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Identifier |
000434163 |
Title |
Learning biologically interpretablelLatent representations from gene expression data |
Alternative Title |
Μαθαίνοντας βιολογικά ερμηνεύσιμες κρυφές αναπαραστάσεις από δεδομένα γονιδιακών εκφράσεων |
Author
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Καραγιαννάκη, Ιουλία Ε.
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Thesis advisor
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Τσαμαρδινός, Ιωάννης
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Reviewer
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Τζιρίτας, Γεώργιος
Πανταζής, Γιάννης
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Abstract |
Gene expression data are typically high dimensional with low sample size. This
leads to several statistical and analytical challenges that one needs to overcome in
order to analyze and infer the underlying biological mechanisms of such data. To
this end, several dimensionality reduction techniques have been proposed. Dimensionality reduction techniques learn a lower dimensional space (latent space), of
newly constructed features and represent the data as a sum of those (latent representations). The projection of the data to the latent feature space compresses the
data, retains the significant information and reduces noise.
Typical dimensionality reduction techniques, such as Principal Component
Analysis, derive latent representations that are uninterpretable biologically. In
order to regain a degree of interpretability, other methods return sparse latent representations. Particularly, the new features are constructed as linear combinations
of only a few of the molecular quantities. However, sparse latent representations
are still hard to interpret biologically as they do not directly correspond to the
known biological pathways or other known genesets.
In this thesis, we present a novel algorithm for feature construction and dimensionality reduction called Pathway Activity Score Learning (PASL). The major novelty of PASL is that the constructed features are constrained to directly
correspond to known molecular pathways and can be interpreted as pathway activity scores. PASL is evaluated both on simulated and real data. We show that
PASL retains the predictive information for disease classification on new, unseen
datasets. We also show that differential activation analysis provides complementary information to standard geneset enrichment analysis.
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Language |
English |
Subject |
Dimensionality reduction |
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Disease classification |
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Κατηγοριοποίηση ασθενειών |
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Μείωση διαστάσεων |
Issue date |
2020-11-27 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/1/0/b/metadata-dlib-1606205109-380105-17844.tkl
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Views |
613 |