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Study of Meta-analysis strategies for network inference using information-theoretic approaches.

BioData mining

Pham NC, Haibe-Kains B, Bellot P, Bontempi G, Meyer PE.
PMID: 28484519
BioData Min. 2017 May 06;10:15. doi: 10.1186/s13040-017-0136-6. eCollection 2017.

BACKGROUND: Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from...

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Pham NC, Haibe-Kains B, Bellot P, et al. Study of Meta-analysis strategies for network inference using information-theoretic approaches. BioData Min. 2017;10:15doi: 10.1186/s13040-017-0136-6.
Pham, N. C., Haibe-Kains, B., Bellot, P., Bontempi, G., & Meyer, P. E. (2017). Study of Meta-analysis strategies for network inference using information-theoretic approaches. BioData mining, 1015. https://doi.org/10.1186/s13040-017-0136-6
Pham, Ngoc C, et al. "Study of Meta-analysis strategies for network inference using information-theoretic approaches." BioData mining vol. 10 (2017): 15. doi: https://doi.org/10.1186/s13040-017-0136-6
Pham NC, Haibe-Kains B, Bellot P, Bontempi G, Meyer PE. Study of Meta-analysis strategies for network inference using information-theoretic approaches. BioData Min. 2017 May 06;10:15. doi: 10.1186/s13040-017-0136-6. eCollection 2017. PMID: 28484519; PMCID: PMC5420410.
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Using shRNA experiments to validate gene regulatory networks.

Genomics data

Olsen C, Fleming K, Prendergast N, Rubio R, Emmert-Streib F, Bontempi G, Quackenbush J, Haibe-Kains B.
PMID: 26484195
Genom Data. 2015 Apr 01;4:123-6. doi: 10.1016/j.gdata.2015.03.011. eCollection 2015 Jun.

Quantitative validation of gene regulatory networks (GRNs) inferred from observational expression data is a difficult task usually involving time intensive and costly laboratory experiments. We were able to show that gene knock-down experiments can be used to quantitatively assess...

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Olsen C, Fleming K, Prendergast N, et al. Using shRNA experiments to validate gene regulatory networks. Genom Data. 2015;4:123-6doi: 10.1016/j.gdata.2015.03.011.
Olsen, C., Fleming, K., Prendergast, N., Rubio, R., Emmert-Streib, F., Bontempi, G., Quackenbush, J., & Haibe-Kains, B. (2015). Using shRNA experiments to validate gene regulatory networks. Genomics data, 4123-6. https://doi.org/10.1016/j.gdata.2015.03.011
Olsen, Catharina, et al. "Using shRNA experiments to validate gene regulatory networks." Genomics data vol. 4 (2015): 123-6. doi: https://doi.org/10.1016/j.gdata.2015.03.011
Olsen C, Fleming K, Prendergast N, Rubio R, Emmert-Streib F, Bontempi G, Quackenbush J, Haibe-Kains B. Using shRNA experiments to validate gene regulatory networks. Genom Data. 2015 Apr 01;4:123-6. doi: 10.1016/j.gdata.2015.03.011. eCollection 2015 Jun. PMID: 26484195; PMCID: PMC4535466.
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Revisiting inconsistency in large pharmacogenomic studies.

F1000Research

Safikhani Z, Smirnov P, Freeman M, El-Hachem N, She A, Rene Q, Goldenberg A, Birkbak NJ, Hatzis C, Shi L, Beck AH, Aerts HJWL, Quackenbush J, Haibe-Kains B.
PMID: 28928933
F1000Res. 2016 Sep 16;5:2333. doi: 10.12688/f1000research.9611.3. eCollection 2016.

In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer...

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Safikhani Z, Smirnov P, Freeman M, et al. Revisiting inconsistency in large pharmacogenomic studies. F1000Res. 2016;5:2333doi: 10.12688/f1000research.9611.3.
Safikhani, Z., Smirnov, P., Freeman, M., El-Hachem, N., She, A., Rene, Q., Goldenberg, A., Birkbak, N. J., Hatzis, C., Shi, L., Beck, A. H., Aerts, H. J. W. L., Quackenbush, J., & Haibe-Kains, B. (2016). Revisiting inconsistency in large pharmacogenomic studies. F1000Research, 52333. https://doi.org/10.12688/f1000research.9611.3
Safikhani, Zhaleh, et al. "Revisiting inconsistency in large pharmacogenomic studies." F1000Research vol. 5 (2016): 2333. doi: https://doi.org/10.12688/f1000research.9611.3
Safikhani Z, Smirnov P, Freeman M, El-Hachem N, She A, Rene Q, Goldenberg A, Birkbak NJ, Hatzis C, Shi L, Beck AH, Aerts HJWL, Quackenbush J, Haibe-Kains B. Revisiting inconsistency in large pharmacogenomic studies. F1000Res. 2016 Sep 16;5:2333. doi: 10.12688/f1000research.9611.3. eCollection 2016. PMID: 28928933; PMCID: PMC5580432.
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Assessment of pharmacogenomic agreement.

F1000Research

Safikhani Z, El-Hachem N, Quevedo R, Smirnov P, Goldenberg A, Juul Birkbak N, Mason C, Hatzis C, Shi L, Aerts HJ, Quackenbush J, Haibe-Kains B.
PMID: 27408686
F1000Res. 2016 May 09;5:825. doi: 10.12688/f1000research.8705.1. eCollection 2016.

In 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE), were inconsistent. The GDSC and...

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Safikhani Z, El-Hachem N, Quevedo R, et al. Assessment of pharmacogenomic agreement. F1000Res. 2016;5:825doi: 10.12688/f1000research.8705.1.
Safikhani, Z., El-Hachem, N., Quevedo, R., Smirnov, P., Goldenberg, A., Juul Birkbak, N., Mason, C., Hatzis, C., Shi, L., Aerts, H. J., Quackenbush, J., & Haibe-Kains, B. (2016). Assessment of pharmacogenomic agreement. F1000Research, 5825. https://doi.org/10.12688/f1000research.8705.1
Safikhani, Zhaleh, et al. "Assessment of pharmacogenomic agreement." F1000Research vol. 5 (2016): 825. doi: https://doi.org/10.12688/f1000research.8705.1
Safikhani Z, El-Hachem N, Quevedo R, Smirnov P, Goldenberg A, Juul Birkbak N, Mason C, Hatzis C, Shi L, Aerts HJ, Quackenbush J, Haibe-Kains B. Assessment of pharmacogenomic agreement. F1000Res. 2016 May 09;5:825. doi: 10.12688/f1000research.8705.1. eCollection 2016. PMID: 27408686; PMCID: PMC4926729.
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MicroRNA paraffin-based studies in osteosarcoma reveal reproducible independent prognostic profiles at 14q32.

Genome medicine

Kelly AD, Haibe-Kains B, Janeway KA, Hill KE, Howe E, Goldsmith J, Kurek K, Perez-Atayde AR, Francoeur N, Fan JB, April C, Schneider H, Gebhardt MC, Culhane A, Quackenbush J, Spentzos D.
PMID: 23339462
Genome Med. 2013 Jan 22;5(1):2. doi: 10.1186/gm406. eCollection 2013.

BACKGROUND: Although microRNAs (miRNAs) are implicated in osteosarcoma biology and chemoresponse, miRNA prognostic models are still needed, particularly because prognosis is imperfectly correlated with chemoresponse. Formalin-fixed, paraffin-embedded tissue is a necessary resource for biomarker studies in this malignancy with...

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Kelly AD, Haibe-Kains B, Janeway KA, et al. MicroRNA paraffin-based studies in osteosarcoma reveal reproducible independent prognostic profiles at 14q32. Genome Med. 2013;5(1):2doi: 10.1186/gm406.
Kelly, A. D., Haibe-Kains, B., Janeway, K. A., Hill, K. E., Howe, E., Goldsmith, J., Kurek, K., Perez-Atayde, A. R., Francoeur, N., Fan, J. B., April, C., Schneider, H., Gebhardt, M. C., Culhane, A., Quackenbush, J., & Spentzos, D. (2013). MicroRNA paraffin-based studies in osteosarcoma reveal reproducible independent prognostic profiles at 14q32. Genome medicine, 5(1), 2. https://doi.org/10.1186/gm406
Kelly, Andrew D, et al. "MicroRNA paraffin-based studies in osteosarcoma reveal reproducible independent prognostic profiles at 14q32." Genome medicine vol. 5,1 (2013): 2. doi: https://doi.org/10.1186/gm406
Kelly AD, Haibe-Kains B, Janeway KA, Hill KE, Howe E, Goldsmith J, Kurek K, Perez-Atayde AR, Francoeur N, Fan JB, April C, Schneider H, Gebhardt MC, Culhane A, Quackenbush J, Spentzos D. MicroRNA paraffin-based studies in osteosarcoma reveal reproducible independent prognostic profiles at 14q32. Genome Med. 2013 Jan 22;5(1):2. doi: 10.1186/gm406. eCollection 2013. PMID: 23339462; PMCID: PMC3706900.
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Author Correction: Gene isoforms as expression-based biomarkers predictive of drug response in vitro.

Nature communications

Safikhani Z, Smirnov P, Thu KL, Silvester J, El-Hachem N, Quevedo R, Lupien M, Mak TW, Cescon D, Haibe-Kains B.
PMID: 29317617
Nat Commun. 2018 Jan 09;9(1):166. doi: 10.1038/s41467-017-02136-5.

In the original version of this Article, financial support was not fully acknowledged. This error has now been corrected in both the PDF and HTML versions of the Article.

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Safikhani Z, Smirnov P, Thu KL, et al. Author Correction: Gene isoforms as expression-based biomarkers predictive of drug response in vitro. Nat Commun. 2018;9(1):166doi: 10.1038/s41467-017-02136-5.
Safikhani, Z., Smirnov, P., Thu, K. L., Silvester, J., El-Hachem, N., Quevedo, R., Lupien, M., Mak, T. W., Cescon, D., & Haibe-Kains, B. (2018). Author Correction: Gene isoforms as expression-based biomarkers predictive of drug response in vitro. Nature communications, 9(1), 166. https://doi.org/10.1038/s41467-017-02136-5
Safikhani, Zhaleh, et al. "Author Correction: Gene isoforms as expression-based biomarkers predictive of drug response in vitro." Nature communications vol. 9,1 (2018): 166. doi: https://doi.org/10.1038/s41467-017-02136-5
Safikhani Z, Smirnov P, Thu KL, Silvester J, El-Hachem N, Quevedo R, Lupien M, Mak TW, Cescon D, Haibe-Kains B. Author Correction: Gene isoforms as expression-based biomarkers predictive of drug response in vitro. Nat Commun. 2018 Jan 09;9(1):166. doi: 10.1038/s41467-017-02136-5. PMID: 29317617; PMCID: PMC5760689.
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Artificial intelligence for drug response prediction in disease models.

Briefings in bioinformatics

Ballester PJ, Stevens R, Haibe-Kains B, Huang RS, Aittokallio T.
PMID: 34655289
Brief Bioinform. 2021 Oct 15; doi: 10.1093/bib/bbab450. Epub 2021 Oct 15.

No abstract available.

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Ballester PJ, Stevens R, Haibe-Kains B, et al. Artificial intelligence for drug response prediction in disease models. Brief Bioinform. 2021;doi: 10.1093/bib/bbab450.
Ballester, P. J., Stevens, R., Haibe-Kains, B., Huang, R. S., & Aittokallio, T. (2021). Artificial intelligence for drug response prediction in disease models. Briefings in bioinformatics, . https://doi.org/10.1093/bib/bbab450
Ballester, Pedro J, et al. "Artificial intelligence for drug response prediction in disease models." Briefings in bioinformatics vol. (2021). doi: https://doi.org/10.1093/bib/bbab450
Ballester PJ, Stevens R, Haibe-Kains B, Huang RS, Aittokallio T. Artificial intelligence for drug response prediction in disease models. Brief Bioinform. 2021 Oct 15; doi: 10.1093/bib/bbab450. Epub 2021 Oct 15. PMID: 34655289.
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Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer.

Cancer discovery

Chang CA, Jen J, Jiang S, Sayad A, Mer AS, Brown KR, Nixon AML, Dhabaria A, Tang KH, Venet D, Sotiriou C, Deng J, Wong KK, Adams S, Meyn P, Heguy A, Skok JA, Tsirigos A, Ueberheide B, Moffat J, Singh A, Haibe-Kains B, Khodadadi-Jamayran A, Neel BG.
PMID: 34911733
Cancer Discov. 2021 Dec 15; doi: 10.1158/2159-8290.CD-20-1265. Epub 2021 Dec 15.

Resistance to targeted therapies is an important clinical problem in HER2-positive (HER2+) breast cancer. "Drug-tolerant persisters" (DTPs), a sub-population of cancer cells that survive via reversible, non-genetic mechanisms, are implicated in resistance to tyrosine kinase inhibitors (TKIs) in other...

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Chang CA, Jen J, Jiang S, et al. Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer. Cancer Discov. 2021;doi: 10.1158/2159-8290.CD-20-1265.
Chang, C. A., Jen, J., Jiang, S., Sayad, A., Mer, A. S., Brown, K. R., Nixon, A. M. L., Dhabaria, A., Tang, K. H., Venet, D., Sotiriou, C., Deng, J., Wong, K. K., Adams, S., Meyn, P., Heguy, A., Skok, J. A., Tsirigos, A., Ueberheide, B., Moffat, J., Singh, A., Haibe-Kains, B., Khodadadi-Jamayran, A., & Neel, B. G. (2021). Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer. Cancer discovery, . https://doi.org/10.1158/2159-8290.CD-20-1265
Chang, Chewei Anderson, et al. "Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer." Cancer discovery vol. (2021). doi: https://doi.org/10.1158/2159-8290.CD-20-1265
Chang CA, Jen J, Jiang S, Sayad A, Mer AS, Brown KR, Nixon AML, Dhabaria A, Tang KH, Venet D, Sotiriou C, Deng J, Wong KK, Adams S, Meyn P, Heguy A, Skok JA, Tsirigos A, Ueberheide B, Moffat J, Singh A, Haibe-Kains B, Khodadadi-Jamayran A, Neel BG. Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer. Cancer Discov. 2021 Dec 15; doi: 10.1158/2159-8290.CD-20-1265. Epub 2021 Dec 15. PMID: 34911733.
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Gene Expression Analyses in Breast Cancer: Sample Matters.

JNCI cancer spectrum

Haibe-Kains B, Cescon DW.
PMID: 31360851
JNCI Cancer Spectr. 2018 May 22;2(2):pky019. doi: 10.1093/jncics/pky019. eCollection 2018 Apr.

No abstract available.

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Haibe-Kains B, Cescon DW. Gene Expression Analyses in Breast Cancer: Sample Matters. JNCI Cancer Spectr. 2018;2(2):pky019doi: 10.1093/jncics/pky019.
Haibe-Kains, B., & Cescon, D. W. (2018). Gene Expression Analyses in Breast Cancer: Sample Matters. JNCI cancer spectrum, 2(2), pky019. https://doi.org/10.1093/jncics/pky019
Haibe-Kains, Benjamin, and Cescon, David W. "Gene Expression Analyses in Breast Cancer: Sample Matters." JNCI cancer spectrum vol. 2,2 (2018): pky019. doi: https://doi.org/10.1093/jncics/pky019
Haibe-Kains B, Cescon DW. Gene Expression Analyses in Breast Cancer: Sample Matters. JNCI Cancer Spectr. 2018 May 22;2(2):pky019. doi: 10.1093/jncics/pky019. eCollection 2018 Apr. PMID: 31360851; PMCID: PMC6649719.
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Safikhani et al. reply.

Nature

Safikhani Z, El-Hachem N, Smirnov P, Freeman M, Goldenberg A, Birkbak NJ, Beck AH, Aerts HJ, Quackenbush J, Haibe-Kains B.
PMID: 27905416
Nature. 2016 Nov 30;540(7631):E6-E8. doi: 10.1038/nature20172.

No abstract available.

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Safikhani Z, El-Hachem N, Smirnov P, et al. Safikhani et al. reply. Nature. 2016;540(7631):E6-E8doi: 10.1038/nature20172.
Safikhani, Z., El-Hachem, N., Smirnov, P., Freeman, M., Goldenberg, A., Birkbak, N. J., Beck, A. H., Aerts, H. J., Quackenbush, J., & Haibe-Kains, B. (2016). Safikhani et al. reply. Nature, 540(7631), E6-E8. https://doi.org/10.1038/nature20172
Safikhani, Zhaleh, et al. "Safikhani et al. reply." Nature vol. 540,7631 (2016): E6-E8. doi: https://doi.org/10.1038/nature20172
Safikhani Z, El-Hachem N, Smirnov P, Freeman M, Goldenberg A, Birkbak NJ, Beck AH, Aerts HJ, Quackenbush J, Haibe-Kains B. Safikhani et al. reply. Nature. 2016 Nov 30;540(7631):E6-E8. doi: 10.1038/nature20172. PMID: 27905416.
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Reporting guidelines for human microbiome research: the STORMS checklist.

Nature medicine

Mirzayi C, Renson A, Zohra F, Elsafoury S, Geistlinger L, Kasselman LJ, Eckenrode K, van de Wijgert J, Loughman A, Marques FZ, MacIntyre DA, Arumugam M, Azhar R, Beghini F, Bergstrom K, Bhatt A, Bisanz JE, Braun J, Bravo HC, Buck GA, Bushman F, Casero D, Clarke G, Collado MC, Cotter PD, Cryan JF, Demmer RT, Devkota S, Elinav E, Escobar JS, Fettweis J, Finn RD, Fodor AA, Forslund S, Franke A, Furlanello C, Gilbert J, Grice E, Haibe-Kains B, Handley S, Herd P, Holmes S, Jacobs JP, Karstens L, Knight R, Knights D, Koren O, Kwon DS, Langille M, Lindsay B, McGovern D, McHardy AC, McWeeney S, Mueller NT, Nezi L, Olm M, Palm N, Pasolli E, Raes J, Redinbo MR, Rühlemann M, Balfour Sartor R, Schloss PD, Schriml L, Segal E, Shardell M, Sharpton T, Smirnova E, Sokol H, Sonnenburg JL, Srinivasan S, Thingholm LB, Turnbaugh PJ, Upadhyay V, Walls RL, Wilmes P, Yamada T, Zeller G, Zhang M, Zhao N, Zhao L, Bao W, Culhane A, Devanarayan V, Dopazo J, Fan X, Fischer M, Jones W, Kusko R, Mason CE, Mercer TR, Sansone SA, Scherer A, Shi L, Thakkar S, Tong W, Wolfinger R, Hunter C, Segata N, Huttenhower C, Dowd JB, Jones HE, Waldron L.
PMID: 34789871
Nat Med. 2021 Nov;27(11):1885-1892. doi: 10.1038/s41591-021-01552-x. Epub 2021 Nov 17.

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific...

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Mirzayi C, Renson A. Reporting guidelines for human microbiome research: the STORMS checklist. Nat Med. 2021;27(11):1885-1892doi: 10.1038/s41591-021-01552-x.
Mirzayi, C., Renson, A., Zohra, F., Elsafoury, S., Geistlinger, L., Kasselman, L. J., Eckenrode, K., van de Wijgert, J., Loughman, A., Marques, F. Z., MacIntyre, D. A., Arumugam, M., Azhar, R., Beghini, F., Bergstrom, K., Bhatt, A., Bisanz, J. E., Braun, J., Bravo, H. C., Buck, G. A., Bushman, F., Casero, D., Clarke, G., Collado, M. C., Cotter, P. D., Cryan, J. F., Demmer, R. T., Devkota, S., Elinav, E., Escobar, J. S., Fettweis, J., Finn, R. D., Fodor, A. A., Forslund, S., Franke, A., Furlanello, C., Gilbert, J., Grice, E., Haibe-Kains, B., Handley, S., Herd, P., Holmes, S., Jacobs, J. P., Karstens, L., Knight, R., Knights, D., Koren, O., Kwon, D. S., Langille, M., Lindsay, B., McGovern, D., McHardy, A. C., McWeeney, S., Mueller, N. T., Nezi, L., Olm, M., Palm, N., Pasolli, E., Raes, J., Redinbo, M. R., Rühlemann, M., Balfour Sartor, R., Schloss, P. D., Schriml, L., Segal, E., Shardell, M., Sharpton, T., Smirnova, E., Sokol, H., Sonnenburg, J. L., Srinivasan, S., Thingholm, L. B., Turnbaugh, P. J., Upadhyay, V., Walls, R. L., Wilmes, P., Yamada, T., Zeller, G., Zhang, M., Zhao, N., Zhao, L., Bao, W., Culhane, A., Devanarayan, V., Dopazo, J., Fan, X., Fischer, M., Jones, W., Kusko, R., Mason, C. E., Mercer, T. R., Sansone, S. A., Scherer, A., Shi, L., Thakkar, S., Tong, W., Wolfinger, R., Hunter, C., Segata, N., Huttenhower, C., Dowd, J. B., Jones, H. E., & Waldron, L. (2021). Reporting guidelines for human microbiome research: the STORMS checklist. Nature medicine, 27(11), 1885-1892. https://doi.org/10.1038/s41591-021-01552-x
Mirzayi, Chloe, et al. "Reporting guidelines for human microbiome research: the STORMS checklist." Nature medicine vol. 27,11 (2021): 1885-1892. doi: https://doi.org/10.1038/s41591-021-01552-x
Mirzayi C, Renson A, Zohra F, Elsafoury S, Geistlinger L, Kasselman LJ, Eckenrode K, van de Wijgert J, Loughman A, Marques FZ, MacIntyre DA, Arumugam M, Azhar R, Beghini F, Bergstrom K, Bhatt A, Bisanz JE, Braun J, Bravo HC, Buck GA, Bushman F, Casero D, Clarke G, Collado MC, Cotter PD, Cryan JF, Demmer RT, Devkota S, Elinav E, Escobar JS, Fettweis J, Finn RD, Fodor AA, Forslund S, Franke A, Furlanello C, Gilbert J, Grice E, Haibe-Kains B, Handley S, Herd P, Holmes S, Jacobs JP, Karstens L, Knight R, Knights D, Koren O, Kwon DS, Langille M, Lindsay B, McGovern D, McHardy AC, McWeeney S, Mueller NT, Nezi L, Olm M, Palm N, Pasolli E, Raes J, Redinbo MR, Rühlemann M, Balfour Sartor R, Schloss PD, Schriml L, Segal E, Shardell M, Sharpton T, Smirnova E, Sokol H, Sonnenburg JL, Srinivasan S, Thingholm LB, Turnbaugh PJ, Upadhyay V, Walls RL, Wilmes P, Yamada T, Zeller G, Zhang M, Zhao N, Zhao L, Bao W, Culhane A, Devanarayan V, Dopazo J, Fan X, Fischer M, Jones W, Kusko R, Mason CE, Mercer TR, Sansone SA, Scherer A, Shi L, Thakkar S, Tong W, Wolfinger R, Hunter C, Segata N, Huttenhower C, Dowd JB, Jones HE, Waldron L. Reporting guidelines for human microbiome research: the STORMS checklist. Nat Med. 2021 Nov;27(11):1885-1892. doi: 10.1038/s41591-021-01552-x. Epub 2021 Nov 17. PMID: 34789871.
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Creating reproducible pharmacogenomic analysis pipelines.

Scientific data

Mammoliti A, Smirnov P, Safikhani Z, Ba-Alawi W, Haibe-Kains B.
PMID: 31481707
Sci Data. 2019 Sep 03;6(1):166. doi: 10.1038/s41597-019-0174-7.

The field of pharmacogenomics presents great challenges for researchers that are willing to make their studies reproducible and shareable. This is attributed to the generation of large volumes of high-throughput multimodal data, and the lack of standardized workflows that...

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Mammoliti A, Smirnov P, Safikhani Z, et al. Creating reproducible pharmacogenomic analysis pipelines. Sci Data. 2019;6(1):166doi: 10.1038/s41597-019-0174-7.
Mammoliti, A., Smirnov, P., Safikhani, Z., Ba-Alawi, W., & Haibe-Kains, B. (2019). Creating reproducible pharmacogenomic analysis pipelines. Scientific data, 6(1), 166. https://doi.org/10.1038/s41597-019-0174-7
Mammoliti, Anthony, et al. "Creating reproducible pharmacogenomic analysis pipelines." Scientific data vol. 6,1 (2019): 166. doi: https://doi.org/10.1038/s41597-019-0174-7
Mammoliti A, Smirnov P, Safikhani Z, Ba-Alawi W, Haibe-Kains B. Creating reproducible pharmacogenomic analysis pipelines. Sci Data. 2019 Sep 03;6(1):166. doi: 10.1038/s41597-019-0174-7. PMID: 31481707; PMCID: PMC6722117.
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Showing 1 to 12 of 53 entries