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STAR Protocols
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April 12, 2024
Protocol to explain support vector machine predictions via exact Shapley value computation
Andrea Mastropietro, Jürgen Bajorath
STAR Protocols
|
January 3, 2023
Protocol to explain graph neural network predictions using an edge-centric Shapley value-based approach
Andrea Mastropietro, Giuseppe Pasculli, Jürgen Bajorath
Scientific Reports
|
November 10, 2023
Calculation of exact Shapley values for explaining support vector machine models using the radial basis function kernel
Andrea Mastropietro, Christian Feldmann, Jürgen Bajorath
Bioinformatics (Oxford, England)
|
August 2, 2023
XGDAG: explainable gene-disease associations via graph neural networks
Andrea Mastropietro, Gianluca De Carlo, Aris Anagnostopoulos
Scientific Reports
|
June 29, 2026
Explainable artificial intelligence reveals divergent learning in pharmacophore-based hierarchical pooling graph neural networks
Maria Julia Teja Urrutia, Andrea Mastropietro, Jürgen Bajorath
NAR Genomics and Bioinformatics
|
February 2, 2026
A novel explainable deep-learning approach for network analysis of epistatic interactions
Andrea Mastropietro, Georgios Markopoulos, Evangelos Evangelou, et al.
Bioinformatics (Oxford, England)
|
February 2, 2023
NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification
Paola Stolfi, Andrea Mastropietro, Giuseppe Pasculli, et al.
Iscience
|
September 22, 2022
EdgeSHAPer: Bond-centric Shapley value-based explanation method for graph neural networks
Andrea Mastropietro, Giuseppe Pasculli, Christian Feldmann, et al.
Biomedicines
|
July 27, 2022
Network Proximity-Based Drug Repurposing Strategy for Early and Late Stages of Primary Biliary Cholangitis
Endrit Shahini, Giuseppe Pasculli, Andrea Mastropietro, et al.
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of 1
Search research articles
Search
Showing results (1-10 of 9) with videos related to
Sort By:
Page
of 1
STAR Protocols
|
April 12, 2024
Protocol to explain support vector machine predictions via exact Shapley value computation
Andrea Mastropietro, Jürgen Bajorath
STAR Protocols
|
January 3, 2023
Protocol to explain graph neural network predictions using an edge-centric Shapley value-based approach
Andrea Mastropietro, Giuseppe Pasculli, Jürgen Bajorath
Scientific Reports
|
November 10, 2023
Calculation of exact Shapley values for explaining support vector machine models using the radial basis function kernel
Andrea Mastropietro, Christian Feldmann, Jürgen Bajorath
Bioinformatics (Oxford, England)
|
August 2, 2023
XGDAG: explainable gene-disease associations via graph neural networks
Andrea Mastropietro, Gianluca De Carlo, Aris Anagnostopoulos
Scientific Reports
|
June 29, 2026
Explainable artificial intelligence reveals divergent learning in pharmacophore-based hierarchical pooling graph neural networks
Maria Julia Teja Urrutia, Andrea Mastropietro, Jürgen Bajorath
NAR Genomics and Bioinformatics
|
February 2, 2026
A novel explainable deep-learning approach for network analysis of epistatic interactions
Andrea Mastropietro, Georgios Markopoulos, Evangelos Evangelou, et al.
Bioinformatics (Oxford, England)
|
February 2, 2023
NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification
Paola Stolfi, Andrea Mastropietro, Giuseppe Pasculli, et al.
Iscience
|
September 22, 2022
EdgeSHAPer: Bond-centric Shapley value-based explanation method for graph neural networks
Andrea Mastropietro, Giuseppe Pasculli, Christian Feldmann, et al.
Biomedicines
|
July 27, 2022
Network Proximity-Based Drug Repurposing Strategy for Early and Late Stages of Primary Biliary Cholangitis
Endrit Shahini, Giuseppe Pasculli, Andrea Mastropietro, et al.
Page
of 1