scholarly journals PFP/ESG: automated protein function prediction servers enhanced with Gene Ontology visualization tool

2014 ◽  
Vol 31 (2) ◽  
pp. 271-272 ◽  
Author(s):  
Ishita K. Khan ◽  
Qing Wei ◽  
Meghana Chitale ◽  
Daisuke Kihara
Keyword(s):  
Gene Ontology ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Visualization Tool ◽  
Ontology Visualization

Gene Ontology GAN (GOGAN): a novel architecture for protein function prediction

2022 ◽  
Author(s):  
Musadaq Mansoor ◽  
Mohammad Nauman ◽  
Hafeez Ur Rehman ◽  
Alfredo Benso
Keyword(s):  
Gene Ontology ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction

scholarly journals Large-scale protein function prediction using heterogeneous ensembles

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1577 ◽  
Author(s):  
Linhua Wang ◽  
Jeffrey Law ◽  
Shiv D. Kale ◽  
T. M. Murali ◽  
Gaurav Pandey
Keyword(s):  
Gene Ontology ◽  
Logistic Regression ◽  
Protein Function ◽  
Large Scale ◽  
Protein Function Prediction ◽  
Ensemble Methods ◽  
Function Prediction ◽  
Data Type ◽  
Heterogeneous Ensemble ◽  
The Ideal

Heterogeneous ensembles are an effective approach in scenarios where the ideal data type and/or individual predictor are unclear for a given problem. These ensembles have shown promise for protein function prediction (PFP), but their ability to improve PFP at a large scale is unclear. The overall goal of this study is to critically assess this ability of a variety of heterogeneous ensemble methods across a multitude of functional terms, proteins and organisms. Our results show that these methods, especially Stacking using Logistic Regression, indeed produce more accurate predictions for a variety of Gene Ontology terms differing in size and specificity. To enable the application of these methods to other related problems, we have publicly shared the HPC-enabled code underlying this work as LargeGOPred (https://github.com/GauravPandeyLab/LargeGOPred).

Gene Ontology-Based Protein Function Prediction by Using Sequence Composition Information

2010 ◽  
Vol 17 (6) ◽  
pp. 789-795 ◽  
Author(s):  
Qiwen Dong ◽  
Shuigeng Zhou ◽  
Lei Deng ◽  
Jihong Guan
Keyword(s):  
Gene Ontology ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Sequence Composition

scholarly journals Wei2GO: weighted sequence similarity-based protein function prediction

2020 ◽  
Author(s):  
Maarten J.M.F Reijnders
Keyword(s):  
Gene Ontology ◽  
Open Source ◽  
Protein Function ◽  
Large Scale ◽  
Sequence Similarity ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Computational Time ◽  
Web Servers ◽  
Weighted Sequence

AbstractBackgroundProtein function prediction is an important part of bioinformatics and genomics studies. There are many different predictors available, however most of these are in the form of web-servers instead of open-source locally installable versions. Such local versions are necessary to perform large scale genomics studies due to the presence of limitations imposed by web servers such as queues, prediction speed, and updatability of databases.MethodsThis paper describes Wei2GO: a weighted sequence similarity and python-based open-source protein function prediction software. It uses DIAMOND and HMMScan sequence alignment searches against the UniProtKB and Pfam databases respectively, transfers Gene Ontology terms from the reference protein to the query protein, and uses a weighing algorithm to calculate a score for the Gene Ontology annotations.ResultsWei2GO is compared against the Argot2 and Argot2.5 web servers, which use a similar concept, and DeepGOPlus which acts as a reference. Wei2GO shows an increase in performance according to precision and recall curves, Fmax scores, and Smin scores for biological process and molecular function ontologies. Computational time compared to Argot2 and Argot2.5 is decreased from several hours to several minutes.AvailabilityWei2GO is written in Python 3, and can be found at https://gitlab.com/mreijnders/Wei2GO

HashGO: hashing gene ontology for protein function prediction

2017 ◽  
Vol 71 ◽  
pp. 264-273 ◽  
Author(s):  
Guoxian Yu ◽  
Yingwen Zhao ◽  
Chang Lu ◽  
Jun Wang
Keyword(s):  
Gene Ontology ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction
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