UkrAD
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB64640 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR13697800.fastq.gz | Fastqsanger.gz | |||
| ERR13697801.fastq.gz | Fastqsanger.gz | |||
| ERR13697802.fastq.gz | Fastqsanger.gz | |||
| ERR13697712.fastq.gz | Fastqsanger.gz | |||
| ERR13697713.fastq.gz | Fastqsanger.gz |
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Project description:This dataset encompassing the profiles of 150 lung cancer tumors was developed to serve as test dataset in the SBV IMPROVER Diagnostic Signature Challenge (sbvimprover.com). The aim of this subchallenge was to verify that it is possible to extract a robust diagnostic signature from gene expression data that can identify stages of different types of lung cancer. Participants were asked to develop and submit a classifier that can stratify lung cancer patients in one of four groups M-bM-^@M-^S Stage 1 of Adenocarcinoma (AC Stage 1), Stage 2 of Adenocarcinoma (AC Stage 2), Stage 1 of Squamous cell carcinoma (SCC Stage 1) or Stage 2 of Squamous cell carcinoma (SCC Stage 2). The classifier could be built by using any publicly available gene expression data with related histopathological information and was tested on the independent dataset described here. 150 non-small cell lung cancer tumors (adenocarcinoma, AC and squamous cell carcinoma, SCC) of stages I and II were collected by surgical resection from patients who have provided consent. Adenosquamous and large cell tumor samples were excluded. The number of smokers and non-smokers was balanced: there were 41 AC1 (adenocarcinoma stage I), 36 AC2, 34 SCC1, and 39 SCC2 samples. Study pathologists at each of the seven sites (Lebanon, Republic of Moldova, Romania, Russian Federation, Ukraine, Vietnam and United States of America) reviewed both the tumor permanent sections and the frozen sections of the samples. Clinical information was also collected about tumor staging, history of prior cancers, lymph node involvement by lymph node sampling/dissection, smoking history, age, gender.
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Project description:Restif2006 - Whooping cough
This model is described in the article:
Integrating life history and
cross-immunity into the evolutionary dynamics of pathogens.
Restif O, Grenfell BT.
Proc. Biol. Sci. 2006 Feb; 273(1585):
409-416
Abstract:
Models for the diversity and evolution of pathogens have
branched into two main directions: the adaptive dynamics of
quantitative life-history traits (notably virulence) and the
maintenance and invasion of multiple, antigenically diverse
strains that interact with the host's immune memory. In a first
attempt to reconcile these two approaches, we developed a
simple modelling framework where two strains of pathogens,
defined by a pair of life-history traits (infectious period and
infectivity), interfere through a given level of
cross-immunity. We used whooping cough as a potential example,
but the framework proposed here could be applied to other acute
infectious diseases. Specifically, we analysed the effects of
these parameters on the invasion dynamics of one strain into a
population, where the second strain is endemic. Whereas the
deterministic version of the model converges towards stable
coexistence of the two strains in most cases, stochastic
simulations showed that transient epidemic dynamics can cause
the extinction of either strain. Thus ecological dynamics,
modulated by the immune parameters, eventually determine the
adaptive value of different pathogen genotypes. We advocate an
integrative view of pathogen dynamics at the crossroads of
immunology, epidemiology and evolution, as a way towards
efficient control of infectious diseases.
This
version of the model can be used for both the stochastic and the
deterministic simulations described in the article. For
deterministic interpretations with infinite population sizes, set
the population size
N = 1. The model reproduces the deterministic time
courses. Stochastic interpretation with Copasi UI gave results
similar to the article, but was not extensively tested. The
initial conditions for competition simulations can be derived by
equilibrating the system for one pathogen and then adding a
starting concentration for the other.
Originally
created by libAntimony v1.3 (using libSBML 4.1.0-b1)
This model is hosted on
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and identified by:
BIOMD0000000249.
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