(Q33762002)

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A regression framework incorporating quantitative and negative interaction data improves quantitative prediction of PDZ domain-peptide interaction from primary sequence.

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Europe PubMed Central

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https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:21127034%20AND%20SRC:MED&resulttype=core&format=json

17 January 2020

A regression framework incorporating quantitative and negative interaction data improves quantitative prediction of PDZ domain-peptide interaction from primary sequence (English)

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Europe PubMed Central

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https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:21127034%20AND%20SRC:MED&resulttype=core&format=json

17 January 2020

Chris Soon Heng Tan

2

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Europe PubMed Central

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17 January 2020

6

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17 January 2020

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Xiaojian Shao

1

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17 January 2020

Courtney Voss

3

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17 January 2020

Shawn S C Li

4

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17 January 2020

Naiyang Deng

5

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17 January 2020

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2 December 2010

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17 January 2020

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17 January 2020

27

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17 January 2020

3

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17 January 2020

383-390

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Europe PubMed Central

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17 January 2020

Novel peptide-mediated interactions derived from high-resolution 3-dimensional structures

1 reference

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1 reference

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1 reference

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1 reference

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1 reference

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1 reference

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1 reference

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Efficient peptide-MHC-I binding prediction for alleles with few known binders.

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PDZ domain binding selectivity is optimized across the mouse proteome

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A novel structure-based encoding for machine-learning applied to the inference of SH3 domain specificity

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22 June 2018

Uncovering quantitative protein interaction networks for mouse PDZ domains using protein microarrays

1 reference

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22 June 2018

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

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30 October 2018

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