B
Bernard Koch
Researcher at University of California, Los Angeles
Publications - 11
Citations - 798
Bernard Koch is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Causal inference & Mnemiopsis. The author has an hindex of 5, co-authored 10 publications receiving 645 citations. Previous affiliations of Bernard Koch include National Institutes of Health.
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Journal ArticleDOI
The Genome of the Ctenophore Mnemiopsis leidyi and Its Implications for Cell Type Evolution
Joseph F. Ryan,Joseph F. Ryan,Kevin Pang,Christine E. Schnitzler,Anh Dao Nguyen,R. Travis Moreland,David Simmons,Bernard Koch,Warren R. Francis,Paul Havlak,Stephen A. Smith,Stephen A. Smith,Nicholas H. Putnam,Steven H. D. Haddock,Casey W. Dunn,Tyra G. Wolfsberg,James C. Mullikin,Mark Q. Martindale,Andreas D. Baxevanis +18 more
TL;DR: The genome of the ctenophore the warty comb jelly or sea walnut, Mnemiopsis leidyi, is sequenced and it is concluded that c tenophores alone, not sponges or the clade consisting of both ctenphores and cnidarians, are the most basal extant animals.
Journal ArticleDOI
The Diversification of the LIM Superclass at the Base of the Metazoa Increased Subcellular Complexity and Promoted Multicellular Specialization
TL;DR: This work has identified and characterized all known LIM domain-containing proteins in six metazoans and three non-metazoans, formalized a classification system for LIM proteins, provided reasonable timing for class and family origin events; and identified lineage-specific loss events.
Journal ArticleDOI
A customized Web portal for the genome of the ctenophore Mnemiopsis leidyi
R. Travis Moreland,Anh-Dao Nguyen,Joseph F. Ryan,Joseph F. Ryan,Christine E. Schnitzler,Bernard Koch,Katherine M. Siewert,Tyra G. Wolfsberg,Andreas D. Baxevanis +8 more
TL;DR: This sequencing effort has produced the first set of whole-genome sequencing data on any ctenophore species and is amongst the first wave of projects to sequence an animal genome de novo solely using next-generation sequencing technologies.
Proceedings ArticleDOI
HINTS: Citation Time Series Prediction for New Publications via Dynamic Heterogeneous Information Network Embedding
TL;DR: In this paper, a deep learning framework that converts citation signals from dynamic heterogeneous information networks (DHIN) into citation time series is proposed to predict citation counts immediately after publication.
Journal ArticleDOI
Uncovering Sociological Effect Heterogeneity Using Tree-Based Machine Learning:
TL;DR: In this article, sociologists routinely partition samples into subgroups to explore how the effects of treatments vary by selecte...individuals do not respond uniformly to treatments, such as events or interventions.