Feature extraction for cancer prediction by tensor decomposition of 1D protein expression levels

Ivica Kopriva, Ante Jukić, Andrzej Cichocki

Результат исследований: Глава в книге, отчете, сборнике статейМатериалы для конференциирецензирование

3 Цитирования (Scopus)

Аннотация

Tensor decomposition approach to feature extraction from one-dimensional data samples is presented. One-dimensional data samples are transformed into matrices of appropriate dimensions that are further concatenated into a third order tensor. This tensor is factorized according to the Tucker-2 model by means of the higher-order-orthogonal iteration (HOOI) algorithm. Derived method is validated on publicly available and well known datasets comprised of low-resolution mass spectra of cancerous and non-cancerous samples related to ovarian and prostate cancers. The method respectively achieved, in 200 independent two-fold cross-validations, average sensitivity of 96.8% (sd 2.9%) and 99.6% (sd 1.2%) and average specificity of 95.4% (sd 3.5%) and 98.7% (sd 2.9%). Due to the widespread significance of mass spectrometry for monitoring protein expression levels and cancer prediction it is conjectured that presented feature extraction scheme can be of practical importance.

Язык оригиналаАнглийский
Название основной публикацииProceedings of the 2nd IASTED International Conference on Computational Bioscience, CompBio 2011
Страницы277-283
Число страниц7
DOI
СостояниеОпубликовано - 2011
Опубликовано для внешнего пользованияДа
Событие2nd International Conference on Computational Bioscience, CompBio 2011 - Cambridge, Великобритания
Продолжительность: 11 июл. 201113 июл. 2011

Серия публикаций

НазваниеProceedings of the 2nd IASTED International Conference on Computational Bioscience, CompBio 2011

Конференция

Конференция2nd International Conference on Computational Bioscience, CompBio 2011
Страна/TерриторияВеликобритания
ГородCambridge
Период11/07/1113/07/11

Fingerprint

Подробные сведения о темах исследования «Feature extraction for cancer prediction by tensor decomposition of 1D protein expression levels». Вместе они формируют уникальный семантический отпечаток (fingerprint).

Цитировать