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Photoinactivation of δ-aminolevulinic acid dehydratase from maize by flavin mononuclotide.

Photosynthesis research

Rao S, Kamath G, Maralihalli G, Bhagwat AS.
PMID: 24435638
Photosynth Res. 1987 Jan;12(2):155-64. doi: 10.1007/BF00047945.

The δ-aminolevulinic acid dehydratase activity was irreversibly inactivated by irradiation of the enzyme in presence of flavin mononucleotide. The loss of enzyme activity was dependent on time of irradiation, concentration of FMN and intensity of irradiance. It required oxygen...

Cite
Rao S, Kamath G, Maralihalli G, et al. Photoinactivation of δ-aminolevulinic acid dehydratase from maize by flavin mononuclotide. Photosynth Res. 1987;12(2):155-64doi: 10.1007/BF00047945.
Rao, S., Kamath, G., Maralihalli, G., & Bhagwat, A. S. (1987). Photoinactivation of δ-aminolevulinic acid dehydratase from maize by flavin mononuclotide. Photosynthesis research, 12(2), 155-64. https://doi.org/10.1007/BF00047945
Rao, S, et al. "Photoinactivation of δ-aminolevulinic acid dehydratase from maize by flavin mononuclotide." Photosynthesis research vol. 12,2 (1987): 155-64. doi: https://doi.org/10.1007/BF00047945
Rao S, Kamath G, Maralihalli G, Bhagwat AS. Photoinactivation of δ-aminolevulinic acid dehydratase from maize by flavin mononuclotide. Photosynth Res. 1987 Jan;12(2):155-64. doi: 10.1007/BF00047945. PMID: 24435638.
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LipGene: Lipschitz continuity guided adaptive learning rates for fast convergence on Microarray Expression Data Sets.

IEEE/ACM transactions on computational biology and bioinformatics

Prashanth T, Saha S, Basarkod S, Aralihalli S, Dhavala SS, Saha S, Aduri R.
PMID: 34495836
IEEE/ACM Trans Comput Biol Bioinform. 2021 Sep 08; doi: 10.1109/TCBB.2021.3110516. Epub 2021 Sep 08.

Hyperparameter tuning, specifically tuning of the learning rate, can often be a time-consuming process, especially when dealing with large data sets. A mathematical foundation in the choice of learning rate can minimize tuning efforts. We propose the application of...

Cite
Prashanth T, Saha S, Basarkod S, et al. LipGene: Lipschitz continuity guided adaptive learning rates for fast convergence on Microarray Expression Data Sets. IEEE/ACM Trans Comput Biol Bioinform. 2021;doi: 10.1109/TCBB.2021.3110516.
Prashanth, T., Saha, S., Basarkod, S., Aralihalli, S., Dhavala, S. S., Saha, S., & Aduri, R. (2021). LipGene: Lipschitz continuity guided adaptive learning rates for fast convergence on Microarray Expression Data Sets. IEEE/ACM transactions on computational biology and bioinformatics, . https://doi.org/10.1109/TCBB.2021.3110516
Prashanth, Tejas, et al. "LipGene: Lipschitz continuity guided adaptive learning rates for fast convergence on Microarray Expression Data Sets." IEEE/ACM transactions on computational biology and bioinformatics vol. (2021). doi: https://doi.org/10.1109/TCBB.2021.3110516
Prashanth T, Saha S, Basarkod S, Aralihalli S, Dhavala SS, Saha S, Aduri R. LipGene: Lipschitz continuity guided adaptive learning rates for fast convergence on Microarray Expression Data Sets. IEEE/ACM Trans Comput Biol Bioinform. 2021 Sep 08; doi: 10.1109/TCBB.2021.3110516. Epub 2021 Sep 08. PMID: 34495836.
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