Diez Lopez, Athina Vidaki, Arwin Ralf, Diego Montiel Gonzalez, Djawad Radjabzadeh, Robert Kraaij, André Uitterlinden, C Haas, O Lao, Manfred Kayser

Research output: Contribution to journal › Article › Academic

34 Citations (Scopus)

Original language	Undefined/Unknown
Pages (from-to)	72-82
Number of pages	11
Journal	Forensic Science International: Genetics
Volume	41
DOIs	https://doi.org/10.1016/j.fsigen.2019.03.015
Publication status	Published - 2019

Research programs

EMC MGC-02-26-01
EMC MM-01-39-09-A
EMC NIHES-01-64-02

Access to Document

10.1016/j.fsigen.2019.03.015

http://hdl.handle.net/1765/117795

Cite this

Diez Lopez, C., Vidaki, A., Ralf, A., Montiel Gonzalez, D., Radjabzadeh, D., Kraaij, R., Uitterlinden, A., Haas, C., Lao, O., & Kayser, M. (2019). Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials. Forensic Science International: Genetics, 41, 72-82. https://doi.org/10.1016/j.fsigen.2019.03.015

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title = "Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials",

author = "{Diez Lopez}, Celia and Athina Vidaki and Arwin Ralf and {Montiel Gonzalez}, Diego and Djawad Radjabzadeh and Robert Kraaij and Andr{\'e} Uitterlinden and C Haas and O Lao and Manfred Kayser",

year = "2019",

doi = "10.1016/j.fsigen.2019.03.015",

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journal = "Forensic Science International: Genetics",

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Diez Lopez, C, Vidaki, A , Ralf, A, Montiel Gonzalez, D, Radjabzadeh, D , Kraaij, R , Uitterlinden, A, Haas, C, Lao, O & Kayser, M 2019, 'Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials', Forensic Science International: Genetics, vol. 41, pp. 72-82. https://doi.org/10.1016/j.fsigen.2019.03.015

TY - JOUR

T1 - Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials

AU - Diez Lopez, Celia

AU - Vidaki, Athina

AU - Ralf, Arwin

AU - Montiel Gonzalez, Diego

AU - Radjabzadeh, Djawad

AU - Kraaij, Robert

AU - Uitterlinden, André

AU - Haas, C

AU - Lao, O

AU - Kayser, Manfred

PY - 2019

Y1 - 2019

U2 - 10.1016/j.fsigen.2019.03.015

DO - 10.1016/j.fsigen.2019.03.015

M3 - Article

C2 - 31003081

SN - 1872-4973

VL - 41

SP - 72

EP - 82

JO - Forensic Science International: Genetics

JF - Forensic Science International: Genetics

ER -