Towards Formal Interpretation of Linear Models Learnt from Genome-wide Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00243199" target="_blank" >RIV/68407700:21230/16:00243199 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Towards Formal Interpretation of Linear Models Learnt from Genome-wide Data
Original language description
Current high-throughput technologies lead to the boost of omics data with thousands of features measured in parallel. The phenotype specific markers are learned from the data to better understand the disease mechanism and to build predictive models. However, the learning is prone to overfitting, caused by a small sample size and large feature space dimension. Consequently, resulting models are inaccurate and difficult to interpret due to the complex nature of omics processes. In this paper we present the discoveries and nuggets we have made by tuning regularization parameters of our method we have recently developed. We extracted nuggets supported by relevant literature record. The main contribution is that these nuggets are relevant to potentially causal mutations, though extracted from solely gene expression, i.e. non-mutational data.
Czech name
—
Czech description
—

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 20th International Scientific Student Conferenece POSTER 2016
ISBN
978-80-01-05950-0
ISSN
—
e-ISSN
—
Number of pages
4
Pages from-to
1-4
Publisher name
Czech Technical University in Prague
Place of publication
Praha
Event location
Praha
Event date
May 24, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)