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To date most of the research on classifying expression data focused on static (snapshot) datasets.
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The approach needs specification of snapshot dataset, which serves as a template during the historical database search.
As case of study, the most common 26 of 48 species from the Snapshot Serengeti (SSe) dataset were selected and the potential of the Very Deep Convolutional neural networks framework for the species identification task was analyzed.
Experiments are performed on real datasets and snapshots are taken from real data.
A snapshot of the published dataset is also available for download.
(A – F ) Snapshots from live imaging datasets for three genotypes with a time lapse of 30 s (A and D ) Venus-Rab7-WT and spin-RFP; (B and E ) Venus-Rab7-Q67L and spin-RFP; (C and F Venus-Rab7-N161T Venus-Rab7-N161T Venus-Rab7-N161T
We could gain more realistic insights by comparing results of dataset snapshots between different points in time.
Table 1 presents the snapshot of the cleaned OSR Repository dataset for the data from 1 November 2009 to 31 May 2014.
Existing repositories for experimental datasets typically capture snapshots of data acquired using a single experimental technique and often require manual population and continual curation.
This issue is particularly pertinent to large omics datasets as their snapshots of the cellular states arise from a variety of interactions throughout cellular networks, even in response to a single local perturbation.
A single genome-scale dataset can capture a snapshot of cellular function; integrative analysis of hundreds or thousands of genome-scale datasets can provide even more extensive systems-level insights regarding gene interactions under diverse conditions (Troyanskaya, 2005).
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