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Showing 1045 to 1056 of 1077 entries
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Semi-supervised network inference using simulated gene expression dynamics.

Bioinformatics (Oxford, England)

Nguyen P, Braun R.
PMID: 29186340
Bioinformatics. 2018 Apr 01;34(7):1148-1156. doi: 10.1093/bioinformatics/btx748.

MOTIVATION: Inferring the structure of gene regulatory networks from high-throughput datasets remains an important and unsolved problem. Current methods are hampered by problems such as noise, low sample size, and incomplete characterizations of regulatory dynamics, leading to networks with...

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Nguyen P, Braun R. Semi-supervised network inference using simulated gene expression dynamics. Bioinformatics. 2018;34(7):1148-1156doi: 10.1093/bioinformatics/btx748.
Nguyen, P., & Braun, R. (2018). Semi-supervised network inference using simulated gene expression dynamics. Bioinformatics (Oxford, England), 34(7), 1148-1156. https://doi.org/10.1093/bioinformatics/btx748
Nguyen, Phan, and Braun, Rosemary. "Semi-supervised network inference using simulated gene expression dynamics." Bioinformatics (Oxford, England) vol. 34,7 (2018): 1148-1156. doi: https://doi.org/10.1093/bioinformatics/btx748
Nguyen P, Braun R. Semi-supervised network inference using simulated gene expression dynamics. Bioinformatics. 2018 Apr 01;34(7):1148-1156. doi: 10.1093/bioinformatics/btx748. PMID: 29186340; PMCID: PMC6455938.
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Calculating Competitive Intransitivity: Computational Challenges.

The American naturalist

Laird RA, Schamp BS.
PMID: 29570401
Am Nat. 2018 Apr;191(4):547-552. doi: 10.1086/696266. Epub 2018 Feb 16.

Intransitive (or "rock-paper-scissors") competition is compelling because it promotes species coexistence and because recent work suggests that it may be common in natural systems. One class of intransitivity indices works by considering s, the minimum number of competitive reversals...

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Laird RA, Schamp BS. Calculating Competitive Intransitivity: Computational Challenges. Am Nat. 2018;191(4):547-552doi: 10.1086/696266.
Laird, R. A., & Schamp, B. S. (2018). Calculating Competitive Intransitivity: Computational Challenges. The American naturalist, 191(4), 547-552. https://doi.org/10.1086/696266
Laird, Robert A, and Schamp, Brandon S. "Calculating Competitive Intransitivity: Computational Challenges." The American naturalist vol. 191,4 (2018): 547-552. doi: https://doi.org/10.1086/696266
Laird RA, Schamp BS. Calculating Competitive Intransitivity: Computational Challenges. Am Nat. 2018 Apr;191(4):547-552. doi: 10.1086/696266. Epub 2018 Feb 16. PMID: 29570401.
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Using agent-based modeling to study multiple risk factors and multiple health outcomes at multiple levels.

Annals of the New York Academy of Sciences

Yang Y.
PMID: 29239482
Ann N Y Acad Sci. 2017 Nov;1408(1):7-14. doi: 10.1111/nyas.13558.

Most health studies focus on one health outcome and examine the influence of one or multiple risk factors. However, in reality, various pathways, interactions, and associations exist not only between risk factors and health outcomes but also among the...

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Yang Y. Using agent-based modeling to study multiple risk factors and multiple health outcomes at multiple levels. Ann N Y Acad Sci. 2017;1408(1):7-14doi: 10.1111/nyas.13558.
Yang, Y. (2017). Using agent-based modeling to study multiple risk factors and multiple health outcomes at multiple levels. Annals of the New York Academy of Sciences, 1408(1), 7-14. https://doi.org/10.1111/nyas.13558
Yang, Yong. "Using agent-based modeling to study multiple risk factors and multiple health outcomes at multiple levels." Annals of the New York Academy of Sciences vol. 1408,1 (2017): 7-14. doi: https://doi.org/10.1111/nyas.13558
Yang Y. Using agent-based modeling to study multiple risk factors and multiple health outcomes at multiple levels. Ann N Y Acad Sci. 2017 Nov;1408(1):7-14. doi: 10.1111/nyas.13558. PMID: 29239482.
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Cell autonomous sanctions in legumes target ineffective rhizobia in nodules with mixed infections.

American journal of botany

Regus JU, Quides KW, O'Neill MR, Suzuki R, Savory EA, Chang JH, Sachs JL.
PMID: 29885243
Am J Bot. 2017 Sep;104(9):1299-1312. doi: 10.3732/ajb.1700165.

PREMISE OF THE STUDY: To maximize benefits from symbiosis, legumes must limit physiological inputs into ineffective rhizobia that nodulate hosts without fixing nitrogen. The capacity of legumes to decrease the relative fitness of ineffective rhizobia-known as sanctions-has been demonstrated...

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Regus JU, Quides KW, O'Neill MR, et al. Cell autonomous sanctions in legumes target ineffective rhizobia in nodules with mixed infections. Am J Bot. 2017;104(9):1299-1312doi: 10.3732/ajb.1700165.
Regus, J. U., Quides, K. W., O'Neill, M. R., Suzuki, R., Savory, E. A., Chang, J. H., & Sachs, J. L. (2017). Cell autonomous sanctions in legumes target ineffective rhizobia in nodules with mixed infections. American journal of botany, 104(9), 1299-1312. https://doi.org/10.3732/ajb.1700165
Regus, John U, et al. "Cell autonomous sanctions in legumes target ineffective rhizobia in nodules with mixed infections." American journal of botany vol. 104,9 (2017): 1299-1312. doi: https://doi.org/10.3732/ajb.1700165
Regus JU, Quides KW, O'Neill MR, Suzuki R, Savory EA, Chang JH, Sachs JL. Cell autonomous sanctions in legumes target ineffective rhizobia in nodules with mixed infections. Am J Bot. 2017 Sep;104(9):1299-1312. doi: 10.3732/ajb.1700165. PMID: 29885243.
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Invasion waves in the biochemical warfare between living organisms.

Physical review. E

Carvalho SA, Martins ML.
PMID: 29758635
Phys Rev E. 2018 Apr;97(4):042403. doi: 10.1103/PhysRevE.97.042403.

Microorganisms and plants very commonly release toxic secondary chemical compounds (allelochemicals) that inhibit or kill sensitive strains or individuals from their own or other species. In this work we study a model that describes two species interacting through allelopathic...

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Carvalho SA, Martins ML. Invasion waves in the biochemical warfare between living organisms. Phys Rev E. 2018;97(4):042403doi: 10.1103/PhysRevE.97.042403.
Carvalho, S. A., & Martins, M. L. (2018). Invasion waves in the biochemical warfare between living organisms. Physical review. E, 97(4), 042403. https://doi.org/10.1103/PhysRevE.97.042403
Carvalho, S A, and Martins, M L. "Invasion waves in the biochemical warfare between living organisms." Physical review. E vol. 97,4 (2018): 042403. doi: https://doi.org/10.1103/PhysRevE.97.042403
Carvalho SA, Martins ML. Invasion waves in the biochemical warfare between living organisms. Phys Rev E. 2018 Apr;97(4):042403. doi: 10.1103/PhysRevE.97.042403. PMID: 29758635.
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Importance of Recalibrating Models for Type 2 Diabetes Onset Prediction: Application of the Diabetes Population Risk Tool on the Health and Retirement Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

Vettoretti M, Longato E, Camillo BD, Facchinetti A.
PMID: 30441547
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:5358-5361. doi: 10.1109/EMBC.2018.8513554.

A timely prediction of type 2 diabetes (T2D) onset is important for early intervention to prevent, or at least postpone, its incidence. Several models to predict T2D onset according to individual risk factors were proposed. However, their practical applicability...

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Vettoretti M, Longato E, Camillo BD, et al. Importance of Recalibrating Models for Type 2 Diabetes Onset Prediction: Application of the Diabetes Population Risk Tool on the Health and Retirement Study. Annu Int Conf IEEE Eng Med Biol Soc. 2018;2018:5358-5361doi: 10.1109/EMBC.2018.8513554.
Vettoretti, M., Longato, E., Camillo, B. D., & Facchinetti, A. (2018). Importance of Recalibrating Models for Type 2 Diabetes Onset Prediction: Application of the Diabetes Population Risk Tool on the Health and Retirement Study. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 20185358-5361. https://doi.org/10.1109/EMBC.2018.8513554
Vettoretti, Martina, et al. "Importance of Recalibrating Models for Type 2 Diabetes Onset Prediction: Application of the Diabetes Population Risk Tool on the Health and Retirement Study." Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference vol. 2018 (2018): 5358-5361. doi: https://doi.org/10.1109/EMBC.2018.8513554
Vettoretti M, Longato E, Camillo BD, Facchinetti A. Importance of Recalibrating Models for Type 2 Diabetes Onset Prediction: Application of the Diabetes Population Risk Tool on the Health and Retirement Study. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:5358-5361. doi: 10.1109/EMBC.2018.8513554. PMID: 30441547.
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Issues in data expansion in understanding criticality in biological systems.

The European physical journal. E, Soft matter

Wasnik V.
PMID: 29380087
Eur Phys J E Soft Matter. 2018 Jan 31;41(1):13. doi: 10.1140/epje/i2018-11621-0.

At the point of a second-order phase transition also termed as a critical point, systems display long-range order and their macroscopic behaviors are independent of the microscopic details making up the system. Due to these properties, it has long...

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Wasnik V. Issues in data expansion in understanding criticality in biological systems. Eur Phys J E Soft Matter. 2018;41(1):13doi: 10.1140/epje/i2018-11621-0.
Wasnik, V. (2018). Issues in data expansion in understanding criticality in biological systems. The European physical journal. E, Soft matter, 41(1), 13. https://doi.org/10.1140/epje/i2018-11621-0
Wasnik, Vaibhav. "Issues in data expansion in understanding criticality in biological systems." The European physical journal. E, Soft matter vol. 41,1 (2018): 13. doi: https://doi.org/10.1140/epje/i2018-11621-0
Wasnik V. Issues in data expansion in understanding criticality in biological systems. Eur Phys J E Soft Matter. 2018 Jan 31;41(1):13. doi: 10.1140/epje/i2018-11621-0. PMID: 29380087.
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Undulations Drive Domain Registration from the Two Membrane Leaflets.

Biophysical journal

Galimzyanov TR, Kuzmin PI, Pohl P, Akimov SA.
PMID: 28122219
Biophys J. 2017 Jan 24;112(2):339-345. doi: 10.1016/j.bpj.2016.12.023.

Phase separation in biological membranes plays an important role in protein targeting and transmembrane signaling. Its occurrence in both membrane leaflets commonly gives rise to matching liquid or liquid-ordered domains in the opposing monolayers. The underlying mechanism of such...

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Galimzyanov TR, Kuzmin PI, Pohl P, et al. Undulations Drive Domain Registration from the Two Membrane Leaflets. Biophys J. 2017;112(2):339-345doi: 10.1016/j.bpj.2016.12.023.
Galimzyanov, T. R., Kuzmin, P. I., Pohl, P., & Akimov, S. A. (2017). Undulations Drive Domain Registration from the Two Membrane Leaflets. Biophysical journal, 112(2), 339-345. https://doi.org/10.1016/j.bpj.2016.12.023
Galimzyanov, Timur R, et al. "Undulations Drive Domain Registration from the Two Membrane Leaflets." Biophysical journal vol. 112,2 (2017): 339-345. doi: https://doi.org/10.1016/j.bpj.2016.12.023
Galimzyanov TR, Kuzmin PI, Pohl P, Akimov SA. Undulations Drive Domain Registration from the Two Membrane Leaflets. Biophys J. 2017 Jan 24;112(2):339-345. doi: 10.1016/j.bpj.2016.12.023. PMID: 28122219; PMCID: PMC5266264.
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Modelling the effects of global climate change on Chikungunya transmission in the 21.

Scientific reports

Tjaden NB, Suk JE, Fischer D, Thomas SM, Beierkuhnlein C, Semenza JC.
PMID: 28630444
Sci Rep. 2017 Jun 19;7(1):3813. doi: 10.1038/s41598-017-03566-3.

The arrival and rapid spread of the mosquito-borne viral disease Chikungunya across the Americas is one of the most significant public health developments of recent years, preceding and mirroring the subsequent spread of Zika. Globalization in trade and travel...

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Tjaden NB, Suk JE, Fischer D, et al. Modelling the effects of global climate change on Chikungunya transmission in the 21. Sci Rep. 2017;7(1):3813doi: 10.1038/s41598-017-03566-3.
Tjaden, N. B., Suk, J. E., Fischer, D., Thomas, S. M., Beierkuhnlein, C., & Semenza, J. C. (2017). Modelling the effects of global climate change on Chikungunya transmission in the 21. Scientific reports, 7(1), 3813. https://doi.org/10.1038/s41598-017-03566-3
Tjaden, Nils B, et al. "Modelling the effects of global climate change on Chikungunya transmission in the 21." Scientific reports vol. 7,1 (2017): 3813. doi: https://doi.org/10.1038/s41598-017-03566-3
Tjaden NB, Suk JE, Fischer D, Thomas SM, Beierkuhnlein C, Semenza JC. Modelling the effects of global climate change on Chikungunya transmission in the 21. Sci Rep. 2017 Jun 19;7(1):3813. doi: 10.1038/s41598-017-03566-3. PMID: 28630444; PMCID: PMC5476675.
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Structural and practical identifiability analysis of S-system.

IET systems biology

Zhan C, Li BY, Yeung LF.
PMID: 26577163
IET Syst Biol. 2015 Dec;9(6):285-93. doi: 10.1049/iet-syb.2015.0014.

In the field of systems biology, biological reaction networks are usually modelled by ordinary differential equations. A sub-class, the S-systems representation, is a widely used form of modelling. Existing S-systems identification techniques assume that the system itself is always...

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Zhan C, Li BY, Yeung LF. Structural and practical identifiability analysis of S-system. IET Syst Biol. 2015;9(6):285-93doi: 10.1049/iet-syb.2015.0014.
Zhan, C., Li, B. Y., & Yeung, L. F. (2015). Structural and practical identifiability analysis of S-system. IET systems biology, 9(6), 285-93. https://doi.org/10.1049/iet-syb.2015.0014
Zhan, Choujun, et al. "Structural and practical identifiability analysis of S-system." IET systems biology vol. 9,6 (2015): 285-93. doi: https://doi.org/10.1049/iet-syb.2015.0014
Zhan C, Li BY, Yeung LF. Structural and practical identifiability analysis of S-system. IET Syst Biol. 2015 Dec;9(6):285-93. doi: 10.1049/iet-syb.2015.0014. PMID: 26577163.
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Outstanding Challenges in the Transferability of Ecological Models.

Trends in ecology & evolution

Yates KL, Bouchet PJ, Caley MJ, Mengersen K, Randin CF, Parnell S, Fielding AH, Bamford AJ, Ban S, Barbosa AM, Dormann CF, Elith J, Embling CB, Ervin GN, Fisher R, Gould S, Graf RF, Gregr EJ, Halpin PN, Heikkinen RK, Heinänen S, Jones AR, Krishnakumar PK, Lauria V, Lozano-Montes H, Mannocci L, Mellin C, Mesgaran MB, Moreno-Amat E, Mormede S, Novaczek E, Oppel S, Ortuño Crespo G, Peterson AT, Rapacciuolo G, Roberts JJ, Ross RE, Scales KL, Schoeman D, Snelgrove P, Sundblad G, Thuiller W, Torres LG, Verbruggen H, Wang L, Wenger S, Whittingham MJ, Zharikov Y, Zurell D, Sequeira AMM.
PMID: 30166069
Trends Ecol Evol. 2018 Oct;33(10):790-802. doi: 10.1016/j.tree.2018.08.001. Epub 2018 Aug 27.

Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions....

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Yates KL, Bouchet PJ, Caley MJ, et al. Outstanding Challenges in the Transferability of Ecological Models. Trends Ecol Evol. 2018;33(10):790-802doi: 10.1016/j.tree.2018.08.001.
Yates, K. L., Bouchet, P. J., Caley, M. J., Mengersen, K., Randin, C. F., Parnell, S., Fielding, A. H., Bamford, A. J., Ban, S., Barbosa, A. M., Dormann, C. F., Elith, J., Embling, C. B., Ervin, G. N., Fisher, R., Gould, S., Graf, R. F., Gregr, E. J., Halpin, P. N., Heikkinen, R. K., Heinänen, S., Jones, A. R., Krishnakumar, P. K., Lauria, V., Lozano-Montes, H., Mannocci, L., Mellin, C., Mesgaran, M. B., Moreno-Amat, E., Mormede, S., Novaczek, E., Oppel, S., Ortuño Crespo, G., Peterson, A. T., Rapacciuolo, G., Roberts, J. J., Ross, R. E., Scales, K. L., Schoeman, D., Snelgrove, P., Sundblad, G., Thuiller, W., Torres, L. G., Verbruggen, H., Wang, L., Wenger, S., Whittingham, M. J., Zharikov, Y., Zurell, D., & Sequeira, A. M. M. (2018). Outstanding Challenges in the Transferability of Ecological Models. Trends in ecology & evolution, 33(10), 790-802. https://doi.org/10.1016/j.tree.2018.08.001
Yates, Katherine L, et al. "Outstanding Challenges in the Transferability of Ecological Models." Trends in ecology & evolution vol. 33,10 (2018): 790-802. doi: https://doi.org/10.1016/j.tree.2018.08.001
Yates KL, Bouchet PJ, Caley MJ, Mengersen K, Randin CF, Parnell S, Fielding AH, Bamford AJ, Ban S, Barbosa AM, Dormann CF, Elith J, Embling CB, Ervin GN, Fisher R, Gould S, Graf RF, Gregr EJ, Halpin PN, Heikkinen RK, Heinänen S, Jones AR, Krishnakumar PK, Lauria V, Lozano-Montes H, Mannocci L, Mellin C, Mesgaran MB, Moreno-Amat E, Mormede S, Novaczek E, Oppel S, Ortuño Crespo G, Peterson AT, Rapacciuolo G, Roberts JJ, Ross RE, Scales KL, Schoeman D, Snelgrove P, Sundblad G, Thuiller W, Torres LG, Verbruggen H, Wang L, Wenger S, Whittingham MJ, Zharikov Y, Zurell D, Sequeira AMM. Outstanding Challenges in the Transferability of Ecological Models. Trends Ecol Evol. 2018 Oct;33(10):790-802. doi: 10.1016/j.tree.2018.08.001. Epub 2018 Aug 27. PMID: 30166069.
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Feature Weight Driven Interactive Mutual Information Modeling for Heterogeneous Bio-Signal Fusion to Estimate Mental Workload.

Sensors (Basel, Switzerland)

Zhang P, Wang X, Chen J, You W.
PMID: 29023364
Sensors (Basel). 2017 Oct 12;17(10). doi: 10.3390/s17102315.

Many people suffer from high mental workload which may threaten human health and cause serious accidents. Mental workload estimation is especially important for particular people such as pilots, soldiers, crew and surgeons to guarantee the safety and security. Different...

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Zhang P, Wang X, Chen J, et al. Feature Weight Driven Interactive Mutual Information Modeling for Heterogeneous Bio-Signal Fusion to Estimate Mental Workload. Sensors (Basel). 2017;17(10)doi: 10.3390/s17102315.
Zhang, P., Wang, X., Chen, J., & You, W. (2017). Feature Weight Driven Interactive Mutual Information Modeling for Heterogeneous Bio-Signal Fusion to Estimate Mental Workload. Sensors (Basel, Switzerland), 17(10), . https://doi.org/10.3390/s17102315
Zhang, Pengbo, et al. "Feature Weight Driven Interactive Mutual Information Modeling for Heterogeneous Bio-Signal Fusion to Estimate Mental Workload." Sensors (Basel, Switzerland) vol. 17,10 (2017). doi: https://doi.org/10.3390/s17102315
Zhang P, Wang X, Chen J, You W. Feature Weight Driven Interactive Mutual Information Modeling for Heterogeneous Bio-Signal Fusion to Estimate Mental Workload. Sensors (Basel). 2017 Oct 12;17(10). doi: 10.3390/s17102315. PMID: 29023364; PMCID: PMC5677372.
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Showing 1045 to 1056 of 1077 entries