Extensive complementarity between gene function prediction methods

By Vedrana Vidulin, Tomislav Smuc, Fran Supek. 2016

12 citations recorded by the source corpus.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Marinka Zitnik
2. Jure Leskovec
3. Hiroshi Mamitsuka
4. Ronghui You
5. Fengzhu Sun
6. Shanfeng Zhu
7. Petar Velickovic
8. William L. Hamilton

In Computational Biology & Bioinformatics

1. Jure Leskovec
2. Marinka Zitnik
3. Hiroshi Mamitsuka
4. Shanfeng Zhu
5. Petar Velickovic
6. Yiding Yang
7. Xinchao Wang
8. Bernard Ghanem

Citations in the corpus

• Predicting multicellular function through multi-layer tissue networks (2017, background): “(1) First, current methods for cellular function prediction on networks (Mostafavi and Morris, 2009; Radivojac et al., 2013; Zitnik and Zupan, 2015; Vidulin et al., 2016) do not model networks with regards to patterns that span tissues, organs, and cellular systems.”
• GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank (2017, methodology): “In fact, [30] assumes that functional annotation within the same ortholog group (OG) should be shared and transferable, focusing on only 4,145 GO terms.”