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Your search keyword '"Blumenthal, D.B."' showing total 4 results
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4 results on '"Blumenthal, D.B."'

1. The AIMe registry for artificial intelligence in biomedical research

Author

Matschinske, J., Matschinske, J., Alcaraz, N., Benis, A., Golebiewski, M., Grimm, D.G., Heumos, L., Kacprowski, T., Lazareva, O., List, M., Louadi, Z., Pauling, J.K., Pfeifer, N., Rottger, R., Schwammle, V., Sturm, G., Traverso, A., Van Steen, K., de Freitas, M.V., Silva, G.C.V., Wee, L., Wenke, N.K., Zanin, M., Zolotareva, O., Baumbach, J., Blumenthal, D.B., Matschinske, J., Matschinske, J., Alcaraz, N., Benis, A., Golebiewski, M., Grimm, D.G., Heumos, L., Kacprowski, T., Lazareva, O., List, M., Louadi, Z., Pauling, J.K., Pfeifer, N., Rottger, R., Schwammle, V., Sturm, G., Traverso, A., Van Steen, K., de Freitas, M.V., Silva, G.C.V., Wee, L., Wenke, N.K., Zanin, M., Zolotareva, O., Baumbach, J., and Blumenthal, D.B.

Abstract

We present the AIMe registry, a community-driven reporting platform for AI in biomedicine. It aims to enhance the accessibility, reproducibility and usability of biomedical AI models, and allows future revisions by the community.

Published
2021

2. Network medicine for disease module identification and drug repurposing with the NeDRex platform

Author

Sadegh, S., Sadegh, S., Skelton, J., Anastasi, E., Bernett, J., Blumenthal, D.B., Galindez, G., Salgado-Albarran, M., Lazareva, O., Flanagan, K., Cockell, S., Nogales, C., Casas, A.I., Schmidt, H.H.H.W., Baumbach, J., Wipat, A., Kacprowski, T., Sadegh, S., Sadegh, S., Skelton, J., Anastasi, E., Bernett, J., Blumenthal, D.B., Galindez, G., Salgado-Albarran, M., Lazareva, O., Flanagan, K., Cockell, S., Nogales, C., Casas, A.I., Schmidt, H.H.H.W., Baumbach, J., Wipat, A., and Kacprowski, T.

Abstract

Traditional drug discovery faces a severe efficacy crisis. Repurposing of registered drugs provides an alternative with lower costs and faster drug development timelines. However, the data necessary for the identification of disease modules, i.e. pathways and sub-networks describing the mechanisms of complex diseases which contain potential drug targets, are scattered across independent databases. Moreover, existing studies are limited to predictions for specific diseases or non-translational algorithmic approaches. There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. We close this gap with NeDRex, an integrative and interactive platform for network-based drug repurposing and disease module discovery. NeDRex integrates ten different data sources covering genes, drugs, drug targets, disease annotations, and their relationships. NeDRex allows for constructing heterogeneous biological networks, mining them for disease modules, prioritizing drugs targeting disease mechanisms, and statistical validation. We demonstrate the utility of NeDRex in five specific use-cases.There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. Here, the authors close this gap with NeDRex, an integrative and interactive platform.

Published
2021

3. Network medicine for disease module identification and drug repurposing with the NeDRex platform

Author

Sadegh, S., Skelton, J., Anastasi, E., Bernett, J., Blumenthal, D.B., Galindez, G., Salgado-Albarran, M., Lazareva, O., Flanagan, K., Cockell, S., Nogales, C., Casas, A.I., Schmidt, H.H.H.W., Baumbach, J., Wipat, A., Kacprowski, T., Sadegh, S., Skelton, J., Anastasi, E., Bernett, J., Blumenthal, D.B., Galindez, G., Salgado-Albarran, M., Lazareva, O., Flanagan, K., Cockell, S., Nogales, C., Casas, A.I., Schmidt, H.H.H.W., Baumbach, J., Wipat, A., and Kacprowski, T.

Abstract

Traditional drug discovery faces a severe efficacy crisis. Repurposing of registered drugs provides an alternative with lower costs and faster drug development timelines. However, the data necessary for the identification of disease modules, i.e. pathways and sub-networks describing the mechanisms of complex diseases which contain potential drug targets, are scattered across independent databases. Moreover, existing studies are limited to predictions for specific diseases or non-translational algorithmic approaches. There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. We close this gap with NeDRex, an integrative and interactive platform for network-based drug repurposing and disease module discovery. NeDRex integrates ten different data sources covering genes, drugs, drug targets, disease annotations, and their relationships. NeDRex allows for constructing heterogeneous biological networks, mining them for disease modules, prioritizing drugs targeting disease mechanisms, and statistical validation. We demonstrate the utility of NeDRex in five specific use-cases.There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. Here, the authors close this gap with NeDRex, an integrative and interactive platform.

Published
2021

4. The AIMe registry for artificial intelligence in biomedical research

Author

Matschinske, J., Alcaraz, N., Benis, A., Golebiewski, M., Grimm, D.G., Heumos, L., Kacprowski, T., Lazareva, O., List, M., Louadi, Z., Pauling, J.K., Pfeifer, N., Rottger, R., Schwammle, V., Sturm, G., Traverso, A., Van Steen, K., de Freitas, M.V., Silva, G.C.V., Wee, L., Wenke, N.K., Zanin, M., Zolotareva, O., Baumbach, J., Blumenthal, D.B., Matschinske, J., Alcaraz, N., Benis, A., Golebiewski, M., Grimm, D.G., Heumos, L., Kacprowski, T., Lazareva, O., List, M., Louadi, Z., Pauling, J.K., Pfeifer, N., Rottger, R., Schwammle, V., Sturm, G., Traverso, A., Van Steen, K., de Freitas, M.V., Silva, G.C.V., Wee, L., Wenke, N.K., Zanin, M., Zolotareva, O., Baumbach, J., and Blumenthal, D.B.

Abstract

We present the AIMe registry, a community-driven reporting platform for AI in biomedicine. It aims to enhance the accessibility, reproducibility and usability of biomedical AI models, and allows future revisions by the community.

Published
2021

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