Search Results - "Joe, G."

A guide to machine learning for biologists by Greener, Joe G., Kandathil, Shaun M., Moffat, Lewis, Jones, David T.

“…The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive…”
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Differentiable simulation to develop molecular dynamics force fields for disordered proteins by Greener, Joe G

“…Implicit solvent force fields are computationally efficient but can be unsuitable for running molecular dynamics on disordered proteins. Here I improve the…”
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Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints by Greener, Joe G., Kandathil, Shaun M., Jones, David T.

“…The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently,…”
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Nontuberculous mycobacterial disease mortality in the United States, 1999-2010: a population-based comparative study by Mirsaeidi, Mehdi, Machado, Roberto F, Garcia, Joe G N, Schraufnagel, Dean E

“…Environmental nontuberculous mycobacteria (NTM) are ubiquitous organisms with which humans commonly interact. The epidemiologic characteristics of NTM diseases…”
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Design of metalloproteins and novel protein folds using variational autoencoders by Greener, Joe G., Moffat, Lewis, Jones, David T

“…The design of novel proteins has many applications but remains an attritional process with success in isolated cases. Meanwhile, deep learning technologies…”
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Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins by Greener, Joe G, Jones, David T

“…Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning…”
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Commentary on Conde et al.: Evidence and gap map offer an important opportunity for dialogue and refinement of the gateway hypothesis controversy by Selya, Arielle, Gitchell, Joe G.

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Magnetic Coupling in Colloidal Clusters for Hierarchical Self-Assembly by Donaldson, Joe G, Schall, Peter, Rossi, Laura

“…Manipulating the way in which colloidal particles self-organize is a central challenge in the design of functional soft materials. Meeting this challenge…”
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Structure-based prediction of protein allostery by Greener, Joe G, Sternberg, Michael JE

“…•The structure-based prediction of allostery will realise the potential of allostery.•Modern computational methods can help predict allosteric sites and…”
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Prediction of interresidue contacts with DeepMetaPSICOV in CASP13 by Kandathil, Shaun M., Greener, Joe G., Jones, David T.

“…In this article, we describe our efforts in contact prediction in the CASP13 experiment. We employed a new deep learning‐based contact prediction tool,…”
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Asthma is Different in Women by Zein, Joe G., Erzurum, Serpil C.

“…ABSTRACT Gender differences in asthma incidence, prevalence and severity have been reported worldwide. After puberty, asthma becomes more prevalent and severe…”
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The Differing Roles of Flavins and Quinones in Extracellular Electron Transfer in Lactiplantibacillus plantarum by Tolar, Joe G, Li, Siliang, Ajo-Franklin, Caroline M

“…Lactiplantibacillus plantarum is a lactic acid bacterium that is commonly found in the human gut and fermented food products. Despite its overwhelmingly…”
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Novel Machine Learning Can Predict Acute Asthma Exacerbation by Zein, Joe G., Wu, Chao-Ping, Attaway, Amy H., Zhang, Peng, Nazha, Aziz

“…Asthma exacerbations result in significant health and economic burden, but are difficult to predict. Can machine learning (ML) models with large-scale…”
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Development of a biomarker mortality risk model in acute respiratory distress syndrome by Bime, Christian, Casanova, Nancy, Oita, Radu C, Ndukum, Juliet, Lynn, Heather, Camp, Sara M, Lussier, Yves, Abraham, Ivo, Carter, Darrick, Miller, Edmund J, Mekontso-Dessap, Armand, Downs, Charles A, Garcia, Joe G N

“…There is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome. Effective…”
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Epidemiology of Asthma: Prevalence and Burden of Disease by Merhej, Tamara, Zein, Joe G

“…Asthma, a common airway disease, results in a significant burden to both patients and society worldwide. Yet, despite global political commitment backed by the…”
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Involvement of eNAMPT/TLR4 inflammatory signaling in progression of non‐alcoholic fatty liver disease, steatohepatitis, and fibrosis by Sun, Belinda L., Sun, Xiaoguang, Kempf, Carrie L., Song, Jin H., Casanova, Nancy G., Camp, Sara M., Reyes Hernon, Vivian, Fallon, Michael, Bime, Christian, Martin, Diego R., Travelli, Cristina, Zhang, Donna D., Garcia, Joe G. N.

“…Although the progression of non‐alcoholic fatty liver disease (NAFLD) from steatosis to steatohepatitis (NASH) and cirrhosis remains poorly understood, a…”
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Recent developments in deep learning applied to protein structure prediction by Kandathil, Shaun M., Greener, Joe G., Jones, David T.

“…Although many structural bioinformatics tools have been using neural network models for a long time, deep neural network (DNN) models have attracted…”
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Age-related macular degeneration: the molecular link between oxidative damage, tissue-specific inflammation and outer retinal disease: the Proctor lecture by Hollyfield, Joe G

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NRF2 and Diabetes: The Good, the Bad, and the Complex by Dodson, Matthew, Shakya, Aryatara, Anandhan, Annadurai, Chen, Jinjing, Garcia, Joe G N, Zhang, Donna D

“…Despite decades of scientific effort, diabetes continues to represent an incredibly complex and difficult disease to treat. This is due in large part to the…”
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Integrative omics provide biological and clinical insights into acute respiratory distress syndrome by Du, Mulong, Garcia, Joe G. N., Christie, Jason D., Xin, Junyi, Cai, Guoshuai, Meyer, Nuala J., Zhu, Zhaozhong, Yuan, Qianyu, Zhang, Zhengdong, Su, Li, Shen, Sipeng, Dong, Xuesi, Li, Hui, Hutchinson, John N., Tejera, Paula, Lin, Xihong, Wang, Meilin, Chen, Feng, Christiani, David C.

“…Purpose Acute respiratory distress syndrome (ARDS) is accompanied by a dysfunctional immune-inflammatory response following lung injury, including during…”
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