Dataset Information

Integrating heterogeneous odor response data into a common response model: A DoOR to the complete olfactome.

ABSTRACT: We have developed a new computational framework for merging odor response data sets from heterogeneous studies, creating a consensus metadatabase, the database of odor responses (DoOR). As a result, we obtained a functional atlas of all available odor responses in Drosophila melanogaster. Both the program and the data set are freely accessible and downloadable on the Internet (http://neuro.uni-konstanz.de/DoOR). The procedure can be adapted to other species, thus creating a family of "olfactomes" in the near future. Drosophila melanogaster was chosen because of all species this one is closest to having the complete olfactome characterized, with the highest number of deorphanized receptors available. The database guarantees long-term stability (by offering time-stamped, downloadable versions), up-to-date accuracy (by including new data sets as soon as they are published), and portability (for other species). We hope that this comprehensive repository of odor response profiles will be useful to the olfactory community and to computational neuroscientists alike.

SUBMITTER: Galizia CG

PROVIDER: S-EPMC2924422 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrating heterogeneous odor response data into a common response model: A DoOR to the complete olfactome.

Galizia C Giovanni CG Münch Daniel D Strauch Martin M Nissler Anja A Ma Shouwen S

Chemical senses 20100607 7

We have developed a new computational framework for merging odor response data sets from heterogeneous studies, creating a consensus metadatabase, the database of odor responses (DoOR). As a result, we obtained a functional atlas of all available odor responses in Drosophila melanogaster. Both the program and the data set are freely accessible and downloadable on the Internet (http://neuro.uni-konstanz.de/DoOR). The procedure can be adapted to other species, thus creating a family of "olfactomes ...[more]

PMID: 20530377

Similar Datasets

Project description:MotivationIntegrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems.ResultsWe propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling.Availability and implementationCode and data are available at https://github.com/GauravPandeyLab/ensemble_integration.Supplementary informationSupplementary data are available at Bioinformatics Advances online.

Dataset Information

Integrating heterogeneous odor response data into a common response model: A DoOR to the complete olfactome.

Publications

Integrating heterogeneous odor response data into a common response model: A DoOR to the complete olfactome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets