A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development

Nolen Perualila-Tan; Adetayo Kasim; Willem Talloen; Bie Verbist; Hinrich W.H. Göhlmann; null QSTAR Consortium; Ziv Shkedy

doi:10.1515/sagmb-2014-0086

A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development

Nolen Perualila-Tan, Adetayo Kasim, Willem Talloen, Bie Verbist, Hinrich W.H. Göhlmann, QSTAR Consortium, Ziv Shkedy

Institute for Machine Learning

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	291–304
Number of pages	14
Journal	Statistical Applications in Genetics and Molecular Biology
Volume	15
DOIs	https://doi.org/10.1515/sagmb-2014-0086
Publication status	Published - 2016

Fields of science

303 Health Sciences
304 Medical Biotechnology
304003 Genetic engineering
305 Other Human Medicine, Health Sciences
101004 Biomathematics
101018 Statistics
102 Computer Sciences
102001 Artificial intelligence
102004 Bioinformatics
102010 Database systems
102015 Information systems
102019 Machine learning
106023 Molecular biology
106002 Biochemistry
106005 Bioinformatics
106007 Biostatistics
106041 Structural biology
301 Medical-Theoretical Sciences, Pharmacy
302 Clinical Medicine

JKU Focus areas

Computation in Informatics and Mathematics
Nano-, Bio- and Polymer-Systems: From Structure to Function
Medical Sciences (in general)
Health System Research
Clinical Research on Aging

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10.1515/sagmb-2014-0086

Cite this

Perualila-Tan, N., Kasim, A., Talloen, W., Verbist, B., Göhlmann, H. W. H., QSTAR Consortium, & Shkedy, Z. (2016). A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development. Statistical Applications in Genetics and Molecular Biology, 15, 291–304. https://doi.org/10.1515/sagmb-2014-0086

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title = "A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development",

author = "Nolen Perualila-Tan and Adetayo Kasim and Willem Talloen and Bie Verbist and G{\"o}hlmann, {Hinrich W.H.} and {QSTAR Consortium} and Ziv Shkedy",

year = "2016",

doi = "10.1515/sagmb-2014-0086",

language = "English",

volume = "15",

pages = "291–304",

journal = "Statistical Applications in Genetics and Molecular Biology",

issn = "1544-6115",

publisher = "The Berkeley Electronic Press",

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Perualila-Tan, N, Kasim, A, Talloen, W, Verbist, B, Göhlmann, HWH, QSTAR Consortium & Shkedy, Z 2016, 'A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development', Statistical Applications in Genetics and Molecular Biology, vol. 15, pp. 291–304. https://doi.org/10.1515/sagmb-2014-0086

A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development. / Perualila-Tan, Nolen; Kasim, Adetayo; Talloen, Willem et al.
In: Statistical Applications in Genetics and Molecular Biology, Vol. 15, 2016, p. 291–304.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development

AU - Perualila-Tan, Nolen

AU - Kasim, Adetayo

AU - Talloen, Willem

AU - Verbist, Bie

AU - Göhlmann, Hinrich W.H.

AU - QSTAR Consortium, null

AU - Shkedy, Ziv

PY - 2016

Y1 - 2016

UR - http://www.bioinf.jku.at/publications/

U2 - 10.1515/sagmb-2014-0086

DO - 10.1515/sagmb-2014-0086

M3 - Article

SN - 1544-6115

VL - 15

SP - 291

EP - 304

JO - Statistical Applications in Genetics and Molecular Biology

JF - Statistical Applications in Genetics and Molecular Biology

ER -

Perualila-Tan N, Kasim A, Talloen W, Verbist B, Göhlmann HWH, QSTAR Consortium et al. A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development. Statistical Applications in Genetics and Molecular Biology. 2016;15:291–304. doi: 10.1515/sagmb-2014-0086

A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development

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JKU Focus areas

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