Fast Bipartite Forests for Semi-supervised Interaction Prediction-Reference-Cited by-同舟云学术

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Fast Bipartite Forests for Semi-supervised Interaction Prediction

Published:2024-04-08 Issue: Volume:36 Page:979-986
ISSN:
Container-title:Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing
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Author:

Ilídio Pedro¹^ORCID,Alves André²^ORCID,Cerri Ricardo³^ORCID

Affiliation:

1. Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, São Paulo, Brazil

2. Departamento de Computação, Universidade Fedral de São Carlos, São Carlos, São Paulo, Brazil

3. Departamento de Computação, Universidade Federal de São Carlos, São Carlos, São Paulo, Brazil

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Fundação de Amparo à Pesquisa do Estado de São Paulo

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3605098.3636071

Reference43 articles.

1. Semi-supervised extensions of multi-task tree ensembles

2. Drug–target interaction prediction through domain-tuned network-based inference

3. Semi-Supervised Hybrid Predictive Bi-Clustering Trees for Drug-Target Interaction Prediction

4. Armen S Asratian, Tristan MJ Denley, and Roland Häggkvist. 1998. Bipartite graphs and their applications. Vol. 131. Cambridge university press.

5. Machine learning approaches and databases for prediction of drug–target interaction: a survey paper

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