Zobrazeno 1 - 10 of 49 pro vyhledávání: '"Joseph Luttrell"'
1
Akademický článek
An autoencoder-based deep learning method for genotype imputation
Autor: Meng Song, Jonathan Greenbaum, Joseph Luttrell, Weihua Zhou, Chong Wu, Zhe Luo, Chuan Qiu, Lan Juan Zhao, Kuan-Jui Su, Qing Tian, Hui Shen, Huixiao Hong, Ping Gong, Xinghua Shi, Hong-Wen Deng, Chaoyang Zhang
Publikováno v: Frontiers in Artificial Intelligence, Vol 5 (2022)
Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with f
Externí odkaz: https://doaj.org/article/1002be0b29d54fc69e4b78b22546b6d0
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2
Akademický článek
Structure–activity relationship-based chemical classification of highly imbalanced Tox21 datasets
Autor: Gabriel Idakwo, Sundar Thangapandian, Joseph Luttrell, Yan Li, Nan Wang, Zhaoxian Zhou, Huixiao Hong, Bei Yang, Chaoyang Zhang, Ping Gong
Publikováno v: Journal of Cheminformatics, Vol 12, Iss 1, Pp 1-19 (2020)
Abstract The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure–Activity Relationship (SAR)-based chemical classification. Undersampling and o
Externí odkaz: https://doaj.org/article/b896e5129bce4435858ec953edd5dd19
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3
Akademický článek
Predicting protein residue-residue contacts using random forests and deep networks
Autor: Joseph Luttrell, Tong Liu, Chaoyang Zhang, Zheng Wang
Publikováno v: BMC Bioinformatics, Vol 20, Iss S2, Pp 115-127 (2019)
Abstract Background The ability to predict which pairs of amino acid residues in a protein are in contact with each other offers many advantages for various areas of research that focus on proteins. For example, contact prediction can be used to redu
Externí odkaz: https://doaj.org/article/8502c0180ade446fa4399abe6ca4b2a3
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4
Akademický článek
A Review of Integrative Imputation for Multi-Omics Datasets
Autor: Meng Song, Jonathan Greenbaum, Joseph Luttrell, Weihua Zhou, Chong Wu, Hui Shen, Ping Gong, Chaoyang Zhang, Hong-Wen Deng
Publikováno v: Frontiers in Genetics, Vol 11 (2020)
Multi-omics studies, which explore the interactions between multiple types of biological factors, have significant advantages over single-omics analysis for their ability to provide a more holistic view of biological processes, uncover the causal and
Externí odkaz: https://doaj.org/article/92a95f9933184fd1a20be84d81360fa1
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5
Akademický článek
Deep Learning-Based Structure-Activity Relationship Modeling for Multi-Category Toxicity Classification: A Case Study of 10K Tox21 Chemicals With High-Throughput Cell-Based Androgen Receptor Bioassay Data
Autor: Gabriel Idakwo, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, Ping Gong
Publikováno v: Frontiers in Physiology, Vol 10 (2019)
Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented.
Externí odkaz: https://doaj.org/article/8297a26d1b35453fa6e1e4b17d253b7f
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6
Akademický článek
Deep Learning Based Analysis of Histopathological Images of Breast Cancer
Autor: Juanying Xie, Ran Liu, Joseph Luttrell, Chaoyang Zhang
Publikováno v: Frontiers in Genetics, Vol 10 (2019)
Breast cancer is associated with the highest morbidity rates for cancer diagnoses in the world and has become a major public health issue. Early diagnosis can increase the chance of successful treatment and survival. However, it is a very challenging
Externí odkaz: https://doaj.org/article/9b705ec37714491d9cf05a3b5c0935b3
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9
Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data
Autor: Gabriel Idakwo, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, Ping Gong
Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented.
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::13ea287e687fde07ae445721033dffaf
https://doi.org/10.21079/11681/41302
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10
Predicting protein residue-residue contacts using random forests and deep networks
Autor: Chaoyang Zhang, Joseph Luttrell, Zheng Wang, Tong Liu
Publikováno v: BMC Bioinformatics, Vol 20, Iss S2, Pp 115-127 (2019)
BMC Bioinformatics
Background The ability to predict which pairs of amino acid residues in a protein are in contact with each other offers many advantages for various areas of research that focus on proteins. For example, contact prediction can be used to reduce the co
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9024327fca1fd9f1483675c4b9d7967e
http://link.springer.com/article/10.1186/s12859-019-2627-6
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