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Application of cellular automata approach for cloud simulation and rendering.

Chaos (Woodbury, N.Y.)

Christopher Immanuel W, Paul Mary Deborrah S, Samuel Selvaraj R.
PMID: 24697387
Chaos. 2014 Mar;24(1):013125. doi: 10.1063/1.4866854.

Current techniques for creating clouds in games and other real time applications produce static, homogenous clouds. These clouds, while viable for real time applications, do not exhibit an organic feel that clouds in nature exhibit. These clouds, when viewed...

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Christopher Immanuel W, Paul Mary Deborrah S, Samuel Selvaraj R. Application of cellular automata approach for cloud simulation and rendering. Chaos. 2014;24(1):013125doi: 10.1063/1.4866854.
Christopher Immanuel, W., Paul Mary Deborrah, S., & Samuel Selvaraj, R. (2014). Application of cellular automata approach for cloud simulation and rendering. Chaos (Woodbury, N.Y.), 24(1), 013125. https://doi.org/10.1063/1.4866854
Christopher Immanuel, W, et al. "Application of cellular automata approach for cloud simulation and rendering." Chaos (Woodbury, N.Y.) vol. 24,1 (2014): 013125. doi: https://doi.org/10.1063/1.4866854
Christopher Immanuel W, Paul Mary Deborrah S, Samuel Selvaraj R. Application of cellular automata approach for cloud simulation and rendering. Chaos. 2014 Mar;24(1):013125. doi: 10.1063/1.4866854. PMID: 24697387.
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Prediction of kinase-inhibitor binding affinity using energetic parameters.

Bioinformation

Usha S, Selvaraj S.
PMID: 28149052
Bioinformation. 2016 Jun 15;12(3):172-181. doi: 10.6026/97320630012172. eCollection 2016.

The combination of physicochemical properties and energetic parameters derived from protein-ligand complexes play a vital role in determining the biological activity of a molecule. In the present work, protein-ligand interaction energy along with logP values was used to predict...

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Usha S, Selvaraj S. Prediction of kinase-inhibitor binding affinity using energetic parameters. Bioinformation. 2016;12(3):172-181doi: 10.6026/97320630012172.
Usha, S., & Selvaraj, S. (2016). Prediction of kinase-inhibitor binding affinity using energetic parameters. Bioinformation, 12(3), 172-181. https://doi.org/10.6026/97320630012172
Usha, Singaravelu, and Selvaraj, Samuel. "Prediction of kinase-inhibitor binding affinity using energetic parameters." Bioinformation vol. 12,3 (2016): 172-181. doi: https://doi.org/10.6026/97320630012172
Usha S, Selvaraj S. Prediction of kinase-inhibitor binding affinity using energetic parameters. Bioinformation. 2016 Jun 15;12(3):172-181. doi: 10.6026/97320630012172. eCollection 2016. PMID: 28149052; PMCID: PMC5267961.
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Better theoretical models and protein design experiments can help to understand protein folding.

Journal of natural science, biology, and medicine

Saravanan KM, Selvaraj S.
PMID: 25810661
J Nat Sci Biol Med. 2015 Jan-Jun;6(1):202-4. doi: 10.4103/0976-9668.149122.

In our study, we have concluded that two proteins with 88% homology choose different energetically favorable pathways in the very early stage of the folding process to attain their native folds. Subsequent reports from other investigators by performing folding...

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Saravanan KM, Selvaraj S. Better theoretical models and protein design experiments can help to understand protein folding. J Nat Sci Biol Med. 2015;6(1):202-4doi: 10.4103/0976-9668.149122.
Saravanan, K. M., & Selvaraj, S. (2015). Better theoretical models and protein design experiments can help to understand protein folding. Journal of natural science, biology, and medicine, 6(1), 202-4. https://doi.org/10.4103/0976-9668.149122
Saravanan, Konda Mani, and Selvaraj, Samuel. "Better theoretical models and protein design experiments can help to understand protein folding." Journal of natural science, biology, and medicine vol. 6,1 (2015): 202-4. doi: https://doi.org/10.4103/0976-9668.149122
Saravanan KM, Selvaraj S. Better theoretical models and protein design experiments can help to understand protein folding. J Nat Sci Biol Med. 2015 Jan-Jun;6(1):202-4. doi: 10.4103/0976-9668.149122. PMID: 25810661; PMCID: PMC4367036.
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HPREP: a comprehensive database for human proteome repeats.

Journal of integrative bioinformatics

Mary Rajathei D, Parthasarathy S, Selvaraj S.
PMID: 33136065
J Integr Bioinform. 2020 Nov 03; doi: 10.1515/jib-2020-0024. Epub 2020 Nov 03.

Amino acid repeats are found to play important roles in both structures and functions of the proteins. These are commonly found in all kingdoms of life, especially in eukaryotes and a larger fraction of human proteins composed of repeats....

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Mary Rajathei D, Parthasarathy S, Selvaraj S. HPREP: a comprehensive database for human proteome repeats. J Integr Bioinform. 2020;doi: 10.1515/jib-2020-0024.
Mary Rajathei, D., Parthasarathy, S., & Selvaraj, S. (2020). HPREP: a comprehensive database for human proteome repeats. Journal of integrative bioinformatics, . https://doi.org/10.1515/jib-2020-0024
Mary Rajathei, David, et al. "HPREP: a comprehensive database for human proteome repeats." Journal of integrative bioinformatics vol. (2020). doi: https://doi.org/10.1515/jib-2020-0024
Mary Rajathei D, Parthasarathy S, Selvaraj S. HPREP: a comprehensive database for human proteome repeats. J Integr Bioinform. 2020 Nov 03; doi: 10.1515/jib-2020-0024. Epub 2020 Nov 03. PMID: 33136065.
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Identification and Analysis of Long Repeats of Proteins at the Domain Level.

Frontiers in bioengineering and biotechnology

Rajathei DM, Parthasarathy S, Selvaraj S.
PMID: 31649924
Front Bioeng Biotechnol. 2019 Oct 08;7:250. doi: 10.3389/fbioe.2019.00250. eCollection 2019.

Amino acid repeats play an important role in the structure and function of proteins. Analysis of long repeats in protein sequences enables one to understand their abundance, structure and function in the protein universe. In the present study, amino...

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Rajathei DM, Parthasarathy S, Selvaraj S. Identification and Analysis of Long Repeats of Proteins at the Domain Level. Front Bioeng Biotechnol. 2019;7:250doi: 10.3389/fbioe.2019.00250.
Rajathei, D. M., Parthasarathy, S., & Selvaraj, S. (2019). Identification and Analysis of Long Repeats of Proteins at the Domain Level. Frontiers in bioengineering and biotechnology, 7250. https://doi.org/10.3389/fbioe.2019.00250
Rajathei, David Mary, et al. "Identification and Analysis of Long Repeats of Proteins at the Domain Level." Frontiers in bioengineering and biotechnology vol. 7 (2019): 250. doi: https://doi.org/10.3389/fbioe.2019.00250
Rajathei DM, Parthasarathy S, Selvaraj S. Identification and Analysis of Long Repeats of Proteins at the Domain Level. Front Bioeng Biotechnol. 2019 Oct 08;7:250. doi: 10.3389/fbioe.2019.00250. eCollection 2019. PMID: 31649924; PMCID: PMC6795024.
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An alphabetic code based atomic level molecular similarity search in databases.

Bioinformation

Saranya N, Selvaraj S.
PMID: 22829718
Bioinformation. 2012;8(11):498-503. doi: 10.6026/97320630008498. Epub 2012 Jun 16.

Atomic level molecular similarity and diversity studies have gained considerable importance through their wide application in Bioinformatics and Chemo-informatics for drug design. The availability of large volumes of data on chemical compounds requires new methodologies for efficient and effective...

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Saranya N, Selvaraj S. An alphabetic code based atomic level molecular similarity search in databases. Bioinformation. 2012;8(11):498-503doi: 10.6026/97320630008498.
Saranya, N., & Selvaraj, S. (2012). An alphabetic code based atomic level molecular similarity search in databases. Bioinformation, 8(11), 498-503. https://doi.org/10.6026/97320630008498
Saranya, Nallusamy, and Selvaraj, Samuel. "An alphabetic code based atomic level molecular similarity search in databases." Bioinformation vol. 8,11 (2012): 498-503. doi: https://doi.org/10.6026/97320630008498
Saranya N, Selvaraj S. An alphabetic code based atomic level molecular similarity search in databases. Bioinformation. 2012;8(11):498-503. doi: 10.6026/97320630008498. Epub 2012 Jun 16. PMID: 22829718; PMCID: PMC3398777.
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Showing 1 to 6 of 6 entries