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Machine learning-based techniques have also been proposed for model learning including neural networks, time regression, and space vector machines [5, 17, 18].

For example, within European birth cohort studies, GIS-based techniques have also being used for assessment of noise.

PCR-based techniques have also allowed the quantification of the number of repeats shaping specific LCRs (7q11.23; [9]).

Recently, proteomics-based techniques have also been used for identification of biomarkers of diagnosis, prognosis, and/or therapy responses in the field of cardiovascular diseases [ 9– 11].

Studies using whole genome SNP array (McGuire et al., 2011) and targeted analysis of the X chromosome using array-based techniques have also been reported (Dudding et al., 2010; Quilter et al., 2010; Knauff et al., 2011).

Algorithms based on machine learning techniques have also been proposed[8, 9].

Studies based on infrared spectrometry techniques have also demonstrated substantial alteration in secondary structures of human CRP in presence of Mg ions 37).

Alternatively, solutions based exclusively on signal processing techniques have also been developed to minimize or cancel the drift [ 1, 22, 35].

These techniques have also been used to predict PPIs based on the topological properties of the neighbours of candidate protein pairs (You et al., 2010).

Other techniques have also been developed such as nucleic acid sequence based amplification (NASBA), although until now they have had limited practical relevance for food monitoring and control.

Other "fingerprinting" techniques have also been developed.