Dataset Information

Survivorship Care Planning in Patients With Colorectal or Non-Small Cell Lung Cancer

ABSTRACT: Survivorship Care Planning may improve overall well-being and quality of life of colorectal and lung cancer survivors

DISEASE(S): Stage Ia Non-small Cell Lung Cancer,Stage Iib Non-small Cell Lung Cancer,Stage Iiib Non-small Cell Lung Cancer,Stage I Colon Cancer,Stage Iic Colon Cancer,Stage I Rectal Cancer,Stage Iib Colon Cancer,Stage Iiia Rectal Cancer,Lung Neoplasms,Stage Iia Colon Cancer,Stage Iiib Rectal Cancer,Stage Ib Non-small Cell Lung Cancer,Stage Iiib Colon Cancer,Stage Iiic Colon Cancer,Rectal Neoplasms,Stage Iiia Non-small Cell Lung Cancer,Stage Iiic Rectal Cancer,Stage Iic Rectal Cancer,Carcinoma, Non-small-cell Lung,Stage Iib Rectal Cancer,Stage Iia Non-small Cell Lung Cancer,Colonic Neoplasms,Stage Iiia Colon Cancer,Stage Iia Rectal Cancer

PROVIDER: 2136243 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

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Project description:Genome-scale datasets have been used extensively in model organisms to screen for specific candidates or to predict functions for uncharacterized genes. However, despite the availability of extensive knowledge in model organisms, the planning of genome-scale experiments in poorly studied species is still based on the intuition of experts or heuristic trials. We propose that computational and systematic approaches can be applied to drive the experiment planning process in poorly studied species based on available data and knowledge in closely related model organisms. In this paper, we suggest a computational strategy for recommending genome-scale experiments based on their capability to interrogate diverse biological processes to enable protein function assignment. To this end, we use the data-rich functional genomics compendium of the model organism to quantify the accuracy of each dataset in predicting each specific biological process and the overlap in such coverage between different datasets. Our approach uses an optimized combination of these quantifications to recommend an ordered list of experiments for accurately annotating most proteins in the poorly studied related organisms to most biological processes, as well as a set of experiments that target each specific biological process. The effectiveness of this experiment- planning system is demonstrated for two related yeast species: the model organism Saccharomyces cerevisiae and the comparatively poorly studied Saccharomyces bayanus. Our system recommended a set of S. bayanus experiments based on an S. cerevisiae microarray data compendium. In silico evaluations estimate that less than 10% of the experiments could achieve similar functional coverage to the whole microarray compendium. This estimation was confirmed by performing the recommended experiments in S. bayanus, therefore significantly reducing the labor devoted to characterize the poorly studied genome. This experiment-planning framework could readily be adapted to the design of other types of large-scale experiments as well as other groups of organisms.

Dataset Information

Survivorship Care Planning in Patients With Colorectal or Non-Small Cell Lung Cancer

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