scholarly journals CrowdGO: a wisdom of the crowd-based Gene Ontology annotation tool

2019 ◽  
Author(s):  
Maarten J.M.F. Reijnders
Keyword(s):  
Gene Ontology ◽  
Protein Function ◽  
Directed Acyclic Graph ◽  
Support Vector Machine Model ◽  
Protein Function Prediction ◽  
Support Vector ◽  
Annotation Tool ◽  
Machine Model ◽  
Prediction Tools ◽  
Go Terms

AbstractMotivationProtein function prediction tools vary widely in their methodologies, resulting in different sets of GO terms being correctly predicted. Ideally, multiple tools are combined to achieve a higher recall of GO terms while increasing precision.ResultsCrowdGO combines input predictions from any number of tools and combines them based on the Gene Ontology Directed Acyclic Graph. Using each GO terms information content, the semantic similarity between GO predictions of different tools, and a Support Vector Machine model, it achieves improved precision and recall compared to each of the tools separately (Figure 1).AvailabilityCrowdGO can be found at https://gitlab.com/mreijnders/CrowdGO

scholarly journals A Topology-Based Metric for Measuring Term Similarity in the Gene Ontology

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Gaston K. Mazandu ◽  
Nicola J. Mulder
Keyword(s):  
Gene Ontology ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Similarity Measures ◽  
Biological Knowledge ◽  
Online Tool ◽  
Protein Protein Interaction ◽  
Or Groups ◽  
Term Similarity ◽  
Go Terms

The wide coverage and biological relevance of the Gene Ontology (GO), confirmed through its successful use in protein function prediction, have led to the growth in its popularity. In order to exploit the extent of biological knowledge that GO offers in describing genes or groups of genes, there is a need for an efficient, scalable similarity measure for GO terms and GO-annotated proteins. While several GO similarity measures exist, none adequately addresses all issues surrounding the design and usage of the ontology. We introduce a new metric for measuring the distance between two GO terms using the intrinsic topology of the GO-DAG, thus enabling the measurement of functional similarities between proteins based on their GO annotations. We assess the performance of this metric using a ROC analysis on human protein-protein interaction datasets and correlation coefficient analysis on the selected set of protein pairs from the CESSM online tool. This metric achieves good performance compared to the existing annotation-based GO measures. We used this new metric to assess functional similarity between orthologues, and show that it is effective at determining whether orthologues are annotated with similar functions and identifying cases where annotation is inconsistent between orthologues.

scholarly journals Protein function prediction with gene ontology: from traditional to deep learning models

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12019
Author(s):  
Thi Thuy Duong Vu ◽  
Jaehee Jung
Keyword(s):  
Gene Ontology ◽  
Deep Learning ◽  
Protein Function ◽  
High Throughput Sequencing ◽  
Protein Function Prediction ◽  
Rapid Development ◽  
Function Prediction ◽  
Amino Acid Sequences ◽  
Go Annotation ◽  
Go Terms

Protein function prediction is a crucial part of genome annotation. Prediction methods have recently witnessed rapid development, owing to the emergence of high-throughput sequencing technologies. Among the available databases for identifying protein function terms, Gene Ontology (GO) is an important resource that describes the functional properties of proteins. Researchers are employing various approaches to efficiently predict the GO terms. Meanwhile, deep learning, a fast-evolving discipline in data-driven approach, exhibits impressive potential with respect to assigning GO terms to amino acid sequences. Herein, we reviewed the currently available computational GO annotation methods for proteins, ranging from conventional to deep learning approach. Further, we selected some suitable predictors from among the reviewed tools and conducted a mini comparison of their performance using a worldwide challenge dataset. Finally, we discussed the remaining major challenges in the field, and emphasized the future directions for protein function prediction with GO.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

Research on SVM Line Loss Rate Prediction Based on Heuristic Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2164-2168 ◽  
Author(s):  
Li Tian ◽  
Qiang Qiang Wang ◽  
An Zhao Cao
Keyword(s):  
Support Vector Machine ◽  
Heuristic Algorithm ◽  
Support Vector Machine Model ◽  
Loss Rate ◽  
Heuristic Algorithms ◽  
Support Vector ◽  
Machine Model ◽  
Rate Prediction ◽  
Line Loss

With the characteristic of line loss volatility, a research of line loss rate prediction was imperatively carried out. Considering the optimization ability of heuristic algorithm and the regression ability of support vector machine, a heuristic algorithm-support vector machine model is constructed. Case study shows that, compared with other heuristic algorithms’, the search efficiency and speed of genetic algorithm are good, and the prediction model is with high accuracy.

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