Publication Date:
2023-07-17
Description:
Recent advances in -omics technology has yielded in large
data-sets in many areas of biology, such as mass spectrometry based proteomics. However, analyzing this data is still a challenging task mainly due to the very high dimensionality and high noise content of the data.
One of the main objectives of the analysis is the identification of relevant patterns (or features) which can be used for classification of new samples to healthy or diseased. So, a method is required to find easily
interpretable models from this data.
To gain the above mentioned goal, we have adapted the disjunctive association rule mining algorithm, TitanicOR, to identify emerging patterns from our mass spectrometry proteomics data-sets. Comparison to five
state-of-the-art methods shows that our method is advantageous them in terms of identifying the inter-dependency between the features and the
TP-rate and precision of the features selected. We further demonstrate the applicability of our algorithm to one previously published clinical data-set.
Language:
English
Type:
conferenceobject
,
doc-type:conferenceObject
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