Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The efficacy of various machine learning models for multi-class
  classification of RNA-seq expression data   Late diagnosis and high costs are key factors that negatively impact the care
of cancer patients worldwide. Although the availability of biological markers
for the diagnosis of cancer type is increasing, costs and reliability of tests
currently present a barrier to the adoption of their routine use. There is a
pressing need for accurate methods that enable early diagnosis and cover a
broad range of cancers. The use of machine learning and RNA-seq expression
analysis has shown promise in the classification of cancer type. However,
research is inconclusive about which type of machine learning models are
optimal. The suitability of five algorithms were assessed for the
classification of 17 different cancer types. Each algorithm was fine-tuned and
trained on the full array of 18,015 genes per sample, for 4,221 samples (75 %
of the dataset). They were then tested with 1,408 samples (25 % of the dataset)
for which cancer types were withheld to determine the accuracy of prediction.
The results show that ensemble algorithms achieve 100% accuracy in the
classification of 14 out of 17 types of cancer. The clustering and
classification models, while faster than the ensembles, performed poorly due to
the high level of noise in the dataset. When the features were reduced to a
list of 20 genes, the ensemble algorithms maintained an accuracy above 95% as
opposed to the clustering and classification models.

11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an