Name
Abstract | ||
|---|---|---|
Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the vast majority of proteins can only be annotated computationally. Nature often brings several domains together to form multi-domain and multi-functional proteins with a vast number of possibilities, and each domain may fulfill its own function independently or in a concerted manner with its neighbors. Thus, it is evident that the protein function prediction problem is naturally and inherently Multi-Instance Multi-Label (MIML) learning tasks. Based on the state-of-the-art MIML algorithm MIMLNN, we propose a novel ensemble MIML learning framework EnMIMLNN and design three algorithms for this task by combining the advantage of three kinds of Hausdorff distance metrics. Experiments on seven real-world organisms covering the biological three-domain system, i.e., archaea, bacteria, and eukaryote, show that the EnMIMLNN algorithms are superior to most state-of-the-art MIML and Multi-Label learning algorithms. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TCBB.2014.2323058 | IEEE/ACM Trans. Comput. Biology Bioinform. |
Keywords | Field | DocType |
genome-wide protein function prediction,hausdorff distance metrics,hausdorff distances,multiinstance multilabel learning tasks,genome sequence,biological three-domain system,learning (artificial intelligence),miml learning framework enmimlnn,microorganisms,genomics,proteins,protein function prediction,biology computing,protein function prediction problem,molecular biophysics,molecular configurations,eukaryote,protein function,ensemble learning,multidomain proteins,bacteria,multi-instance multi-label learning,archaea,machine learning,real-world organisms,genome wide,multiinstance multilabel learning,multifunctional proteins,automated annotation,bioinformatics,organisms,prediction algorithms | Genome,Annotation,Multi instance multi label,Computer science,Genomics,Protein function,Hausdorff distance,Artificial intelligence,Bioinformatics,Protein function prediction,Ensemble learning,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 5 | 1545-5963 |
Citations | PageRank | References |
26 | 0.70 | 27 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian-Sheng Wu | 1 | 39 | 1.31 |
| Sheng-Jun Huang | 2 | 475 | 27.21 |
| Zhi-Hua Zhou | 3 | 13480 | 569.92 |