Stratification of biological samples by using high-dimensional data, such as those derived from mass spectrometry-based proteomics approaches, has become a promising strategy to solve biological questions, as well as to classify samples in relation to different phenotypes. In this regard, we have discussed some computational aspects related to the processing of Multidimensional Protein Identification Technology data through a class of algorithms widely used in machine learning community, such as support vector machines. Specifically, after a short presentation of the input data structure, we focused on properties and abilities of feature selection and classification models, indicating useful tools for assisting scientists in these computations. Finally, we concluded this review hinting at new strategies of inference which coupled to mass spectrometry improvement,in instruments and methods, may represent the perspectives of this field.
Di Silvestre, D., Zoppis, I., Mauri, G., & Mauri, P. (2013). Stratification of biological samples based on proteomics data. OA BIOINFORMATICS, 1(1), 1-8.
|Citazione:||Di Silvestre, D., Zoppis, I., Mauri, G., & Mauri, P. (2013). Stratification of biological samples based on proteomics data. OA BIOINFORMATICS, 1(1), 1-8.|
|Tipo:||Articolo in rivista - Articolo scientifico|
|Carattere della pubblicazione:||Scientifica|
|Presenza di un coautore afferente ad Istituzioni straniere:||No|
|Titolo:||Stratification of biological samples based on proteomics data|
|Autori:||Di Silvestre, D; Zoppis, I; Mauri, G; Mauri, P|
|Data di pubblicazione:||2013|
|Appare nelle tipologie:||01 - Articolo su rivista|