JK
Jaap Kool
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
2 records found
1
Discovering cancer pathways
By inferring combinatorial association logic
Poster
(2009)
-
Jeroen de Ridder, Jan Bot, Jaap Kool, Anthony Uren, Lodewyk Wessels, Marcel Reinders
In this study, 43 tumors that were induced by retroviral insertional mutagenesis are expression profiled, resulting in a dataset for which both the initiating events (the viral integration sites) as well as the consequent expression profiles are available.
To capture complex associations that arise due to interaction among insertion target genes, we infer small Boolean logic networks that explicitly incorporate operators to model the potential parallel alternatives (‘exclusive-or’ gates) as well as the potential cooperation between mutations (‘and’ gates). ...
To capture complex associations that arise due to interaction among insertion target genes, we infer small Boolean logic networks that explicitly incorporate operators to model the potential parallel alternatives (‘exclusive-or’ gates) as well as the potential cooperation between mutations (‘and’ gates). ...
In this study, 43 tumors that were induced by retroviral insertional mutagenesis are expression profiled, resulting in a dataset for which both the initiating events (the viral integration sites) as well as the consequent expression profiles are available.
To capture complex associations that arise due to interaction among insertion target genes, we infer small Boolean logic networks that explicitly incorporate operators to model the potential parallel alternatives (‘exclusive-or’ gates) as well as the potential cooperation between mutations (‘and’ gates).
To capture complex associations that arise due to interaction among insertion target genes, we infer small Boolean logic networks that explicitly incorporate operators to model the potential parallel alternatives (‘exclusive-or’ gates) as well as the potential cooperation between mutations (‘and’ gates).
Discovering cooperating oncogenes
By statistical analysis of Retroviral Insertional Mutagenesis Data
Viruses can induce oncogenic mutations when inserted near (or within) proto-oncogenes. Cancer genes can be identified by determining the loci of viral
insertions from tumors induced by retroviruses. Most often, multiple co-occurring mutations are needed for a cell to develop into a tumor. We propose a 2D Gaussian Kernel Convolution method to discover the cooperating oncogenes from publicly available retroviral insertional mutagenesis data.
...
insertions from tumors induced by retroviruses. Most often, multiple co-occurring mutations are needed for a cell to develop into a tumor. We propose a 2D Gaussian Kernel Convolution method to discover the cooperating oncogenes from publicly available retroviral insertional mutagenesis data.
...
Viruses can induce oncogenic mutations when inserted near (or within) proto-oncogenes. Cancer genes can be identified by determining the loci of viral
insertions from tumors induced by retroviruses. Most often, multiple co-occurring mutations are needed for a cell to develop into a tumor. We propose a 2D Gaussian Kernel Convolution method to discover the cooperating oncogenes from publicly available retroviral insertional mutagenesis data.
insertions from tumors induced by retroviruses. Most often, multiple co-occurring mutations are needed for a cell to develop into a tumor. We propose a 2D Gaussian Kernel Convolution method to discover the cooperating oncogenes from publicly available retroviral insertional mutagenesis data.