PAM clustering algorithm based on mutual information matrix for ATR-FTIR spectral feature selection and disease diagnosis

The ATR-FTIR spectral data represent a valuable source of information in a wide range of pathologies, including neurological disorders, and can be used for disease discrimination. To this end, the identification of the potential spectral biomarkers among all possible candidates is needed, but the am...

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Veröffentlicht in:	BMC medical research methodology Jg. 25; H. 1; S. 225 - 13
Hauptverfasser:	Condino, Francesca, Crocco, Maria Caterina, Guzzi, Rita
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 01.10.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Cluster Analysis Clustering Clustering Algorithms Data analysis Dependence Diagnosis Disease Diseases Dissimilarity measure Feature selection Fourier transform infrared spectroscopy Health Sciences High-dimensional statistics and omics data analysis Humans Italy Lipids Medical diagnosis Medical research Medicine Medicine & Public Health Medicine, Experimental Methods Multiple Sclerosis - diagnosis Neurological disorders Physiological aspects Principal components analysis Random variables Shannon entropy Spectroscopy, Fourier Transform Infrared - methods Spectrum analysis Statistical Theory and Methods Statistics for Life Sciences Theory of Medicine/Bioethics Italy Shannon entropy Feature selection Dissimilarity measure Clustering Dependence
ISSN:	1471-2288, 1471-2288
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Abstract	The ATR-FTIR spectral data represent a valuable source of information in a wide range of pathologies, including neurological disorders, and can be used for disease discrimination. To this end, the identification of the potential spectral biomarkers among all possible candidates is needed, but the amount of information characterizing the spectral dataset and the presence of redundancy among data could make the selection of the more informative features cumbersome. Here, a novel approach is proposed to perform feature selection based on redundant information among spectral data. In particular, we consider the Partition Around Medoids algorithm based on a dissimilarity matrix obtained from mutual information measure, in order to obtain groups of variables (wavenumbers) having similar patterns of pairwise dependence. Indeed, an advantage of this grouping algorithm with respect to other more widely used clustering methods, is to facilitate the interpretation of results, since the centre of each cluster, the so-called medoid, corresponds to an observed data point. As a consequence, the obtained medoid can be considered as representative of the whole wavenumbers belonging to the same cluster and retained in the subsequent statistical methods for disease prediction. An application on real data is finally reported to show the ability of the proposed approach in discriminating between patients affected by multiple sclerosis and healthy subjects.
AbstractList	The ATR-FTIR spectral data represent a valuable source of information in a wide range of pathologies, including neurological disorders, and can be used for disease discrimination. To this end, the identification of the potential spectral biomarkers among all possible candidates is needed, but the amount of information characterizing the spectral dataset and the presence of redundancy among data could make the selection of the more informative features cumbersome. Here, a novel approach is proposed to perform feature selection based on redundant information among spectral data. In particular, we consider the Partition Around Medoids algorithm based on a dissimilarity matrix obtained from mutual information measure, in order to obtain groups of variables (wavenumbers) having similar patterns of pairwise dependence. Indeed, an advantage of this grouping algorithm with respect to other more widely used clustering methods, is to facilitate the interpretation of results, since the centre of each cluster, the so-called medoid, corresponds to an observed data point. As a consequence, the obtained medoid can be considered as representative of the whole wavenumbers belonging to the same cluster and retained in the subsequent statistical methods for disease prediction. An application on real data is finally reported to show the ability of the proposed approach in discriminating between patients affected by multiple sclerosis and healthy subjects. The ATR-FTIR spectral data represent a valuable source of information in a wide range of pathologies, including neurological disorders, and can be used for disease discrimination. To this end, the identification of the potential spectral biomarkers among all possible candidates is needed, but the amount of information characterizing the spectral dataset and the presence of redundancy among data could make the selection of the more informative features cumbersome. Here, a novel approach is proposed to perform feature selection based on redundant information among spectral data. In particular, we consider the Partition Around Medoids algorithm based on a dissimilarity matrix obtained from mutual information measure, in order to obtain groups of variables (wavenumbers) having similar patterns of pairwise dependence. Indeed, an advantage of this grouping algorithm with respect to other more widely used clustering methods, is to facilitate the interpretation of results, since the centre of each cluster, the so-called medoid, corresponds to an observed data point. As a consequence, the obtained medoid can be considered as representative of the whole wavenumbers belonging to the same cluster and retained in the subsequent statistical methods for disease prediction. An application on real data is finally reported to show the ability of the proposed approach in discriminating between patients affected by multiple sclerosis and healthy subjects.The ATR-FTIR spectral data represent a valuable source of information in a wide range of pathologies, including neurological disorders, and can be used for disease discrimination. To this end, the identification of the potential spectral biomarkers among all possible candidates is needed, but the amount of information characterizing the spectral dataset and the presence of redundancy among data could make the selection of the more informative features cumbersome. Here, a novel approach is proposed to perform feature selection based on redundant information among spectral data. In particular, we consider the Partition Around Medoids algorithm based on a dissimilarity matrix obtained from mutual information measure, in order to obtain groups of variables (wavenumbers) having similar patterns of pairwise dependence. Indeed, an advantage of this grouping algorithm with respect to other more widely used clustering methods, is to facilitate the interpretation of results, since the centre of each cluster, the so-called medoid, corresponds to an observed data point. As a consequence, the obtained medoid can be considered as representative of the whole wavenumbers belonging to the same cluster and retained in the subsequent statistical methods for disease prediction. An application on real data is finally reported to show the ability of the proposed approach in discriminating between patients affected by multiple sclerosis and healthy subjects. Abstract The ATR-FTIR spectral data represent a valuable source of information in a wide range of pathologies, including neurological disorders, and can be used for disease discrimination. To this end, the identification of the potential spectral biomarkers among all possible candidates is needed, but the amount of information characterizing the spectral dataset and the presence of redundancy among data could make the selection of the more informative features cumbersome. Here, a novel approach is proposed to perform feature selection based on redundant information among spectral data. In particular, we consider the Partition Around Medoids algorithm based on a dissimilarity matrix obtained from mutual information measure, in order to obtain groups of variables (wavenumbers) having similar patterns of pairwise dependence. Indeed, an advantage of this grouping algorithm with respect to other more widely used clustering methods, is to facilitate the interpretation of results, since the centre of each cluster, the so-called medoid, corresponds to an observed data point. As a consequence, the obtained medoid can be considered as representative of the whole wavenumbers belonging to the same cluster and retained in the subsequent statistical methods for disease prediction. An application on real data is finally reported to show the ability of the proposed approach in discriminating between patients affected by multiple sclerosis and healthy subjects. The ATR-FTIR spectral data represent a valuable source of information in a wide range of pathologies, including neurological disorders, and can be used for disease discrimination. To this end, the identification of the potential spectral biomarkers among all possible candidates is needed, but the amount of information characterizing the spectral dataset and the presence of redundancy among data could make the selection of the more informative features cumbersome. Here, a novel approach is proposed to perform feature selection based on redundant information among spectral data. In particular, we consider the Partition Around Medoids algorithm based on a dissimilarity matrix obtained from mutual information measure, in order to obtain groups of variables (wavenumbers) having similar patterns of pairwise dependence. Indeed, an advantage of this grouping algorithm with respect to other more widely used clustering methods, is to facilitate the interpretation of results, since the centre of each cluster, the so-called medoid, corresponds to an observed data point. As a consequence, the obtained medoid can be considered as representative of the whole wavenumbers belonging to the same cluster and retained in the subsequent statistical methods for disease prediction. An application on real data is finally reported to show the ability of the proposed approach in discriminating between patients affected by multiple sclerosis and healthy subjects. Keywords: Feature selection, Shannon entropy, Dependence, Clustering, Dissimilarity measure
ArticleNumber	225
Audience	Academic
Author	Guzzi, Rita Condino, Francesca Crocco, Maria Caterina
Author_xml	– sequence: 1 givenname: Francesca surname: Condino fullname: Condino, Francesca email: francesca.condino@unical.it organization: Department of Economics, Statistics and Finance “Giovanni Anania”, University of Calabria – sequence: 2 givenname: Maria Caterina surname: Crocco fullname: Crocco, Maria Caterina organization: Department of Physics, Molecular Biophysics Laboratory, University of Calabria, STAR Research Infrastructure, University of Calabria, CNR-Nanotec – sequence: 3 givenname: Rita surname: Guzzi fullname: Guzzi, Rita organization: Department of Physics, Molecular Biophysics Laboratory, University of Calabria, CNR-Nanotec
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/41034819$$D View this record in MEDLINE/PubMed
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Snippet	The ATR-FTIR spectral data represent a valuable source of information in a wide range of pathologies, including neurological disorders, and can be used for... Abstract The ATR-FTIR spectral data represent a valuable source of information in a wide range of pathologies, including neurological disorders, and can be...
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SubjectTerms	Algorithms Cluster Analysis Clustering Clustering Algorithms Data analysis Dependence Diagnosis Disease Diseases Dissimilarity measure Feature selection Fourier transform infrared spectroscopy Health Sciences High-dimensional statistics and omics data analysis Humans Italy Lipids Medical diagnosis Medical research Medicine Medicine & Public Health Medicine, Experimental Methods Multiple Sclerosis - diagnosis Neurological disorders Physiological aspects Principal components analysis Random variables Shannon entropy Spectroscopy, Fourier Transform Infrared - methods Spectrum analysis Statistical Theory and Methods Statistics for Life Sciences Theory of Medicine/Bioethics
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Title	PAM clustering algorithm based on mutual information matrix for ATR-FTIR spectral feature selection and disease diagnosis
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Volume	25
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