DropLasso: A robust variant of Lasso for single cell RNA-seq data

Autor: Khalfaoui, Beyrem, Vert, Jean-Philippe

Single-cell RNA sequencing (scRNA-seq) is a fast growing approach to measure the genome-wide transcriptome of many individual cells in parallel, but results in noisy data with many dropout events. Existing methods to learn molecular signatures from b

Externí odkaz: http://arxiv.org/abs/1802.09381

DropLasso: A robust variant of Lasso for single cell RNA-seq data

Autor: Khalfaoui, Beyrem, Vert, Jean-Philippe

Single-cell RNA sequencing (scRNA-seq) is a fast growing approach to measure the genome-wide transcriptome of many individual cells in parallel, but results in noisy data with many dropout events. Existing methods to learn molecular signatures from b

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d74b4d36192a73eef07acc3609b93c8
https://hal.archives-ouvertes.fr/hal-01716704v2/document

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Autor: Sieberts, Solveig K., Zhu, Fan, García-García, Javier, Stahl, Eli, Pratap, Abhishek, Pandey, Gaurav, Pappas, Dimitrios, Aguilar, Daniel, Anton, Bernat, Bonet, Jaume, Eksi, Ridvan, Fornés, Oriol, Guney, Emre, Li, Hongdong, Marín, Manuel Alejandro, Panwar, Bharat, Planas-Iglesias, Joan, Poglayen, Daniel, Cui, Jing, Falcao, Andre O., Suver, Christine, Hoff, Bruce, Balagurusamy, Venkat S. K., Dillenberger, Donna, Neto, Elias Chaibub, Norman, Thea, Aittokallio, Tero, Ammad-ud-din, Muhammad, Azencott, Chloe-Agathe, Bellón, Víctor, Boeva, Valentina, Bunte, Kerstin, Chheda, Himanshu, Cheng, Lu, Corander, Jukka, Dumontier, Michel, Goldenberg, Anna, Gopalacharyulu, Peddinti, Hajiloo, Mohsen, Hidru, Daniel, Jaiswal, Alok, Kaski, Samuel, Khalfaoui, Beyrem, Khan, Suleiman Ali, Kramer, Eric R., Marttinen, Pekka, Mezlini, Aziz M., Molparia, Bhuvan, Pirinen, Matti, Saarela, Janna, Samwald, Matthias, Stoven, Véronique, Tang, Hao, Tang, Jing, Torkamani, Ali, Vert, Jean-Phillipe, Wang, Bo, Wang, Tao, Wennerberg, Krister, Wineinger, Nathan E., Xiao, Guanghua, Xie, Yang, Yeung, Rae, Zhan, Xiaowei, Zhao, Cheng, Calaza, Manuel, Elmarakeby, Haitham, Heath, Lenwood S., Long, Quan, Moore, Jonathan D., Opiyo, Stephen Obol, Savage, Richard S., Zhu, Jun, Greenberg, Jeff, Kremer, Joel, Michaud, Kaleb, Barton, Anne, Coenen, Marieke, Mariette, Xavier, Miceli, Corinne, Shadick, Nancy, Weinblatt, Michael, de Vries, Niek, Tak, Paul P., Gerlag, Danielle, Huizinga, Tom W. J., Kurreeman, Fina, Allaart, Cornelia F., Louis Bridges Jr., S., Criswell, Lindsey, Moreland, Larry, Klareskog, Lars, Saevarsdottir, Saedis, Padyukov, Leonid, Gregersen, Peter K., Friend, Stephen, Plenge, Robert, Stolovitzky, Gustavo, Oliva, Baldo, Guan, Yuanfang, Mangravite, Lara M., Bridges, S. Louis

Publikováno v: Nature Communications, 7:12460 (2016). Nature Publishing Group
Recercat. Dipósit de la Recerca de Catalunya
instname
Nature communications, 7. Nature Publishing Group
Nature Communications
Nature Communications, 2016, 7, pp.12460. ⟨10.1038/ncomms12460⟩
Nature Communications, 7:12460. Nature Publishing Group
Nature Communications, Nature Publishing Group, 2016, 7, pp.12460. ⟨10.1038/ncomms12460⟩
Nature Communications, Vol 7, Iss 1, Pp 1-10 (2016)
Nature Communications, 7
Sieberts, S K, Zhu, F, García-García, J, Stahl, E, Pratap, A, Pandey, G, Pappas, D, Aguilar, D, Anton, B, Bonet, J, Eksi, R, Fornés, O, Guney, E, Li, H, Marín, M A, Panwar, B, Planas-Iglesias, J, Poglayen, D, Cui, J, Falcao, A O, Suver, C, Hoff, B, Balagurusamy, V S K, Dillenberger, D, Neto, E C, Norman, T, Aittokallio, T, Ammad-Ud-Din, M, Azencott, C A, Bellón, V, Boeva, V, Bunte, K, Chheda, H, Cheng, L, Corander, J, Dumontier, M, Goldenberg, A, Gopalacharyulu, P, Hajiloo, M, Hidru, D, Jaiswal, A, Kaski, S, Khalfaoui, B, Khan, S A, Kramer, E R, Marttinen, P, Mezlini, A M, Molparia, B, Pirinen, M, Saarela, J, Samwald, M, Stoven, V, Tang, H, Tang, J, Torkamani, A, Vert, J P, Wang, B, Wang, T, Wennerberg, K, Wineinger, N E, Xiao, G, Xie, Y, Yeung, R, Zhan, X, Zhao, C, Greenberg, J, Kremer, J, Michaud, K, Barton, A, Coenen, M, Mariette, X, Miceli, C, Shadick, N, Weinblatt, M, De Vries, N, Tak, P P, Gerlag, D, Huizinga, T W J, Kurreeman, F, Allaart, C F, Louis Bridges, S, Criswell, L, Moreland, L, Klareskog, L, Saevarsdottir, S, Padyukov, L, Gregersen, P K, Friend, S, Plenge, R, Stolovitzky, G, Oliva, B, Guan, Y & Mangravite, L M 2016, ' Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis ', Nature Communications, vol. 7, 12460 . https://doi.org/10.1038/ncomms12460

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5fe800839878ab5e485119caf3f56464
http://juuli.fi/Record/0274668216