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Showing 1 to 12 of 18 entries
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Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites.

Journal of chemical information and modeling

Hughes TB, Dang NL, Kumar A, Flynn NR, Swamidass SJ.
PMID: 32881497
J Chem Inf Model. 2020 Oct 26;60(10):4702-4716. doi: 10.1021/acs.jcim.0c00360. Epub 2020 Sep 16.

Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across thousands of candidates in the early stages of drug...

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Hughes TB, Dang NL, Kumar A, et al. Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites. J Chem Inf Model. 2020;60(10):4702-4716doi: 10.1021/acs.jcim.0c00360.
Hughes, T. B., Dang, N. L., Kumar, A., Flynn, N. R., & Swamidass, S. J. (2020). Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites. Journal of chemical information and modeling, 60(10), 4702-4716. https://doi.org/10.1021/acs.jcim.0c00360
Hughes, Tyler B, et al. "Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites." Journal of chemical information and modeling vol. 60,10 (2020): 4702-4716. doi: https://doi.org/10.1021/acs.jcim.0c00360
Hughes TB, Dang NL, Kumar A, Flynn NR, Swamidass SJ. Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites. J Chem Inf Model. 2020 Oct 26;60(10):4702-4716. doi: 10.1021/acs.jcim.0c00360. Epub 2020 Sep 16. PMID: 32881497; PMCID: PMC8716321.
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XenoNet: Inference and Likelihood of Intermediate Metabolite Formation.

Journal of chemical information and modeling

Flynn NR, Dang NL, Ward MD, Swamidass SJ.
PMID: 32525671
J Chem Inf Model. 2020 Jul 27;60(7):3431-3449. doi: 10.1021/acs.jcim.0c00361. Epub 2020 Jun 29.

Drug metabolism is a common cause of adverse drug reactions. Drug molecules can be metabolized into reactive metabolites, which can conjugate to biomolecules, like protein and DNA, in a process termed bioactivation. To mitigate adverse reactions caused by bioactivation,...

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Flynn NR, Dang NL, Ward MD, et al. XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J Chem Inf Model. 2020;60(7):3431-3449doi: 10.1021/acs.jcim.0c00361.
Flynn, N. R., Dang, N. L., Ward, M. D., & Swamidass, S. J. (2020). XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. Journal of chemical information and modeling, 60(7), 3431-3449. https://doi.org/10.1021/acs.jcim.0c00361
Flynn, Noah R, et al. "XenoNet: Inference and Likelihood of Intermediate Metabolite Formation." Journal of chemical information and modeling vol. 60,7 (2020): 3431-3449. doi: https://doi.org/10.1021/acs.jcim.0c00361
Flynn NR, Dang NL, Ward MD, Swamidass SJ. XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J Chem Inf Model. 2020 Jul 27;60(7):3431-3449. doi: 10.1021/acs.jcim.0c00361. Epub 2020 Jun 29. PMID: 32525671.
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Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network.

ACS central science

Hughes TB, Miller GP, Swamidass SJ.
PMID: 27162970
ACS Cent Sci. 2015 Jul 22;1(4):168-80. doi: 10.1021/acscentsci.5b00131. Epub 2015 Jun 09.

Drug toxicity is frequently caused by electrophilic reactive metabolites that covalently bind to proteins. Epoxides comprise a large class of three-membered cyclic ethers. These molecules are electrophilic and typically highly reactive due to ring tension and polarized carbon-oxygen bonds....

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Hughes TB, Miller GP, Swamidass SJ. Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network. ACS Cent Sci. 2015;1(4):168-80doi: 10.1021/acscentsci.5b00131.
Hughes, T. B., Miller, G. P., & Swamidass, S. J. (2015). Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network. ACS central science, 1(4), 168-80. https://doi.org/10.1021/acscentsci.5b00131
Hughes, Tyler B, et al. "Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network." ACS central science vol. 1,4 (2015): 168-80. doi: https://doi.org/10.1021/acscentsci.5b00131
Hughes TB, Miller GP, Swamidass SJ. Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network. ACS Cent Sci. 2015 Jul 22;1(4):168-80. doi: 10.1021/acscentsci.5b00131. Epub 2015 Jun 09. PMID: 27162970; PMCID: PMC4827534.
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Continuity, divergence, and the evolution of brain language pathways.

Frontiers in evolutionary neuroscience

Rilling JK, Glasser MF, Jbabdi S, Andersson J, Preuss TM.
PMID: 22319495
Front Evol Neurosci. 2012 Jan 03;3:11. doi: 10.3389/fnevo.2011.00011. eCollection 2011.

Recently, the assumption of evolutionary continuity between humans and non-human primates has been used to bolster the hypothesis that human language is mediated especially by the ventral extreme capsule pathway that mediates auditory object recognition in macaques. Here, we...

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Rilling JK, Glasser MF, Jbabdi S, et al. Continuity, divergence, and the evolution of brain language pathways. Front Evol Neurosci. 2012;3:11doi: 10.3389/fnevo.2011.00011.
Rilling, J. K., Glasser, M. F., Jbabdi, S., Andersson, J., & Preuss, T. M. (2011). Continuity, divergence, and the evolution of brain language pathways. Frontiers in evolutionary neuroscience, 311. https://doi.org/10.3389/fnevo.2011.00011
Rilling, James K, et al. "Continuity, divergence, and the evolution of brain language pathways." Frontiers in evolutionary neuroscience vol. 3 (2011): 11. doi: https://doi.org/10.3389/fnevo.2011.00011
Rilling JK, Glasser MF, Jbabdi S, Andersson J, Preuss TM. Continuity, divergence, and the evolution of brain language pathways. Front Evol Neurosci. 2012 Jan 03;3:11. doi: 10.3389/fnevo.2011.00011. eCollection 2011. PMID: 22319495; PMCID: PMC3249609.
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Fast electron paramagnetic resonance magic angle spinning simulations using analytical powder averaging techniques.

The Journal of chemical physics

Saliba EP, Barnes AB.
PMID: 31542017
J Chem Phys. 2019 Sep 21;151(11):114107. doi: 10.1063/1.5113598.

Simulations describing the spin physics underpinning nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR) spectroscopy play an important role in the design of new experiments. When experiments are performed in the solid state, samples are commonly composed of...

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Saliba EP, Barnes AB. Fast electron paramagnetic resonance magic angle spinning simulations using analytical powder averaging techniques. J Chem Phys. 2019;151(11):114107doi: 10.1063/1.5113598.
Saliba, E. P., & Barnes, A. B. (2019). Fast electron paramagnetic resonance magic angle spinning simulations using analytical powder averaging techniques. The Journal of chemical physics, 151(11), 114107. https://doi.org/10.1063/1.5113598
Saliba, Edward P, and Barnes, Alexander B. "Fast electron paramagnetic resonance magic angle spinning simulations using analytical powder averaging techniques." The Journal of chemical physics vol. 151,11 (2019): 114107. doi: https://doi.org/10.1063/1.5113598
Saliba EP, Barnes AB. Fast electron paramagnetic resonance magic angle spinning simulations using analytical powder averaging techniques. J Chem Phys. 2019 Sep 21;151(11):114107. doi: 10.1063/1.5113598. PMID: 31542017; PMCID: PMC7043854.
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A Representational Similarity Analysis of Cognitive Control during Color-Word Stroop.

The Journal of neuroscience : the official journal of the Society for Neuroscience

Freund MC, Bugg JM, Braver TS.
PMID: 34162756
J Neurosci. 2021 Sep 01;41(35):7388-7402. doi: 10.1523/JNEUROSCI.2956-20.2021. Epub 2021 Jun 23.

Progress in understanding the neural bases of cognitive control has been supported by the paradigmatic color-word Stroop task, in which a target response (color name) must be selected over a more automatic, yet potentially incongruent, distractor response (word). For...

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Freund MC, Bugg JM, Braver TS. A Representational Similarity Analysis of Cognitive Control during Color-Word Stroop. J Neurosci. 2021;41(35):7388-7402doi: 10.1523/JNEUROSCI.2956-20.2021.
Freund, M. C., Bugg, J. M., & Braver, T. S. (2021). A Representational Similarity Analysis of Cognitive Control during Color-Word Stroop. The Journal of neuroscience : the official journal of the Society for Neuroscience, 41(35), 7388-7402. https://doi.org/10.1523/JNEUROSCI.2956-20.2021
Freund, Michael C, et al. "A Representational Similarity Analysis of Cognitive Control during Color-Word Stroop." The Journal of neuroscience : the official journal of the Society for Neuroscience vol. 41,35 (2021): 7388-7402. doi: https://doi.org/10.1523/JNEUROSCI.2956-20.2021
Freund MC, Bugg JM, Braver TS. A Representational Similarity Analysis of Cognitive Control during Color-Word Stroop. J Neurosci. 2021 Sep 01;41(35):7388-7402. doi: 10.1523/JNEUROSCI.2956-20.2021. Epub 2021 Jun 23. PMID: 34162756; PMCID: PMC8412987.
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XenoNet: Inference and Likelihood of Intermediate Metabolite Formation.

Journal of chemical information and modeling

Flynn NR, Dang NL, Ward MD, Swamidass SJ.
PMID: 32525671
J Chem Inf Model. 2020 Jul 27;60(7):3431-3449. doi: 10.1021/acs.jcim.0c00361. Epub 2020 Jun 29.

Drug metabolism is a common cause of adverse drug reactions. Drug molecules can be metabolized into reactive metabolites, which can conjugate to biomolecules, like protein and DNA, in a process termed bioactivation. To mitigate adverse reactions caused by bioactivation,...

Cite
Flynn NR, Dang NL, Ward MD, et al. XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J Chem Inf Model. 2020;60(7):3431-3449doi: 10.1021/acs.jcim.0c00361.
Flynn, N. R., Dang, N. L., Ward, M. D., & Swamidass, S. J. (2020). XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. Journal of chemical information and modeling, 60(7), 3431-3449. https://doi.org/10.1021/acs.jcim.0c00361
Flynn, Noah R, et al. "XenoNet: Inference and Likelihood of Intermediate Metabolite Formation." Journal of chemical information and modeling vol. 60,7 (2020): 3431-3449. doi: https://doi.org/10.1021/acs.jcim.0c00361
Flynn NR, Dang NL, Ward MD, Swamidass SJ. XenoNet: Inference and Likelihood of Intermediate Metabolite Formation. J Chem Inf Model. 2020 Jul 27;60(7):3431-3449. doi: 10.1021/acs.jcim.0c00361. Epub 2020 Jun 29. PMID: 32525671; PMCID: PMC8716322.
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Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network.

ACS central science

Hughes TB, Dang NL, Miller GP, Swamidass SJ.
PMID: 27610414
ACS Cent Sci. 2016 Aug 24;2(8):529-37. doi: 10.1021/acscentsci.6b00162. Epub 2016 Jul 29.

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict...

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Hughes TB, Dang NL, Miller GP, et al. Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network. ACS Cent Sci. 2016;2(8):529-37doi: 10.1021/acscentsci.6b00162.
Hughes, T. B., Dang, N. L., Miller, G. P., & Swamidass, S. J. (2016). Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network. ACS central science, 2(8), 529-37. https://doi.org/10.1021/acscentsci.6b00162
Hughes, Tyler B, et al. "Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network." ACS central science vol. 2,8 (2016): 529-37. doi: https://doi.org/10.1021/acscentsci.6b00162
Hughes TB, Dang NL, Miller GP, Swamidass SJ. Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network. ACS Cent Sci. 2016 Aug 24;2(8):529-37. doi: 10.1021/acscentsci.6b00162. Epub 2016 Jul 29. PMID: 27610414; PMCID: PMC4999971.
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Informatics and data mining tools and strategies for the human connectome project.

Frontiers in neuroinformatics

Marcus DS, Harwell J, Olsen T, Hodge M, Glasser MF, Prior F, Jenkinson M, Laumann T, Curtiss SW, Van Essen DC.
PMID: 21743807
Front Neuroinform. 2011 Jun 27;5:4. doi: 10.3389/fninf.2011.00004. eCollection 2011.

The Human Connectome Project (HCP) is a major endeavor that will acquire and analyze connectivity data plus other neuroimaging, behavioral, and genetic data from 1,200 healthy adults. It will serve as a key resource for the neuroscience research community,...

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Marcus DS, Harwell J, Olsen T, et al. Informatics and data mining tools and strategies for the human connectome project. Front Neuroinform. 2011;5:4doi: 10.3389/fninf.2011.00004.
Marcus, D. S., Harwell, J., Olsen, T., Hodge, M., Glasser, M. F., Prior, F., Jenkinson, M., Laumann, T., Curtiss, S. W., & Van Essen, D. C. (2011). Informatics and data mining tools and strategies for the human connectome project. Frontiers in neuroinformatics, 54. https://doi.org/10.3389/fninf.2011.00004
Marcus, Daniel S, et al. "Informatics and data mining tools and strategies for the human connectome project." Frontiers in neuroinformatics vol. 5 (2011): 4. doi: https://doi.org/10.3389/fninf.2011.00004
Marcus DS, Harwell J, Olsen T, Hodge M, Glasser MF, Prior F, Jenkinson M, Laumann T, Curtiss SW, Van Essen DC. Informatics and data mining tools and strategies for the human connectome project. Front Neuroinform. 2011 Jun 27;5:4. doi: 10.3389/fninf.2011.00004. eCollection 2011. PMID: 21743807; PMCID: PMC3127103.
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High-Resolution Identification of Specificity Determining Positions in the LacI Protein Family Using Ensembles of Sub-Sampled Alignments.

PloS one

Sloutsky R, Naegle KM.
PMID: 27681038
PLoS One. 2016 Sep 28;11(9):e0162579. doi: 10.1371/journal.pone.0162579. eCollection 2016.

Since the advent of large-scale genomic sequencing, and the consequent availability of large numbers of homologous protein sequences, there has been burgeoning development of methods for extracting functional information from multiple sequence alignments (MSAs). One type of analysis seeks...

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Sloutsky R, Naegle KM. High-Resolution Identification of Specificity Determining Positions in the LacI Protein Family Using Ensembles of Sub-Sampled Alignments. PLoS One. 2016;11(9):e0162579doi: 10.1371/journal.pone.0162579.
Sloutsky, R., & Naegle, K. M. (2016). High-Resolution Identification of Specificity Determining Positions in the LacI Protein Family Using Ensembles of Sub-Sampled Alignments. PloS one, 11(9), e0162579. https://doi.org/10.1371/journal.pone.0162579
Sloutsky, Roman, and Naegle, Kristen M. "High-Resolution Identification of Specificity Determining Positions in the LacI Protein Family Using Ensembles of Sub-Sampled Alignments." PloS one vol. 11,9 (2016): e0162579. doi: https://doi.org/10.1371/journal.pone.0162579
Sloutsky R, Naegle KM. High-Resolution Identification of Specificity Determining Positions in the LacI Protein Family Using Ensembles of Sub-Sampled Alignments. PLoS One. 2016 Sep 28;11(9):e0162579. doi: 10.1371/journal.pone.0162579. eCollection 2016. PMID: 27681038; PMCID: PMC5040260.
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Learning a Local-Variable Model of Aromatic and Conjugated Systems.

ACS central science

Matlock MK, Dang NL, Swamidass SJ.
PMID: 29392176
ACS Cent Sci. 2018 Jan 24;4(1):52-62. doi: 10.1021/acscentsci.7b00405. Epub 2018 Jan 03.

A collection of new approaches to building and training neural networks, collectively referred to as deep learning, are attracting attention in theoretical chemistry. Several groups aim to replace computationally expensive

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Matlock MK, Dang NL, Swamidass SJ. Learning a Local-Variable Model of Aromatic and Conjugated Systems. ACS Cent Sci. 2018;4(1):52-62doi: 10.1021/acscentsci.7b00405.
Matlock, M. K., Dang, N. L., & Swamidass, S. J. (2018). Learning a Local-Variable Model of Aromatic and Conjugated Systems. ACS central science, 4(1), 52-62. https://doi.org/10.1021/acscentsci.7b00405
Matlock, Matthew K, et al. "Learning a Local-Variable Model of Aromatic and Conjugated Systems." ACS central science vol. 4,1 (2018): 52-62. doi: https://doi.org/10.1021/acscentsci.7b00405
Matlock MK, Dang NL, Swamidass SJ. Learning a Local-Variable Model of Aromatic and Conjugated Systems. ACS Cent Sci. 2018 Jan 24;4(1):52-62. doi: 10.1021/acscentsci.7b00405. Epub 2018 Jan 03. PMID: 29392176; PMCID: PMC5785769.
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Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease.

Brain communications

Vermunt L, Dicks E, Wang G, Dincer A, Flores S, Keefe SJ, Berman SB, Cash DM, Chhatwal JP, Cruchaga C, Fox NC, Ghetti B, Graff-Radford NR, Hassenstab J, Karch CM, Laske C, Levin J, Masters CL, McDade E, Mori H, Morris JC, Noble JM, Perrin RJ, Schofield PR, Xiong C, Scheltens P, Visser PJ, Bateman RJ, Benzinger TLS, Tijms BM, Gordon BA.
PMID: 32954344
Brain Commun. 2020 Jul 15;2(2):fcaa102. doi: 10.1093/braincomms/fcaa102. eCollection 2020.

Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer's disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore,...

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Vermunt L, Dicks E, Wang G, et al. Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease. Brain Commun. 2020;2(2):fcaa102doi: 10.1093/braincomms/fcaa102.
Vermunt, L., Dicks, E., Wang, G., Dincer, A., Flores, S., Keefe, S. J., Berman, S. B., Cash, D. M., Chhatwal, J. P., Cruchaga, C., Fox, N. C., Ghetti, B., Graff-Radford, N. R., Hassenstab, J., Karch, C. M., Laske, C., Levin, J., Masters, C. L., McDade, E., Mori, H., Morris, J. C., Noble, J. M., Perrin, R. J., Schofield, P. R., Xiong, C., Scheltens, P., Visser, P. J., Bateman, R. J., Benzinger, T. L. S., Tijms, B. M., Gordon, B. A. (2020). Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease. Brain communications, 2(2), fcaa102. https://doi.org/10.1093/braincomms/fcaa102
Vermunt, Lisa, et al. "Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease." Brain communications vol. 2,2 (2020): fcaa102. doi: https://doi.org/10.1093/braincomms/fcaa102
Vermunt L, Dicks E, Wang G, Dincer A, Flores S, Keefe SJ, Berman SB, Cash DM, Chhatwal JP, Cruchaga C, Fox NC, Ghetti B, Graff-Radford NR, Hassenstab J, Karch CM, Laske C, Levin J, Masters CL, McDade E, Mori H, Morris JC, Noble JM, Perrin RJ, Schofield PR, Xiong C, Scheltens P, Visser PJ, Bateman RJ, Benzinger TLS, Tijms BM, Gordon BA. Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease. Brain Commun. 2020 Jul 15;2(2):fcaa102. doi: 10.1093/braincomms/fcaa102. eCollection 2020. PMID: 32954344; PMCID: PMC7475695.
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Showing 1 to 12 of 18 entries