One data processing pipeline is used to pre-build homolog gene families, identify orthologs and dump the resultant data into the database (we refer to this pipeline as " pre-built" pipeline thereafter), and the other one is for on-the-fly classification of the query sequence uploaded online by a user into its best matched gene family (we refer to this pipeline as " on-the-fly" pipeline thereafter).
Learning algorithms could also be used to do on-the-fly load classification and comparison to established system models.
As for the climbing activity phenotypic classification, flies were termed normal when were undistinguishable from WT, "lack of climbing capacity" when flies stayed permanently at the bottom of the culture vials, "lack of equilibrium when standing" or "slowness" if they displayed each of these intermediate movement coordination phenotypes.
We also estimated the number of frames containing erroneous swaps in identities between flies, by setting a classification threshold in which both flies had an identical change in the distance of their positions that was greater than 1.5 mm within a single frame.
Further, this review recommends for the classification of fly ash apart from the existing ASTM classification of fly ash as Class F and Class C. Furthermore, amendments in existing codes are recommended for high volume utilization of fly ash.
In this paper, we address the problem of integrating new classes on the fly into on-line classification systems.
This paper details the methods used to acquire the particle composition data and the derivation of the representative groups of the fly ash particles or classification groups.
New catalysts designed as glass crystal microspheres were obtained as a result of coal and lignite combustion, hydrodynamic classification of fly ash followed by magnetic separation.
In this review paper, we identify a novel classification of flying drones that ranges from unmanned air vehicles to smart dusts at both ends of this spectrum, with their new defined applications.
If this were measurable, it may be possible to mix training datasets on-the-fly and rebuild supervised classification models to reflect the context of the antecedent event and measure public reaction in real-time.
MetAnnotate has a range of novel features that distinguish it from other metagenome analysis tools: automated function selection (PFAM HMMs, GO terms and pathways), easy integration of custom HMMs, on-the-fly search and taxonomic classification, a built-in web server and queue capabilities for larger-scale job handling, and a web interface for comparative analysis and results visualization.
