research-article

Leveraging class hierarchy for detecting missing annotations on hierarchical multi-label classification

Authors:

Felipe Kenji Nakano,

Celine VensAuthors Info & Claims

Volume 152, Issue C

https://doi.org/10.1016/j.compbiomed.2022.106423

Published: 01 January 2023 Publication History

Abstract

With the development of new sequencing technologies, availability of genomic data has grown exponentially. Over the past decade, numerous studies have used genomic data to identify associations between genes and biological functions. While these studies have shown success in annotating genes with functions, they often assume that genes are completely annotated and fail to take into account that datasets are sparse and noisy. This work proposes a method to detect missing annotations in the context of hierarchical multi-label classification. More precisely, our method exploits the relations of functions, represented as a hierarchy, by computing probabilities based on the paths of functions in the hierarchy. By performing several experiments on a variety of rice (Oriza sativa Japonica), we showcase that the proposed method accurately detects missing annotations and yields superior results when compared to state-of-art methods from the literature.

Highlights

•

One of the first works to address detection of missing annotations in HMC datasets.

•

A novel state-of-art method based on post-processing predicted probabilities.

•

An experimental evaluation including 8 new datasets related to Oryza sativa Japonica.

•

Exploiting the hierarchy of functions helps to better identify missing gene functions.

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Cited By

Ostapuk NAudiffren JDolamic LMermoud ACudré-Mauroux PChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Follow the Path: Hierarchy-Aware Extreme Multi-Label Completion for Semantic Text TaggingProceedings of the ACM Web Conference 202410.1145/3589334.3645558(2094-2105)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645558

Index Terms

Leveraging class hierarchy for detecting missing annotations on hierarchical multi-label classification
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
    2. Genetics
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Computers in Biology and Medicine

Computers in Biology and Medicine Volume 152, Issue C

Jan 2023

1242 pages

ISSN:0010-4825

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Copyright © 2022.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 January 2023

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Cited By

Ostapuk NAudiffren JDolamic LMermoud ACudré-Mauroux PChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Follow the Path: Hierarchy-Aware Extreme Multi-Label Completion for Semantic Text TaggingProceedings of the ACM Web Conference 202410.1145/3589334.3645558(2094-2105)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645558

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