scholarly journals Multi-Instance Metric Transfer Learning for Genome-Wide Protein Function Prediction

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Yonghui Xu ◽  
Huaqing Min ◽  
Qingyao Wu ◽  
Hengjie Song ◽  
Bicui Ye
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Naihui Zhou ◽  
Yuxiang Jiang ◽  
Timothy R. Bergquist ◽  
Alexandra J. Lee ◽  
Balint Z. Kacsoh ◽  
...  

Abstract Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.


2019 ◽  
Author(s):  
Naihui Zhou ◽  
Yuxiang Jiang ◽  
Timothy R Bergquist ◽  
Alexandra J Lee ◽  
Balint Z Kacsoh ◽  
...  

AbstractThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Here we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility (P. aureginosa only). We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. We conclude that, while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. We finally report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bioontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.


PLoS ONE ◽  
2007 ◽  
Vol 2 (3) ◽  
pp. e337 ◽  
Author(s):  
Naoki Nariai ◽  
Eric D. Kolaczyk ◽  
Simon Kasif

2017 ◽  
Vol 47 (11) ◽  
pp. 1538-1550
Author(s):  
Jiansheng WU ◽  
Mao ZHENG ◽  
Haifeng HU ◽  
Weijian WU ◽  
Jun WANG

Sign in / Sign up

Export Citation Format

Share Document