Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving generalization of machine learning-identified biomarkers with
  causal modeling: an investigation into immune receptor diagnostics   Machine learning is increasingly used to discover diagnostic and prognostic
biomarkers from high-dimensional molecular data. However, a variety of factors
related to experimental design may affect the ability to learn generalizable
and clinically applicable diagnostics. Here, we argue that a causal perspective
improves the identification of these challenges, and formalizes their relation
to the robustness and generalization of machine learning-based diagnostics. To
make for a concrete discussion, we focus on a specific, recently established
high-dimensional biomarker - adaptive immune receptor repertoires (AIRRs). We
discuss how the main biological and experimental factors of the AIRR domain may
influence the learned biomarkers and provide easily adjustable simulations of
such effects. In conclusion, we find that causal modeling improves machine
learning-based biomarker robustness by identifying stable relations between
variables and by guiding the adjustment of the relations and variables that
vary between populations.

7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th