scholarly journals Combining Multisource Information Through Functional-Annotation-Based Weighting: Gene Function Prediction in Yeast

2009 ◽  
Vol 56 (2) ◽  
pp. 229-236 ◽  
Author(s):  
S.S. Ray ◽  
S. Bandyopadhyay ◽  
S.K. Pal
Keyword(s):  
Gene Function ◽  
Functional Annotation ◽  
Function Prediction ◽  
Gene Function Prediction

scholarly journals Non-Homology-Based Prediction of Gene Functions

2019 ◽  
Author(s):  
Xiuru Dai ◽  
Zheng Xu ◽  
Zhikai Liang ◽  
Xiaoyu Tu ◽  
Silin Zhong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gene Function ◽  
Functional Annotation ◽  
Function Prediction ◽  
Classification Algorithms ◽  
Gene Function Prediction ◽  
Machine Learning Classification ◽  
New Gene ◽  
Go Terms ◽  
Annotation Errors

AbstractAdvances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations.

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 ◽  
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