Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Applications of Machine Learning Methods to Quantifying Phenotypic
  Traits that Distinguish the Wild Type from the Mutant Arabidopsis Thaliana
  Seedlings during Root Gravitropism   Post-genomic research deals with challenging problems in screening genomes of
organisms for particular functions or potential for being the targets of
genetic engineering for desirable biological features. 'Phenotyping' of wild
type and mutants is a time-consuming and costly effort by many individuals.
This article is a preliminary progress report in research on large-scale
automation of phenotyping steps (imaging, informatics and data analysis) needed
to study plant gene-proteins networks that influence growth and development of
plants. Our results undermine the significance of phenotypic traits that are
implicit in patterns of dynamics in plant root response to sudden changes of
its environmental conditions, such as sudden re-orientation of the root tip
against the gravity vector. Including dynamic features besides the common
morphological ones has paid off in design of robust and accurate machine
learning methods to automate a typical phenotyping scenario, i.e. to
distinguish the wild type from the mutants.

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The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
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The categories of this paper are: physics
4
The categories of this paper are: cond-mat
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The categories of this paper are: eess