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The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles.
Schreiber, Jacob; Boix, Carles; Wook Lee, Jin; Li, Hongyang; Guan, Yuanfang; Chang, Chun-Chieh; Chang, Jen-Chien; Hawkins-Hooker, Alex; Schölkopf, Bernhard; Schweikert, Gabriele; Carulla, Mateo Rojas; Canakoglu, Arif; Guzzo, Francesco; Nanni, Luca; Masseroli, Marco; Carman, Mark James; Pinoli, Pietro; Hong, Chenyang; Yip, Kevin Y; Spence, Jeffrey P; Batra, Sanjit Singh; Song, Yun S; Mahony, Shaun; Zhang, Zheng; Tan, Wuwei; Shen, Yang; Sun, Yuanfei; Shi, Minyi; Adrian, Jessika; Sandstrom, Richard; Farrell, Nina; Halow, Jessica; Lee, Kristen; Jiang, Lixia; Yang, Xinqiong; Epstein, Charles; Strattan, J Seth; Bernstein, Bradley; Snyder, Michael; Kellis, Manolis; Stafford, William; Kundaje, Anshul.
Afiliación
  • Schreiber J; Stanford University School of Medicine, Stanford, CA, USA. jmschreiber91@gmail.com.
  • Boix C; Stanford University School of Medicine, Stanford, CA, USA.
  • Wook Lee J; Stanford University School of Medicine, Stanford, CA, USA.
  • Li H; Stanford University School of Medicine, Stanford, CA, USA.
  • Guan Y; Stanford University School of Medicine, Stanford, CA, USA.
  • Chang CC; Stanford University School of Medicine, Stanford, CA, USA.
  • Chang JC; Stanford University School of Medicine, Stanford, CA, USA.
  • Hawkins-Hooker A; Stanford University School of Medicine, Stanford, CA, USA.
  • Schölkopf B; Stanford University School of Medicine, Stanford, CA, USA.
  • Schweikert G; Stanford University School of Medicine, Stanford, CA, USA.
  • Carulla MR; Stanford University School of Medicine, Stanford, CA, USA.
  • Canakoglu A; Stanford University School of Medicine, Stanford, CA, USA.
  • Guzzo F; Stanford University School of Medicine, Stanford, CA, USA.
  • Nanni L; Stanford University School of Medicine, Stanford, CA, USA.
  • Masseroli M; Stanford University School of Medicine, Stanford, CA, USA.
  • Carman MJ; Stanford University School of Medicine, Stanford, CA, USA.
  • Pinoli P; Stanford University School of Medicine, Stanford, CA, USA.
  • Hong C; Stanford University School of Medicine, Stanford, CA, USA.
  • Yip KY; Stanford University School of Medicine, Stanford, CA, USA.
  • Spence JP; Stanford University School of Medicine, Stanford, CA, USA.
  • Batra SS; Stanford University School of Medicine, Stanford, CA, USA.
  • Song YS; Stanford University School of Medicine, Stanford, CA, USA.
  • Mahony S; Stanford University School of Medicine, Stanford, CA, USA.
  • Zhang Z; Stanford University School of Medicine, Stanford, CA, USA.
  • Tan W; Stanford University School of Medicine, Stanford, CA, USA.
  • Shen Y; Stanford University School of Medicine, Stanford, CA, USA.
  • Sun Y; Stanford University School of Medicine, Stanford, CA, USA.
  • Shi M; Stanford University School of Medicine, Stanford, CA, USA.
  • Adrian J; Stanford University School of Medicine, Stanford, CA, USA.
  • Sandstrom R; Stanford University School of Medicine, Stanford, CA, USA.
  • Farrell N; Stanford University School of Medicine, Stanford, CA, USA.
  • Halow J; Stanford University School of Medicine, Stanford, CA, USA.
  • Lee K; Stanford University School of Medicine, Stanford, CA, USA.
  • Jiang L; Stanford University School of Medicine, Stanford, CA, USA.
  • Yang X; Stanford University School of Medicine, Stanford, CA, USA.
  • Epstein C; Stanford University School of Medicine, Stanford, CA, USA.
  • Strattan JS; Stanford University School of Medicine, Stanford, CA, USA.
  • Bernstein B; Stanford University School of Medicine, Stanford, CA, USA.
  • Snyder M; Stanford University School of Medicine, Stanford, CA, USA.
  • Kellis M; Stanford University School of Medicine, Stanford, CA, USA.
  • Stafford W; Stanford University School of Medicine, Stanford, CA, USA.
  • Kundaje A; Stanford University School of Medicine, Stanford, CA, USA.
Genome Biol ; 24(1): 79, 2023 04 18.
Article en En | MEDLINE | ID: mdl-37072822
ABSTRACT
A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Epigenómica Idioma: En Revista: Genome Biol Asunto de la revista: BIOLOGIA MOLECULAR / GENETICA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Epigenómica Idioma: En Revista: Genome Biol Asunto de la revista: BIOLOGIA MOLECULAR / GENETICA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos