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To validate our method, we considered several movement pathways from marine animals, each bringing a possibility of evaluating the accuracy of the method and/or posing a particular analytical challenge, as detailed below: Dataset 1 The first data set was composed of 3 Argos tracks of adult female northern elephant seals, Mirounga angustirostris (Gill, 1866) during their post-breeding migrations.

The Matlab code of the used function here (named BandPassFilter.m) is available below (Dataset 7).

The raw data corresponding to the eight time windows and all the different days are available below (Dataset 1– Dataset 6).

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As described further below, this dataset was selected because it exhibits a common type of nonstationarity characterized by relatively abrupt changes of "state".

Below, UCI standard dataset was chosen to study the influence of the size of grouped training set in the GVCA method on the classification performance.

For compact genomes with smaller maximum intron sizes, such as the P. falciparum and A. thaliana datasets below, a shorter anchor size of 6 nt can be matched uniquely (data not shown).

Given that ReliefF generated high quality AD for the benchmark dataset (discussed below), we propose this technique is, in principle, particularly well-suited for AD mapping.

The dog samples were collected to represent different parts of the world (Table 1; see also below and in Supplementary Dataset 3 for complete list of samples).

Even in such a case, we still face a large number of possible parent sets, of size, and so in the analyses presented below, we restrict our dataset to a subset of genes of special interest, as is commonly the case in gene expression data analysis.

In the case study described below, three datasets are included; each having specific advantages and disadvantages (see Table 3).

The method could be deployed within controlled experimental and captive animal settings and, with the below caveats, to natural datasets too.

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