10 results on '"Milani, Emanuela S"'
Number of results to display per page
Search Results
2. Establishing standardized immune phenotyping of metastatic melanoma by digital pathology
- Author
-
Sobottka, Bettina, Nowak, Marta, Frei, Anja Laura, Haberecker, Martina, Merki, Samuel, Levesque, Mitchell P., Dummer, Reinhard, Moch, Holger, Koelzer, Viktor Hendrik, Aebersold, Rudolf, Ak, Melike, Al-Quaddoomi, Faisal S., Albinus, Jonas, Alborelli, Ilaria, Andani, Sonali, Attinger, Per-Olof, Bacac, Marina, Baumhoer, Daniel, Beck-Schimmer, Beatrice, Beerenwinkel, Niko, Beisel, Christian, Bernasconi, Lara, Bertolini, Anne, Bodenmiller, Bernd, Bonilla, Ximena, Casanova, Ruben, Chevrier, Stéphane, Chicherova, Natalia, D'Costa, Maya, Danenberg, Esther, Davidson, Natalie, Drăganmoch, Monica-Andreea, Engler, Stefanie, Erkens, Martin, Eschbach, Katja, Esposito, Cinzia, Fedier, André, Ferreira, Pedro, Ficek, Joanna, Frey, Bruno, Goetze, Sandra, Grob, Linda, Gut, Gabriele, Günther, Detlef, Haeuptle, Pirmin, Heinzelmann-Schwarz, Viola, Herter, Sylvia, Holtackers, Rene, Huesser, Tamara, Irmisch, Anja, Jacob, Francis, Jacobs, Andrea, Jaeger, Tim M., Jahn, Katharina, James, Alva R., Jermann, Philip M., Kahles, André, Kahraman, Abdullah, Kuebler, Werner, Kuipers, Jack, Kunze, Christian P., Kurzeder, Christian, Lehmann, Kjong-Van, Lugert, Sebastian, Maass, Gerd, Manz, Markus G., Markolin, Philipp, Mena, Julien, Menzel, Ulrike, Metzler, Julian M., Miglino, Nicola, Milani, Emanuela S., Muenst, Simone, Murri, Riccardo, Ng, Charlotte K.Y., Nicolet, Stefan, Pedrioli, Patrick G.A., Pelkmans, Lucas, Piscuoglio, Salvatore, Prummer, Michael, Ritter, Mathilde, Rommel, Christian, Rosano-González, María L., Rätsch, Gunnar, Santacroce, Natascha, del Castillo, Jacobo Sarabia, Schlenker, Ramona, Schwalie, Petra C., Schwan, Severin, Schär, Tobias, Senti, Gabriela, Singer, Franziska, Sivapatham, Sujana, Snijder, Berend, Sreedharan, Vipin T., Stark, Stefan, Stekhoven, Daniel J., Theocharides, Alexandre P.A., Thomas, Tinu M., Tolnay, Markus, Tosevski, Vinko, Toussaint, Nora C., Tuncel, Mustafa A., Tusup, Marina, Van Drogen, Audrey, Vetter, Marcus, Vlajnic, Tatjana, Weber, Sandra, Weber, Walter P., Wegmann, Rebekka, Weller, Michael, Wendt, Fabian, Wey, Norbert, Wicki, Andreas, Wildschut, Mattheus HE, Wollscheid, Bernd, Yu, Shuqing, Ziegler, Johanna, Zimmermann, Marc, Zoche, Martin, and Zuend, Gregor
- Published
- 2021
- Full Text
- View/download PDF
3. Probabilistic pathway-based multimodal factor analysis.
- Author
-
Immer, Alexander, Stark, Stefan G, Jacob, Francis, Bonilla, Ximena, Thomas, Tinu, Kahles, André, Goetze, Sandra, Milani, Emanuela S, Wollscheid, Bernd, Consortium, The Tumor Profiler, Rätsch, Gunnar, and Lehmann, Kjong-Van
- Subjects
FACTOR analysis ,MOLECULAR biology ,RESEARCH questions ,TRANSCRIPTOMES ,SAMPLE size (Statistics) - Abstract
Motivation Multimodal profiling strategies promise to produce more informative insights into biomedical cohorts via the integration of the information each modality contributes. To perform this integration, however, the development of novel analytical strategies is needed. Multimodal profiling strategies often come at the expense of lower sample numbers, which can challenge methods to uncover shared signals across a cohort. Thus, factor analysis approaches are commonly used for the analysis of high-dimensional data in molecular biology, however, they typically do not yield representations that are directly interpretable, whereas many research questions often center around the analysis of pathways associated with specific observations. Results We develop PathFA, a novel approach for multimodal factor analysis over the space of pathways. PathFA produces integrative and interpretable views across multimodal profiling technologies, which allow for the derivation of concrete hypotheses. PathFA combines a pathway-learning approach with integrative multimodal capability under a Bayesian procedure that is efficient, hyper-parameter free, and able to automatically infer observation noise from the data. We demonstrate strong performance on small sample sizes within our simulation framework and on matched proteomics and transcriptomics profiles from real tumor samples taken from the Swiss Tumor Profiler consortium. On a subcohort of melanoma patients, PathFA recovers pathway activity that has been independently associated with poor outcome. We further demonstrate the ability of this approach to identify pathways associated with the presence of specific cell-types as well as tumor heterogeneity. Our results show that we capture known biology, making it well suited for analyzing multimodal sample cohorts. Availability and implementation The tool is implemented in python and available at https://github.com/ratschlab/path-fa [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
4. SCIM: universal single-cell matching with unpaired feature sets
- Author
-
Stark, Stefan G, Ficek, Joanna, Locatello, Francesco, Bonilla, Ximena, Chevrier, Stéphane, Singer, Franziska, Aebersold, Rudolf, Al-Quaddoomi, Faisal S, Albinus, Jonas, Alborelli, Ilaria, Andani, Sonali, Attinger, Per-Olof, Bacac, Marina, Baumhoer, Daniel, Beck-Schimmer, Beatrice, Beerenwinkel, Niko, Beisel, Christian, Bernasconi, Lara, Bertolini, Anne, Bodenmiller, Bernd, Casanova, Ruben, Chicherova, Natalia, D'Costa, Maya, Danenberg, Esther, Davidson, Natalie, gan, Monica-Andreea Dră, Dummer, Reinhard, Engler, Stefanie, Erkens, Martin, Eschbach, Katja, Esposito, Cinzia, Fedier, André, Ferreira, Pedro, Frei, Anja L, Frey, Bruno, Goetze, Sandra, Grob, Linda, Gut, Gabriele, Günther, Detlef, Haberecker, Martina, Haeuptle, Pirmin, Heinzelmann-Schwarz, Viola, Herter, Sylvia, Holtackers, Rene, Huesser, Tamara, Irmisch, Anja, Jacob, Francis, Jacobs, Andrea, Jaeger, Tim M, Jahn, Katharina, James, Alva R, Jermann, Philip M, Kahles, André, Kahraman, Abdullah, Koelzer, Viktor H, Kuebler, Werner, Kuipers, Jack, Kunze, Christian P, Kurzeder, Christian, Lehmann, Kjong-Van, Levesque, Mitchell, Lugert, Sebastian, Maass, Gerd, Manz, Markus, Markolin, Philipp, Mena, Julien, Menzel, Ulrike, Metzler, Julian M, Miglino, Nicola, Milani, Emanuela S, Moch, Holger, Muenst, Simone, Murri, Riccardo, Ng, Charlotte KY, Nicolet, Stefan, Nowak, Marta, Pedrioli, Patrick GA, Pelkmans, Lucas, Piscuoglio, Salvatore, Prummer, Michael, Ritter, Mathilde, Rommel, Christian, Rosano-González, María L, Rätsch, Gunnar, Santacroce, Natascha, Castillo, Jacobo Sarabia del, Schlenker, Ramona, Schwalie, Petra C, Schwan, Severin, Schär, Tobias, Senti, Gabriela, Sivapatham, Sujana, Snijder, Berend, Sobottka, Bettina, Sreedharan, Vipin T, Stark, Stefan, Stekhoven, Daniel J, Theocharides, Alexandre PA, Thomas, Tinu M, Tolnay, Markus, Tosevski, Vinko, Toussaint, Nora C, Tuncel, Mustafa A, Tusup, Marina, Drogen, Audrey Van, Vetter, Marcus, Vlajnic, Tatjana, Weber, Sandra, Weber, Walter P, Wegmann, Rebekka, Weller, Michael, Wendt, Fabian, Wey, Norbert, Wicki, Andreas, Wollscheid, Bernd, Yu, Shuqing, Ziegler, Johanna, Zimmermann, Marc, Zoche, Martin, Zuend, Gregor, and University of Zurich
- Subjects
Statistics and Probability ,1303 Biochemistry ,AcademicSubjects/SCI01060 ,Computer science ,610 Medicine & health ,computer.software_genre ,Biochemistry ,03 medical and health sciences ,0302 clinical medicine ,Text mining ,1312 Molecular Biology ,1706 Computer Science Applications ,Humans ,Profiling (information science) ,2613 Statistics and Probability ,Molecular Biology ,030304 developmental biology ,Data ,0303 health sciences ,Sequence Analysis, RNA ,business.industry ,Gene Expression Profiling ,Autoencoder ,Computer Science Applications ,Computational Mathematics ,Computational Theory and Mathematics ,10032 Clinic for Oncology and Hematology ,Bipartite graph ,Data mining ,Single-Cell Analysis ,business ,computer ,2605 Computational Mathematics ,Algorithms ,Software ,030217 neurology & neurosurgery ,Data integration ,1703 Computational Theory and Mathematics - Abstract
Motivation: Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results: We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an autoencoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 90% and 78% cell-matching accuracy for each one of the samples, respectively., Bioinformatics, 36 (S2), ISSN:1367-4803, ISSN:1460-2059
- Published
- 2020
5. The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support
- Author
-
Irmisch, Anja, primary, Bonilla, Ximena, additional, Chevrier, Stéphane, additional, Lehmann, Kjong-Van, additional, Singer, Franziska, additional, Toussaint, Nora C., additional, Esposito, Cinzia, additional, Mena, Julien, additional, Milani, Emanuela S., additional, Casanova, Ruben, additional, Stekhoven, Daniel J., additional, Wegmann, Rebekka, additional, Jacob, Francis, additional, Sobottka, Bettina, additional, Goetze, Sandra, additional, Kuipers, Jack, additional, Sarabia del Castillo, Jacobo, additional, Prummer, Michael, additional, Tuncel, Mustafa A., additional, Menzel, Ulrike, additional, Jacobs, Andrea, additional, Engler, Stefanie, additional, Sivapatham, Sujana, additional, Frei, Anja L., additional, Gut, Gabriele, additional, Ficek, Joanna, additional, Miglino, Nicola, additional, Aebersold, Rudolf, additional, Bacac, Marina, additional, Beerenwinkel, Niko, additional, Beisel, Christian, additional, Bodenmiller, Bernd, additional, Dummer, Reinhard, additional, Heinzelmann-Schwarz, Viola, additional, Koelzer, Viktor H., additional, Manz, Markus G., additional, Moch, Holger, additional, Pelkmans, Lucas, additional, Snijder, Berend, additional, Theocharides, Alexandre P.A., additional, Tolnay, Markus, additional, Wicki, Andreas, additional, Wollscheid, Bernd, additional, Rätsch, Gunnar, additional, Levesque, Mitchell P., additional, Ak, Melike, additional, Al-Quaddoomi, Faisal S., additional, Albinus, Jonas, additional, Alborelli, Ilaria, additional, Andani, Sonali, additional, Attinger, Per-Olof, additional, Baumhoer, Daniel, additional, Beck-Schimmer, Beatrice, additional, Bernasconi, Lara, additional, Bertolini, Anne, additional, Chicherova, Natalia, additional, D'Costa, Maya, additional, Danenberg, Esther, additional, Davidson, Natalie, additional, Drăgan, Monica-Andreea, additional, Erkens, Martin, additional, Eschbach, Katja, additional, Fedier, André, additional, Ferreira, Pedro, additional, Frey, Bruno, additional, Grob, Linda, additional, Günther, Detlef, additional, Haberecker, Martina, additional, Haeuptle, Pirmin, additional, Herter, Sylvia, additional, Holtackers, Rene, additional, Huesser, Tamara, additional, Jaeger, Tim M., additional, Jahn, Katharina, additional, James, Alva R., additional, Jermann, Philip M., additional, Kahles, André, additional, Kahraman, Abdullah, additional, Kuebler, Werner, additional, Kunze, Christian P., additional, Kurzeder, Christian, additional, Lugert, Sebastian, additional, Maass, Gerd, additional, Markolin, Philipp, additional, Metzler, Julian M., additional, Muenst, Simone, additional, Murri, Riccardo, additional, Ng, Charlotte K.Y., additional, Nicolet, Stefan, additional, Nowak, Marta, additional, Pedrioli, Patrick G.A., additional, Piscuoglio, Salvatore, additional, Ritter, Mathilde, additional, Rommel, Christian, additional, Rosano-González, María L., additional, Santacroce, Natascha, additional, Schlenker, Ramona, additional, Schwalie, Petra C., additional, Schwan, Severin, additional, Schär, Tobias, additional, Senti, Gabriela, additional, Sreedharan, Vipin T., additional, Stark, Stefan, additional, Thomas, Tinu M., additional, Tosevski, Vinko, additional, Tusup, Marina, additional, Van Drogen, Audrey, additional, Vetter, Marcus, additional, Vlajnic, Tatjana, additional, Weber, Sandra, additional, Weber, Walter P., additional, Weller, Michael, additional, Wendt, Fabian, additional, Wey, Norbert, additional, Wildschut, Mattheus H.E., additional, Yu, Shuqing, additional, Ziegler, Johanna, additional, Zimmermann, Marc, additional, Zoche, Martin, additional, and Zuend, Gregor, additional
- Published
- 2021
- Full Text
- View/download PDF
6. SCIM: Universal Single-Cell Matching with Unpaired Feature Sets
- Author
-
Stark, Stefan, Ficek, Joanna, Locatello, Francesco, Bonilla Bustillo, Ximena, Chicherova, Natalia, Singer, Franziska, Tumor Profiler Consortium, Aebersold, Rudolf, Beerenwinkel, Niko, Al-Quaddoomi, Faisal S., Albinus, Jonas, Beisel, Christian, Bertolini, Anne, Davidson, Natalie, Eschbach, Katja, Ferreira, Pedro, Goetze, Sandra, Grob, Linda, Günther, Detlef, Jahn, Katharina, James, Alva R., Kahles, André, Kuipers, Jack, Lehmann, Kjong-Van, Mena, Julien, Menzel, Ulrike, Milani, Emanuela S., Pedrioli, Patrick G.A., Prummer, Michael, Rosano-Gonzalez, Maria L., Rätsch, Gunnar, Schär, Tobias, Snijder, Berend, Thankam Sreedharan, Vipin, Stekhoven, Daniel J., Thomas, Tinu M., Toussaint, Nora C., Tuncel, Mustafa, van Drogen, Audrey, Wegmann, Rebekka, Wendt, Fabian, Wollscheid, Bernd, Yu, Shuqing, and Zimmermann, Marc
- Abstract
Motivation Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an auto-encoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 93% and 84% cell-matching accuracy for each one of the samples respectively. Availability https://github.com/ratschlab/scim, bioRxiv
- Published
- 2020
7. An adverse outcome pathway-based approach to assess steatotic mixture effects of hepatotoxic pesticides in vitro
- Author
-
Lichtenstein, Dajana, primary, Luckert, Claudia, additional, Alarcan, Jimmy, additional, de Sousa, Georges, additional, Gioutlakis, Michail, additional, Katsanou, Efrosini S., additional, Konstantinidou, Parthena, additional, Machera, Kyriaki, additional, Milani, Emanuela S., additional, Peijnenburg, Ad, additional, Rahmani, Roger, additional, Rijkers, Deborah, additional, Spyropoulou, Anastasia, additional, Stamou, Marianna, additional, Stoopen, Geert, additional, Sturla, Shana J., additional, Wollscheid, Bernd, additional, Zucchini-Pascal, Nathalie, additional, Braeuning, Albert, additional, and Lampen, Alfonso, additional
- Published
- 2020
- Full Text
- View/download PDF
8. Adverse Outcome Pathway-Driven Analysis of Liver Steatosis in Vitro: A Case Study with Cyproconazole
- Author
-
Luckert, Claudia, primary, Braeuning, Albert, additional, de Sousa, Georges, additional, Durinck, Sigrid, additional, Katsanou, Efrosini S., additional, Konstantinidou, Parthena, additional, Machera, Kyriaki, additional, Milani, Emanuela S., additional, Peijnenburg, Ad A. C. M., additional, Rahmani, Roger, additional, Rajkovic, Andreja, additional, Rijkers, Deborah, additional, Spyropoulou, Anastasia, additional, Stamou, Marianna, additional, Stoopen, Geert, additional, Sturla, Shana, additional, Wollscheid, Bernd, additional, Zucchini-Pascal, Nathalie, additional, and Lampen, Alfonso, additional
- Published
- 2018
- Full Text
- View/download PDF
9. Protein tyrosine phosphatase 1B restrains mammary alveologenesis and secretory differentiation
- Author
-
Milani, Emanuela S., primary, Brinkhaus, Heike, additional, Dueggeli, Regula, additional, Klebba, Ina, additional, Mueller, Urs, additional, Stadler, Michael, additional, Kohler, Hubertus, additional, Smalley, Matthew J., additional, and Bentires-Alj, Mohamed, additional
- Published
- 2013
- Full Text
- View/download PDF
10. Elucidation of host-virus surfaceome interactions using spatial proteotyping.
- Author
-
Wendt F, Milani ES, and Wollscheid B
- Subjects
- Host-Pathogen Interactions, Humans, Immune Evasion, Virion metabolism, Virion pathogenicity, Virus Internalization, Host Microbial Interactions, Protein Interaction Mapping methods, Viral Proteins metabolism, Virus Diseases immunology
- Abstract
The cellular surfaceome and its residing extracellularly exposed proteins are involved in a multitude of molecular signaling processes across the viral infection cycle. Successful viral propagation, including viral entry, immune evasion, virion release and viral spread rely on dynamic molecular interactions with the surfaceome. Decoding of these viral-host surfaceome interactions using advanced technologies enabled the discovery of fundamental new functional insights into cellular and viral biology. In this review, we highlight recently developed experimental strategies, with a focus on spatial proteotyping technologies, aiding in the rational design of theranostic strategies to combat viral infections., Competing Interests: Conflict of interest statement The authors declare no conflicts of interest, competing interests or financial interests., (Copyright © 2021 Elsevier Inc. All rights reserved.)
- Published
- 2021
- Full Text
- View/download PDF
Catalog
Books, media, physical & digital resources
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.