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Daniel E. Carlin

Researcher at University of California, San Diego

Publications -  25
Citations -  28069

Daniel E. Carlin is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Cancer & Biological network. The author has an hindex of 15, co-authored 25 publications receiving 22707 citations. Previous affiliations of Daniel E. Carlin include University of California, Santa Cruz & Institute for Systems Biology.

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A Fast and Flexible Framework for Network-Assisted Genomic Association.

TL;DR: It is shown that NAGA recovers many known disease genes from analysis of schizophrenia genetic data, and it substantially boosts associations with previously unappreciated genes such as amyloid beta precursor.
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A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines

Mehmet Gönen, +91 more
- 22 Nov 2017 - 
TL;DR: This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens.
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pyNBS: a Python implementation for network-based stratification of tumor mutations.

TL;DR: A modularized Python 2.7 implementation of the network-based stratification (NBS) algorithm for stratifying tumor somatic mutation profiles into molecularly and clinically relevant subtypes and provides a compact cancer reference network that increases the significance of tumor stratification using the NBS algorithm.
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Prophetic Granger Causality to infer gene regulatory networks.

TL;DR: The investigations reveal that PGC provides complementary information to other approaches, raising the performance of ensemble learners, while on its own achieves moderate performance, and serves as a valuable new tool in the bioinformatics toolkit for analyzing temporal datasets.
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Modeling of RAS complexes supports roles in cancer for less studied partners

TL;DR: A RAS isoform-specific protein-protein interaction network is constructed and predicted 3D complexes involving RASisoforms and interaction partners to identify the most probable interaction interfaces and implicated residues in the allosteric and hyper-variable regions of RAS proteins as the predominant binding site for non-canonical effectors.