A multi-objective hybrid machine learning approach-based optimization for enhanced biomass and bioactive phycobiliproteins production in Nostoc sp. CCC-403

MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms

Kumar, S., Stecher, G., Li, M., Knyaz, C., & Tamura, K. (2018) · Molecular Biology and Evolution · doi:10.1093/molbev/msy096 · Cited by 37,683 · see citing works

KEGG for representation and analysis of molecular networks involving diseases and drugs

Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., & Hirakawa, M. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp896 · Cited by 2,322 · see citing works

antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences

Medema, M. H., Blin, K., Cimermančič, P., de Jager, V., Zakrzewski, P., Fischbach, M. A., Weber, T., Eriko Takano, A., & Rainer Breitling, A. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr466 · Cited by 2,013 · see citing works

Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology

Karp, P. D., Paley, S. M., Krummenacker, M., Latendresse, M., Dale, J. M., Lee, T. J., Kaipa, P., Gilham, F., Spaulding, A., Popescu, L., et al. (2010) · Briefings in Bioinformatics · doi:10.1093/bib/bbp043 · Cited by 694 · see citing works

MetaCyc: a multiorganism database of metabolic pathways and enzymes

Caspi, R. R. (2005) · Nucleic Acids Research · doi:10.1093/nar/gkj128 · Cited by 620 · see citing works

Cyanobacterial Phycobilisomes

MacColl, R. (1998) · Journal of Structural Biology · doi:10.1006/jsbi.1998.4062 · Cited by 618 · see citing works

Effects of UV radiation on aquatic ecosystems and interactions with other environmental factors

Häder, D., Williamson, C. E., Wängberg, S., Rautio, M., Rose, K. C., Gao, K., Helbling, E. W., Sinha, R. P., & Worrest, R. C. (2014) · Photochemical & Photobiological Sciences · doi:10.1039/c4pp90035a · Cited by 422 · see citing works

OptFlux: an open-source software platform for in silico metabolic engineering

Rocha, I., Maia, P., Evangelista, P., Vilaça, P., Soares, S., Pinto, J. P., Nielsen, J., Patil, K. R., Ferreira, E. C., & Rocha, M. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-45 · Cited by 388 · see citing works

Availability and Utilization of Pigments from Microalgae

Begum, H., Yusoff, F. M., Banerjee, S., Khatoon, H., & Shariff, M. (2015) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2013.764841 · Cited by 380 · see citing works

Purification and characterization of C-Phycocyanin from cyanobacterial species of marine and freshwater habitat

Patel, A. B., Mishra, S., Pawar, R., & Ghosh, P. K. (2004) · Protein Expression and Purification · doi:10.1016/j.pep.2004.10.028 · Cited by 361 · see citing works

BRENDA, AMENDA and FRENDA the enzyme information system: new content and tools in 2009

Chang, A., Scheer, M. P., Grote, A., Schomburg, D., & Schomburg, D. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn820 · Cited by 357 · see citing works

Phytoplankton change in the North Atlantic

Reid, P. C., Edwards, M., Hunt, H. G., et al. (1998) · Nature · doi:10.1038/35290 · Cited by 318 · see citing works

Nutraceutical properties of phycocyanin

Fernández-Rojas, B., Hernández‐Juárez, J., & Pedraza‐Chaverrí, J. (2014) · Journal of Functional Foods · doi:10.1016/j.jff.2014.10.011 · Cited by 293 · see citing works

Phycocyanin and phycoerythrin: Strategies to improve production yield and chemical stability

Hsieh-Lo, M., Castillo-Herrera, G. A., Ochoa-Becerra, M. A., & Mojica, L. (2019) · Algal Research · doi:10.1016/j.algal.2019.101600 · Cited by 271 · see citing works

Is exploitation of microalgae economically and energetically sustainable?

Barsanti, L. A., & Gualtieri, P. (2018) · Algal Research · doi:10.1016/j.algal.2018.02.001 · Cited by 270 · see citing works

Phycobiliproteins: Molecular structure, production, applications, and prospects

Li, W., Su, H., Pu, Y., Chen, J., Liu, L., Liu, Q., & Qin, S. (2019) · Biotechnology Advances · doi:10.1016/j.biotechadv.2019.01.008 · Cited by 240 · see citing works

Ultrasound assisted biodiesel production from sesame (Sesamum indicum L.) oil using barium hydroxide as a heterogeneous catalyst: Comparative assessment of prediction abilities between response surface methodology (RSM) and artificial neural network (ANN)

Sarve, A. N., Sonawane, S. S., & Varma, M. N. (2015) · Ultrasonics Sonochemistry · doi:10.1016/j.ultsonch.2015.01.013 · Cited by 211 · see citing works

Backpropagation Learning Algorithm Based on Levenberg Marquardt Algorithm

Sapna, S. (2012) · doi:10.5121/csit.2012.2438 · Cited by 206 · see citing works

Cyanobacterial pigments: Perspectives and biotechnological approaches

Saini, D. K., Pabbi, S., & Shukla, P. (2018) · Food and Chemical Toxicology · doi:10.1016/j.fct.2018.08.002 · Cited by 184 · see citing works

Exercising hybrid statistical tools GA-RSM, GA-ANN and GA-ANFIS to optimize FDM process parameters for tensile strength improvement

Deshwal, S., Kumar, A., & Chhabra, D. (2020) · CIRP journal of manufacturing science and technology · doi:10.1016/j.cirpj.2020.05.009 · Cited by 184 · see citing works

On finding multiple Pareto-optimal solutions using classical and evolutionary generating methods

Shukla, P. K., & Deb, K. (2006) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.08.002 · Cited by 168 · see citing works

Engineered cyanobacteria with enhanced growth show increased ethanol production and higher biofuel to biomass ratio

Liang, F., Englund, E., Lindberg, P., & Lindblad, P. C. (2018) · Metabolic Engineering · doi:10.1016/j.ymben.2018.02.006 · Cited by 143 · see citing works

Enhancing production of microalgal biopigments through metabolic and genetic engineering

Saini, D. K., Chakdar, H., Pabbi, S., & Shukla, P. (2019) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2018.1533518 · Cited by 143 · see citing works

Screening of Cyanobacteria for Phycobiliproteins and Effect of Different Environmental Stress on Its Yield

Hemlata, H., & Fatma, T. (2009) · Bulletin of Environmental Contamination and Toxicology · doi:10.1007/s00128-009-9837-y · Cited by 134 · see citing works

Extraction, purification and characterization of phycoerythrin from Michrochaete and its biological activities

Hemlata, H., Afreen, S., & Fatma, T. (2017) · Biocatalysis and Agricultural Biotechnology · doi:10.1016/j.bcab.2017.11.012 · Cited by 109 · see citing works

Phycocyanin from Microalgae: Properties, Extraction and Purification, with Some Recent Applications

de Morais, M. G., Prates, D. D. F., Moreira, J. B., Duarte, J. H., & Costa, J. A. V. (2018) · Industrial Biotechnology · doi:10.1089/ind.2017.0009 · Cited by 109 · see citing works

Effects of different light source and media on growth and production of phycobiliprotein from freshwater cyanobacteria

Khatoon, H., Leong, L. K., Rahman, N. A., Mian, S., Begum, H., Banerjee, S., & Endut, A. (2017) · Bioresource Technology · doi:10.1016/j.biortech.2017.10.052 · Cited by 105 · see citing works

Physicochemical parameters optimization, and purification of phycobiliproteins from the isolated Nostoc sp.

Johnson, E. M., Kumar, K., & Das, D. (2014) · Bioresource Technology · doi:10.1016/j.biortech.2014.05.097 · Cited by 100 · see citing works

Effect of light quality on the C-phycoerythrin production in marine cyanobacteria Pseudanabaena sp. isolated from Gujarat coast, India

Mishra, S. K., Shrivastav, A., Maurya, R., Patidar, S. K., Haldar, S., & Mishra, S. (2011) · Protein Expression and Purification · doi:10.1016/j.pep.2011.08.011 · Cited by 97 · see citing works

Effects of different nitrogen sources and light paths of flat plate photobioreactors on the growth and lipid accumulation of Chlorella sp. GN1 outdoors

Feng, P., Xu, Z., Qin, L., Asraful Alam, M., Wang, Z., & Zhu, S. (2020) · Bioresource Technology · doi:10.1016/j.biortech.2020.122762 · Cited by 96 · see citing works

Optimization of medium components for increased production of C-phycocyanin from Phormidium ceylanicum and its purification by single step process

Singh, N., Parmar, A., & Madamwar, D. (2008) · Bioresource Technology · doi:10.1016/j.biortech.2008.09.021 · Cited by 93 · see citing works

Back propagation artificial neural network and central composite design modeling of operational parameter impact for sunset yellow and azur (II) adsorption onto MWCNT and MWCNT-Pd-NPs: Isotherm and kinetic study

Karimi, R., Yousefi, F., Ghaedi, M., et al. (2016) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2016.10.012 · Cited by 78 · see citing works

A simple method for rapid purification of phycobiliproteins from Arthrospira platensis and Porphyridium cruentum biomass

Lauceri, R., Zittelli, G. C., & Torzillo, G. (2019) · Algal Research · doi:10.1016/j.algal.2019.101685 · Cited by 73 · see citing works

Characterization and Optimization of Bioflocculant Exopolysaccharide Production by Cyanobacteria Nostoc sp. BTA97 and Anabaena sp. BTA990 in Culture Conditions

Tiwari, O. N., Khangembam, R., Shamjetshabam, M., Aribam, S. S., Oinam, G., & Brand, J. J. (2015) · Applied Biochemistry and Biotechnology · doi:10.1007/s12010-015-1691-2 · Cited by 63 · see citing works

Estimation of adsorption isotherm and mass transfer parameters in protein chromatography using artificial neural networks

Wang, G., Briskot, T., Hahn, T., et al. (2017) · Journal of Chromatography A · doi:10.1016/j.chroma.2017.01.068 · Cited by 58 · see citing works

Phycocyanin Exerts Anti-Proliferative Effects through Down-Regulating TIRAP/NF-κB Activity in Human Non-Small Cell Lung Cancer Cells

Hao, S., Li, S., Wang, J., et al. (2019) · Cells · doi:10.3390/cells8060588 · Cited by 58 · see citing works

Statistical optimization of culture media for production of phycobiliprotein by Synechocystis sp. PCC 6701

Hong, S.-J., & Lee, C. (2008) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-008-0154-9 · Cited by 48 · see citing works

Hydrothermal liquefaction of Nostoc ellipsosporum biomass grown in municipal wastewater under optimized conditions for bio-oil production

Devi, T. E., & Parthiban, R. (2020) · Bioresource Technology · doi:10.1016/j.biortech.2020.123943 · Cited by 48 · see citing works

Machine-learning-based simulation and fed-batch control of cyanobacterial-phycocyanin production in Plectonema by artificial neural network and deep reinforcement learning

Ma, Y., Noreña-Caro, D., Adams, A. J., et al. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.107016 · Cited by 47 · see citing works

Production of phycobiliprotein and carotenoid by efficient extraction from Thermosynechococcus sp. CL-1 cultivation in swine wastewater

Winayu, B. N. R., Lai, K. T., Hsueh, H.-T., & Chu, H. (2020) · Bioresource Technology · doi:10.1016/j.biortech.2020.124125 · Cited by 35 · see citing works

Differential Effects of Varying Concentrations of Phosphorus, Iron, and Nitrogen in N2-Fixing Cyanobacteria

Fernández‐Juárez, V., Bennasar-Figueras, A., Sureda, A., Ramis, G., & Agawin, N. S. R. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.541558 · Cited by 35 · see citing works

Phycobiliproteins from Anabaena variabilis CCC421 and its production enhancement strategies using combinatory evolutionary algorithm approach

Saini, D. K., Yadav, D., Pabbi, S., Chhabra, D., & Shukla, P. (2020) · Bioresource Technology · doi:10.1016/j.biortech.2020.123347 · Cited by 34 · see citing works

Elucidating the role of nutrients in C-phycocyanin production by the halophilic cyanobacterium Euhalothece sp.

Mogany, T., Swalaha, F. M., Kumari, S., & Bux, F. (2018) · Journal of Applied Phycology · doi:10.1007/s10811-018-1477-3 · Cited by 30 · see citing works

Microbial photosynthetic and photoprotective pigments in Himalayan soils originating from different elevations and successional stages

Řeháková, K., Čapková, K., Hrouzek, P., Koblížek, M., & Doležal, J. (2019) · Soil Biology and Biochemistry · doi:10.1016/j.soilbio.2019.02.008 · Cited by 19 · see citing works

Factorial optimization of upstream process for Cyanobium sp. pigments production

Pagels, F., Salvaterra, D., Amaro, H. M., Lopes, G., Sousa‐Pinto, I., Vasconcelos, V. M., & Guedes, A. C. (2020) · Journal of Applied Phycology · doi:10.1007/s10811-020-02260-8 · Cited by 19 · see citing works

Automation on the Generation of Genome-Scale Metabolic Models

Reyes, R. G., Gamermann, D., Montagud, A., Fuente, D., Triana, J., Urchueguía, J. F., & de Córdoba, P. F. (2012) · Journal of Computational Biology · doi:10.1089/cmb.2012.0183 · Cited by 16 · see citing works

Optimization of Collocated/Noncollocated Sensors and Actuators along with Feedback Gain Using Hybrid Multiobjective Genetic Algorithm-Artificial Neural Network

Chhabra, D., Bhushan, G., & Chandna, P. (2014) · Chinese journal of engineering · doi:10.1155/2014/692140 · Cited by 13 · see citing works

An integrated network visualization framework towards metabolic engineering applications

Noronha, A., Vilaça, P., & Rocha, M. (2014) · BMC Bioinformatics · doi:10.1186/s12859-014-0420-0 · Cited by 7 · see citing works

Artificial Intelligence and Machine Learning Tools for High-Performance Microalgal Wastewater Treatment and Algal Biorefinery: A Critical Review

Oruganti, R. K., Biji, A. P., Lanuyanger, T., Show, P. L., Sriariyanun, M., Upadhyayula, V. K. K., Gadhamshetty, V., & Bhattacharyya, D. (2023) · Science of The Total Environment · doi:10.1016/j.scitotenv.2023.162797 · Cited by 211 · see citing works

Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses

Imamoglu, E. (2024) · Bioengineering · doi:10.3390/bioengineering11111143 · Cited by 143 · see citing works

Real-Time Algal Monitoring Using Novel Machine Learning Approaches

Uguz, S., Sahin, Y. S., Kumar, P., Yang, X., & Anderson, G. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9060153 · Cited by 20 · see citing works

Microalgae biotechnology and its role in sustainable and healthy food design

Schneider, A. T., Machado, R. L. S., Dutra, D. A., Machado, E. F., Dias, R. R., Deprá, M. C., Zepka, L. Q., & Jacob-Lopes, E. (2025) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2025.1716473 · Cited by 15 · see citing works

Molecular composition of cyanobacterial phycobilisomes.

de Marsac, N. T., & Cohen-Bazire, G. (1977) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.74.4.1635 · Cited by 225 · see citing works

Structure and Function of Phycobilisomes

Mimuro, M., Kikuchi, H., & Murakami, A. (1999) · Springer eBooks · doi:10.1007/978-94-011-4832-0_5 · Cited by 13 · see citing works

Applications of Natural and Artificial Phycobiliproteins in Solar Cells

Ma, J., Chen, H., Qin, S., et al. (2015) · Current Biotechnology · doi:10.2174/2211550104666151016203528 · Cited by 11 · see citing works

Characterization, structure and function of linker polypeptides in phycobilisomes of cyanobacteria and red algae: An overview

Liu, L., Chen, X., Zhang, Y., & Zhou, B. (2005) · Biochimica et Biophysica Acta (BBA) - Bioenergetics · doi:10.1016/j.bbabio.2005.04.001 · Cited by 208 · see citing works

Phycocyanin Genes in the Cyanobacterium Synechocystis 6701 and a Potential Gene Rearrangement in a Pigment Variant

Anderson, L. K., & Grossman, A. R. (1987) · Springer eBooks · doi:10.1007/978-94-017-0519-6_172 · Cited by 3 · see citing works