Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Meta Analytic Data Integration for Phenotype Prediction: Application to
  Chronic Fatigue Syndrome   Predictive modeling plays key role in providing accurate prognosis and
enables us to take a step closer to personalized treatment. We identified two
potential sources of human induced biases that can lead to disparate
conclusions. We illustrate through a complex phenotype that robust results can
still be drawn after accounting for such biases.
  Often predictive models build based in high dimensional data suffers from the
drawback of lack of interpretability. To achieve interpretability in the form
of description of the organism level phenomena in term of molecular or cellular
level activities, functional and pathway information is often augmented.
Functional information can greatly facilitate the interpretation of the results
of the predictive model.
  However an important aspect of (vertical) data augmentation is routinely
ignored, that is there could be several stages of analysis where such
information could be meaningfully integrated. There is no know criteria to
enable us to assess the effect of such augmentation. A novel aspect of the
proposed work is in exploring possibilities of stages of analysis where
functional information may be incorporated and in assessing the extent to which
the ultimate conclusions would differ depending on level of amalgamation.
  To boost our confidence on the key findings a first level of meta-analysis is
done by exploring different levels of data augmentation. This is followed by
comparison of predictive models across different definitions of the same
phenotype developed by different groups, which is also an extended form of
meta-analysis.
  We have used real life data on a complex phenotype to illustrate the above.
The data pertains to Chronic Fatigue Syndrome (CFS) and another novel aspect of
the current work is in modeling the underlying continuous symptom measurements
for CFS, which is the first for this disease to our knowledge.

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The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
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The categories of this paper are: math
18
The categories of this paper are: econ
19
The categories of this paper are: eess
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org