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Sarwal Lab »  Lab Members »  Postdoctoral Fellows »  Silvia Pineda, Ph.D.
Silvia Pineda, Ph.D.

Silvia Pineda, Ph.D.

Postdoctoral Fellow

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  • Zaragoza University, Spain, B.S., Statistics, 2006
  • Madrid Complutense University, Spain, M.S., Statistics, 2009
  • Madrid Autónoma University, Spain, M.S., Quantitative Methods in Epidemiology, 2012
  • University of Liege, Belgium Ph.D., System and Modeling Unit, 2015
  • National Cancer Research Centre Ph.D., Genetic and Molecular Epidemiology , 2015

I am currently a Post Doctoral fellow for the Sarwal lab (Department of Surgery at UCSF) and the Sirota lab (Computational Health Science at UCSF) working on kidney organ transplantation. I completed my PhD in Statistical Genetics at the Spanish National Cancer Research Centre in Spain jointly with the University of Liege in Belgium. 

  • Statistics Degree National Award, Ministry of Education and Science, Spain, 2006
  • Extraordinary Award for Statistics Degree, Zaragoza University, Spain, 2006

My work is focused on the development and application of advanced statistical methods for the integration of omics data. My research interest is based on the development of advanced statistical approaches to integrate omics and clinical/epidemiological data to better understand complex diseases. Now, I am involved in the analysis of Next Generation Sequencing data to better explain the rejection process of kidney transplants.

Most recent publications from a total of 15
  1. Pineda S, Sigdel TK, Liberto JM, Vincenti F, Sirota M, Sarwal MM. Characterizing pre-transplant and post-transplant kidney rejection risk by B cell immune repertoire sequencing. Nat Commun. 2019 04 23; 10(1):1906. View in PubMed
  2. López de Maturana E, Alonso L, Alarcón P, Martín-Antoniano IA, Pineda S, Piorno L, Calle ML, Malats N. Challenges in the Integration of Omics and Non-Omics Data. Genes (Basel). 2019 03 20; 10(3). View in PubMed
  3. Pineda S, Sirota M. Determining Significance in the New Era for P Values. J Pediatr Gastroenterol Nutr. 2018 11; 67(5):547-548. View in PubMed
  4. Pineda S, Sigdel TK, Chen J, Jackson AM, Sirota M, Sarwal MM. Corrigendum: Novel Non-Histocompatibility Antigen Mismatched Variants Improve the Ability to Predict Antibody-Mediated Rejection Risk in Kidney Transplant. Front Immunol. 2018; 9:107. View in PubMed
  5. Pineda S, Sigdel TK, Chen J, Jackson AM, Sirota M, Sarwal MM. Novel Non-Histocompatibility Antigen Mismatched Variants Improve the Ability to Predict Antibody-Mediated Rejection Risk in Kidney Transplant. Front Immunol. 2017; 8:1687. View in PubMed
  6. Pineda S, Van Steen K, Malats N. Integrative eQTL analysis of tumor and host omics data in individuals with bladder cancer. Genet Epidemiol. 2017 09; 41(6):567-573. View in PubMed
  7. López de Maturana E, Pineda S, Brand A, Van Steen K, Malats N. Toward the integration of Omics data in epidemiological studies: still a "long and winding road". Genet Epidemiol. 2016 11; 40(7):558-569. View in PubMed
  8. Pineda S, Real FX, Kogevinas M, Carrato A, Chanock SJ, Malats N, Van Steen K. Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer. PLoS Genet. 2015 Dec; 11(12):e1005689. View in PubMed
  9. Mason-Lecomte A, Lopez de Maturana E, Pineda S, Rava M, Vordos D, Allory Y, Real F, Malats N. [Not Available]. Prog Urol. 2015 Nov; 25(13):758-9. View in PubMed
  10. Pineda S, Gomez-Rubio P, Picornell A, Bessonov K, Márquez M, Kogevinas M, Real FX, Van Steen K, Malats N. Framework for the Integration of Genomics, Epigenomics and Transcriptomics in Complex Diseases. Hum Hered. 2015; 79(3-4):124-36. View in PubMed
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