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Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine.

Database : the journal of biological databases and curation

Islamaj Dogan R, Kim S, Chatr-Aryamontri A, Wei CH, Comeau DC, Antunes R, Matos S, Chen Q, Elangovan A, Panyam NC, Verspoor K, Liu H, Wang Y, Liu Z, Altinel B, Hüsünbeyi ZM, Özgür A, Fergadis A, Wang CK, Dai HJ, Tran T, Kavuluru R, Luo L, Steppi A, Zhang J, Qu J, Lu Z.
PMID: 30689846
Database (Oxford). 2019 Jan 01;2019. doi: 10.1093/database/bay147.

The Precision Medicine Initiative is a multicenter effort aiming at formulating personalized treatments leveraging on individual patient data (clinical, genome sequence and functional genomic data) together with the information in large knowledge bases (KBs) that integrate genome annotation, disease...

Cite
Islamaj Dogan R, Kim S, Chatr-Aryamontri A, et al. Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine. Database (Oxford). 2019;2019doi: 10.1093/database/bay147.
Islamaj Dogan, R., Kim, S., Chatr-Aryamontri, A., Wei, C. H., Comeau, D. C., Antunes, R., Matos, S., Chen, Q., Elangovan, A., Panyam, N. C., Verspoor, K., Liu, H., Wang, Y., Liu, Z., Altinel, B., Hüsünbeyi, Z. M., Özgür, A., Fergadis, A., Wang, C. K., Dai, H. J., Tran, T., Kavuluru, R., Luo, L., Steppi, A., Zhang, J., Qu, J., & Lu, Z. (2019). Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine. Database : the journal of biological databases and curation, 2019. https://doi.org/10.1093/database/bay147
Islamaj Dogan, Rezarta, et al. "Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine." Database : the journal of biological databases and curation vol. 2019 (2019). doi: https://doi.org/10.1093/database/bay147
Islamaj Dogan R, Kim S, Chatr-Aryamontri A, Wei CH, Comeau DC, Antunes R, Matos S, Chen Q, Elangovan A, Panyam NC, Verspoor K, Liu H, Wang Y, Liu Z, Altinel B, Hüsünbeyi ZM, Özgür A, Fergadis A, Wang CK, Dai HJ, Tran T, Kavuluru R, Luo L, Steppi A, Zhang J, Qu J, Lu Z. Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine. Database (Oxford). 2019 Jan 01;2019. doi: 10.1093/database/bay147. PMID: 30689846; PMCID: PMC6348314.
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Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning.

Cell systems

Wildenhain J, Spitzer M, Dolma S, Jarvik N, White R, Roy M, Griffiths E, Bellows DS, Wright GD, Tyers M.
PMID: 27136353
Cell Syst. 2015 Dec 23;1(6):383-95. doi: 10.1016/j.cels.2015.12.003. Epub 2015 Dec 23.

The structure of genetic interaction networks predicts that, analogous to synthetic lethal interactions between non-essential genes, combinations of compounds with latent activities may exhibit potent synergism. To test this hypothesis, we generated a chemical-genetic matrix of 195 diverse yeast...

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Wildenhain J, Spitzer M, Dolma S, et al. Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning. Cell Syst. 2015;1(6):383-95doi: 10.1016/j.cels.2015.12.003.
Wildenhain, J., Spitzer, M., Dolma, S., Jarvik, N., White, R., Roy, M., Griffiths, E., Bellows, D. S., Wright, G. D., & Tyers, M. (2015). Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning. Cell systems, 1(6), 383-95. https://doi.org/10.1016/j.cels.2015.12.003
Wildenhain, Jan, et al. "Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning." Cell systems vol. 1,6 (2015): 383-95. doi: https://doi.org/10.1016/j.cels.2015.12.003
Wildenhain J, Spitzer M, Dolma S, Jarvik N, White R, Roy M, Griffiths E, Bellows DS, Wright GD, Tyers M. Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning. Cell Syst. 2015 Dec 23;1(6):383-95. doi: 10.1016/j.cels.2015.12.003. Epub 2015 Dec 23. PMID: 27136353; PMCID: PMC5998823.
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