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Unsupervised feature selection in linked biological data

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Abstract

Feature selection techniques have become an apparent need in many bioinformatics applications, especially when there exist a huge number of features. For instance, classification of hereditary disease genes/proteins plays a significant role in prediction and diagnosis of diseases. In this regard, some knowledge of features’ goodness in making predictions is needed. Apparently, distinctive features and their relevancy to class labels are determinant in designing efficient classifiers. Indeed, excluding redundant and/or irrelevant features, without incurring much loss of information, can reduce the processing cost while improving the predictor’s performance. Consequently, feature selection is a preliminary task in most biological studies. Traditionally, biological data analysis methods also use the common feature selection techniques which imagine the data instances as independent objects and so not consider their possibly inter-relations. For instance, protein–protein interactions (PPIs) handle a wide range of biological processes including cell-to-cell interactions and metabolic and developmental control. Apparently, linked data have more similar characteristics than uncorrelated ones and so accounting these inter-relations beside to data content will be beneficial in feature selection. To incorporate the data inter-relations (e.g., PPIs in biological data) along with the data content in selecting more effective features, a novel feature selection algorithm is proposed. This method works in unsupervised manner to handle the unlabeled biological data since most of the real-world genes/proteins have no label. For this purpose, we try to optimize a novel objective function which incorporates both the inter-relations of data instances and their content. The proposed method tries to identify the most relevant and non-redundant features and extract the top-ranked ones. For this purpose, an efficient iterative algorithm is developed to optimize the objective function. To assess our methods, three well-known evaluation criteria are examined on some real-world biological datasets and the results are compared against some of the state-of-the-art feature selection methods. The experiments demonstrate the effectiveness of our proposed approach.

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Notes

  1. http://www.ebi.ac.uk/intact/.

  2. http://www.uniprot.org/uniprot/.

  3. http://www.hprd.org/.

  4. http://het.io/disease-genes/.

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Authors and Affiliations

  1. Shiraz University, Shiraz, Iran

    Elham Hoseini & Eghbal G. Mansoori

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  1. Elham Hoseini
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  2. Eghbal G. Mansoori
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Correspondence to Elham Hoseini.

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Hoseini, E., Mansoori, E.G. Unsupervised feature selection in linked biological data. Pattern Anal Applic 22, 999–1013 (2019). https://doi.org/10.1007/s10044-018-0707-2

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  • DOI: https://doi.org/10.1007/s10044-018-0707-2

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