Biomarkers able to characterise and predict multifactorial diseases are still one of the most important targets for all the "omics" investigations. In this context, Matrix-Assisted Laser Desorption/Ionisation-Mass Spectrometry Imaging (MALDI-MSI) has gained considerable attention in recent years, but it also led to a huge amount of complex data to be elaborated and interpreted. For this reason, computational and machine learning procedures for biomarker discovery are important tools to consider, both to reduce data dimension and to provide predictive markers for specific diseases. For instance, the availability of protein and genetic markers to support thyroid lesion diagnoses would impact deeply on society due to the high presence of undetermined reports (THY3) that are generally treated as malignant patients. In this paper we show how an accurate classification of thyroid bioptic specimens can be obtained through the application of a state-of-the-art machine learning approach (i.e., Support Vector Machines) on MALDI-MSI data, together with a particular wrapper feature selection algorithm (i.e., recursive feature elimination). The model is able to provide an accurate discriminatory capability using only 20 out of 144 features, resulting in an increase of the model performances, reliability, and computational efficiency. Finally, tissue areas rather than average proteomic profiles are classified, highlighting potential discriminating areas of clinical interest.

Galli, M., Zoppis, I., DE SIO, G., Chinello, C., Pagni, F., Magni, F., et al. (2016). A Support Vector Machine Classification of Thyroid Bioptic Specimens Using MALDI-MSI Data. ADVANCES IN BIOINFORMATICS, 2016, 1-7 [10.1155/2016/3791214].

A Support Vector Machine Classification of Thyroid Bioptic Specimens Using MALDI-MSI Data

GALLI, MANUEL
Primo
;
ZOPPIS, ITALO FRANCESCO
Secondo
;
DE SIO, GABRIELE;CHINELLO, CLIZIA;PAGNI, FABIO;MAGNI, FULVIO
Penultimo
;
MAURI, GIANCARLO
Ultimo
2016

Abstract

Biomarkers able to characterise and predict multifactorial diseases are still one of the most important targets for all the "omics" investigations. In this context, Matrix-Assisted Laser Desorption/Ionisation-Mass Spectrometry Imaging (MALDI-MSI) has gained considerable attention in recent years, but it also led to a huge amount of complex data to be elaborated and interpreted. For this reason, computational and machine learning procedures for biomarker discovery are important tools to consider, both to reduce data dimension and to provide predictive markers for specific diseases. For instance, the availability of protein and genetic markers to support thyroid lesion diagnoses would impact deeply on society due to the high presence of undetermined reports (THY3) that are generally treated as malignant patients. In this paper we show how an accurate classification of thyroid bioptic specimens can be obtained through the application of a state-of-the-art machine learning approach (i.e., Support Vector Machines) on MALDI-MSI data, together with a particular wrapper feature selection algorithm (i.e., recursive feature elimination). The model is able to provide an accurate discriminatory capability using only 20 out of 144 features, resulting in an increase of the model performances, reliability, and computational efficiency. Finally, tissue areas rather than average proteomic profiles are classified, highlighting potential discriminating areas of clinical interest.
Articolo in rivista - Articolo scientifico
Mass Spectrometry Imaging, Support Vector Machine, Classification, Thyroid lesions
English
1
7
7
Galli, M., Zoppis, I., DE SIO, G., Chinello, C., Pagni, F., Magni, F., et al. (2016). A Support Vector Machine Classification of Thyroid Bioptic Specimens Using MALDI-MSI Data. ADVANCES IN BIOINFORMATICS, 2016, 1-7 [10.1155/2016/3791214].
Galli, M; Zoppis, I; DE SIO, G; Chinello, C; Pagni, F; Magni, F; Mauri, G
File in questo prodotto:
File Dimensione Formato  
R147- Adv Bioinf 2016.pdf

accesso aperto

Dimensione 2.41 MB
Formato Adobe PDF
2.41 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/111036
Citazioni
  • Scopus 17
  • ???jsp.display-item.citation.isi??? ND
Social impact