Supervised Gene Function Prediction Using Spectral Clustering on Gene Co-expression Networks

2022 ◽  
pp. 652-663
Author(s):  
Miguel Romero ◽  
Óscar Ramírez ◽  
Jorge Finke ◽  
Camilo Rocha
Keyword(s):  
Gene Function ◽  
Spectral Clustering ◽  
Function Prediction ◽  
Gene Function Prediction

scholarly journals Accurate and efficient gene function prediction using a multi-bacterial network

2020 ◽  
Author(s):  
Jeffrey N Law ◽  
Shiv D Kale ◽  
T M Murali
Keyword(s):  
Gene Function ◽  
Bacterial Species ◽  
Heterogeneous Data ◽  
Function Prediction ◽  
Label Propagation ◽  
Supplementary Information ◽  
Gene Function Prediction ◽  
Functional Annotations ◽  
A Genome ◽  
Multiple Species

Abstract Motivation Nearly 40% of the genes in sequenced genomes have no experimentally or computationally derived functional annotations. To fill this gap, we seek to develop methods for network-based gene function prediction that can integrate heterogeneous data for multiple species with experimentally based functional annotations and systematically transfer them to newly sequenced organisms on a genome-wide scale. However, the large sizes of such networks pose a challenge for the scalability of current methods. Results We develop a label propagation algorithm called FastSinkSource. By formally bounding its rate of progress, we decrease the running time by a factor of 100 without sacrificing accuracy. We systematically evaluate many approaches to construct multi-species bacterial networks and apply FastSinkSource and other state-of-the-art methods to these networks. We find that the most accurate and efficient approach is to pre-compute annotation scores for species with experimental annotations, and then to transfer them to other organisms. In this manner, FastSinkSource runs in under 3 min for 200 bacterial species. Availability and implementation An implementation of our framework and all data used in this research are available at https://github.com/Murali-group/multi-species-GOA-prediction. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Gene function prediction from synthetic lethality networks via ranking on demand

2010 ◽  
Vol 26 (7) ◽  
pp. 912-918 ◽  
Author(s):  
Christoph Lippert ◽  
Zoubin Ghahramani ◽  
Karsten M. Borgwardt
Keyword(s):  
Gene Function ◽  
Synthetic Lethality ◽  
Function Prediction ◽  
Gene Function Prediction ◽  
On Demand

Hierarchical multi-label classification with SVMs: A case study in gene function prediction

2014 ◽  
Vol 18 (4) ◽  
pp. 717-738 ◽  
Author(s):  
Peerapon Vateekul ◽  
Miroslav Kubat ◽  
Kanoksri Sarinnapakorn
Keyword(s):  
Gene Function ◽  
Function Prediction ◽  
Gene Function Prediction
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