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From Unsupervised Multi-Instance Learning to Identification of Near-Native Protein Structures

10 pagesPublished: March 11, 2020

Abstract

A major challenge in computational biology regards recognizing one or more biologically- active/native tertiary protein structures among thousands of physically-realistic structures generated via template-free protein structure prediction algorithms. Clustering structures based on structural similarity remains a popular approach. However, clustering orga- nizes structures into groups and does not directly provide a mechanism to select individual structures for prediction. In this paper, we provide a few algorithms for this selection prob- lem. We approach the problem under unsupervised multi-instance learning and address it in three stages, first organizing structures into bags, identifying relevant bags, and then drawing individual structures/instances from these bags. We present both non-parametric and parametric algorithms for drawing individual instances. In the latter, parameters are trained over training data and evaluated over testing data via rigorous metrics.

Keyphrases: multi instance learning, protein structure prediction, protein tertiary structure, unsupervised learning

In: Qin Ding, Oliver Eulenstein and Hisham Al-Mubaid (editors). Proceedings of the 12th International Conference on Bioinformatics and Computational Biology, vol 70, pages 59-68.

BibTeX entry
@inproceedings{BICOB2020:From_Unsupervised_Multi_Instance,
  author    = {Fardina Alam and Amarda Shehu},
  title     = {From Unsupervised Multi-Instance Learning to Identification of Near-Native Protein Structures},
  booktitle = {Proceedings of the 12th International Conference on Bioinformatics and Computational Biology},
  editor    = {Qin Ding and Oliver Eulenstein and Hisham Al-Mubaid},
  series    = {EPiC Series in Computing},
  volume    = {70},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/s9h6},
  doi       = {10.29007/pjcf},
  pages     = {59-68},
  year      = {2020}}
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