References
- Schwab, J. D., S. D. Kühlwein, N. Ikonomi, M. Kühl, H. A. Kestler. Concepts in Boolean Network Modeling: What Do They All Mean? – Computational and Structural Biotechnology Journal, Vol. 18, 2020, pp. 571-582.
- Delgado, F. M., F. Gómez-Vela. Computational Methods for Gene Regulatory Networks Reconstruction and Analysis: A Review. – Artificial Intelligence in Medicine, Vol. 95, 2019, pp. 133-145.
- Milano, M., G. Agapito, M. Cannataro. Challenges and Limitations of Biological Network Analysis. – BioTech. (Basel), Vol. 11, 2022, No 3, 24.
- Golub, T. R., D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C D. Bloomfield, E. S. Lander. Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. – Science, Vol. 286, 1999, pp. 531-537.
- Furey, T. S., N. Cristianini, N. Duffy, D. W. Bednarski, M. Schummer, D. Haussler. Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data. – Bioinformatics, Vol 16, 2000, pp. 906-914.
- Khan, J., J. S. Wei, M. Ringner, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, P. S. Meltzer. Classification and Diagnostic Prediction of Cancers Using Gene Expression Profiling and an Artificial Neural Network. – Nature Medicine, Vol. 7, 2001, No 6, pp. 673-679.
- Chen, W., H. Lu, M. Wang. Gene Expression Data Classification Using Artificial Neural Network Ensembles Based on Samples Filtering. – International Conference on Artificial Intelligence and Computational Intelligence, Shanghai, China, 2009, pp. 626-628.
- Vanitha, C. D. A., D. Devaraj, M. Venkatesulu. Gene Expression Data Classification Using Support Vector Machine and Mutual Information-Based Gene Selection. – Procedia Computer Science, Vol. 47, 2015, pp. 13-21.
- Fan, L., K. L. Poh, P. Zhou. A Sequential Feature Extraction Approach for Naïve Bayes Classification of Microarray Data. – Expert Systems with Applications, Vol. 36, 2009, pp. 9919-9923.
- Fan, L., K. L. A Comparative Study of PCA, ICA, and Class-Conditional ICA for Naïve Bayes Classifier. – In: F. Sandoval, A. Prieto, J. Cabestany, M. Graña, Eds. Conference: Computational and Ambient Intelligence, Computational and Ambient Intelligence (IWANN), Lecture Notes in Computer Science. Vol. 4507. 2007, Berlin, Heidelberg, Springer, Poh. pp. 16-22. ISBN: 978-3-540-73006-4.
- Maulik, U., A. Mukhopadhyay, S. Bandyopadhyay. Combining Pareto-Optimal Clusters Using Supervised Learning for Identifying Co-Expressed Genes. – BMC Bioinformatics, Vol. 10, 2009, pp. 1-16.
- Mukhopadhyay, A., S. Bandyopadhyay, U. Maulik. Multi-Class Clustering of Cancer Subtypes through SVM-Based Ensemble of Pareto-Optimal Solutions for Gene Marker Identification. – PLoS One, Vol. 5, 2010, pp. 1-14.
- Bhuvaneswari, V., K. Vanitha. Classification of Microarray Gene Expression Data by Gene Combinations Using Fuzzy Logic (MGC-FL). – International Journal of Computer Science Engineering and Application, Vol. 2, 2012, pp. 79-98.
- Cilia, N. D., D. Stefano, C. F. Fontanella, S. Raimondo, A. Cotto. An Experimental Comparison of Feature-Selection and Classification Methods for Microarray Datasets. – Information, Vol. 10, 2019, No 3, pp. 1-13. DOI: 10.3390/info10030109.
- Lee, J., I. Choi, C. H. Jun. An Efficient Multivariate Feature Ranking Method for Gene Selection in High-Dimensional Microarray Data. – Expert Systems with Applications, Vol. 166, 2021, pp. 1-9.
- Helmy, M., R. Agrawal, J. Ali, M. Soudy, T. T. Bui, K. Selvarajoo. GeneCloudOmics: A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis. – Frontiers in Bioinformatics, Vol. 1, 2021, pp. 1-14.
- Widiharto, M., A. Soeleman, A. Syukur. Performance Improvement of Naïve Bayes Algorithm Based on Information Gain and Forward Selection Features Selection for Heart Disease Classification. – IOSR Journal of Computer Engineering, Vol. 24, 2022, No 3, pp. 69-79.
- Wahid, A., M. T. Banday. Classification of DNA Microarray Gene Expression Leukemia Data through the ABC and CNN Methods. – International Journal of Intelligent Systems and Application in Engineering, Vol. 11, 2023, No 75, pp. 119-131.
- Majumder, D. Application of Information Theory for Understanding of HLA Gene Regulation in Leukemia. – In: Advances in Computing & Information Technology, Advances in Intelligent Systems and Computing. Vol. 177. Berlin, Heidelberg, Springer, 2013, pp.161-173. ISBN: 978-3-642-31551-0.
- Das, B., D. Majumder. Maximum Entropy-Based Multivariate Dependence Analysis with a Case Study for HLA Gene Regulatory Network in Human Leukemia. – International Journal of Information Engineering, Vol. 3, 2013, No 4, pp. 137-142.
- Das, B., D. Majumder. Differences of HLA Gene Regulatory Network in Human Myeloid and Lymphoid Leukemias. – In: Proc. of International Conference on Bioinformatics and Systems Biology, 2018, pp. 165-169. DOI: 10.1109/BSB.2018.8770568.
- Jetka, T., K. Nienaltowski, S. Filippi, M. P. H. Stumpf, M. Komorowski. An Information-Theoretic Framework for Deciphering Pleiotropic and Noisy Biochemical Signaling. – Nature Communications, Vol. 9, 2018, No 4591, pp. 1-9.
- Martino, A. D., D. Martino. An Introduction to the Maximum Entropy Approach and Its Application to Inference Problems in Biology. – Heliyon, Vol. 4, 2018, No 4, pp. 1-33.
- Conforte, A. J., J. A. Tuszynski, F. D. Barbosa, N. Carels. Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine. – Frontiers in Genetics, Vol. 10, 2019, pp. 1-14.
- Karolak, A., S. Branciamore, J. S. McCune, P. P. Lee. Concepts and Applications of Information Theory to Immune-Oncology. – Trends in Cancer, Vol. 7, 2021, No 4, pp. 335-346.
- Billing, U., T. Jetka, L. Nortmann, N. Wundrack, M. Komorowski, S. Waldherr, F. Schaper, A. Dittrich. Robustness and Information Transfer within IL-6-Induced JAK/STAT Signaling. – Communications Biology, Vol. 2, 2019, No 27, pp. 1-14.
- Dixit, P. D., E. Lyashenko, M. Niepel, D. Vitkup. Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks. – Cell Systems, Vol. 10, 2020, No 2, pp. 204-212.
- Guo, Z., Y. Fu, C. Huang, C. Zheng, Z. Wu, X. Chen, S. Gao, Y. Ma, M. Shahen, Y. Li, P. Tu, J. Zhu, Z. Wang, W. Xiao, Y. Wang. NOGEA: A Network-Oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Respositioning. – Bioinformatics, Vol. 19, 2021, No 4, pp. 549-564.
- Ameri, A. J., Z. A. Lewis. Shannon Entropy as a Metric for Conditional Gene Expression in Neurospora Crassa. – G3 Genes| Genomes| Genetics, Vol. 11, 2021, No 4, pp. 1-7.
- Das, M., D. Majumder. Development of an Algorithm for Gene Expression Analysis through MaxEnt-Based Multivariate Information Theory. – In: International Conference on Intelligent Communication and Computational Techniques (ICCT’17), New York, New Jersey, IEEE, 2017, pp. 217-222. DOI: 10.1109/INTELCCT.2017.8324048.
- Greven, A., G. Keller, G. Warnecke. Entropy. Princeton, NJ, USA, Princeton University Press, 2014, 384 p.
- Demirel, Y., V. Gerbaud. Nonequilibrium Thermodynamics: Transport and Rate Processes in Physical, Chemical, and Biological Systems. – Amsterdam, The Netherlands, Elsevier, 2019.
- Jakimowicz, A. The Role of Entropy in the Development of Economics. – Entropy, Vol. 22, 2020, No 4, p. 452. DOI: 10.3390/e22040452.
- Rostaghi, M., H. Azam. Dispersion Entropy: A Measure for Time-Series Analysis. – IEEE Signal Processing Letters, Vol. 23, 2016, pp. 610-614.
- Reynar, J. C., A. Ratnaparkhi. A Maximum Entropy Approach to Identifying Sentence Boundaries. – In: Proc. of 5th Conference on Applied Natural Language Processing. Association for Computational Linguistics, Stroudsburg, PA, USA, 1997, pp. 16-19.
- Shannon, C. E. A Mathematical Theory of Communication. – The Bell System Technical Journal, Vol. 27, 1948, pp. 379-423.
- Petrov, I. I. Information Systems Reliability in Traditional Entropy and Novel Hierarchy. – Cybernetics and Information Technologies, Vol. 22, 2022, No 3, pp. 1-15.
- Majumder, D. HLA Expression in Leukemia: Status, Regulation & Therapeutic Implications of HLA Expression in Leukemia.– USA & UK: LAMBERT Academic Publishing GmbH & Co., Canada, India, Germany, 2012. ISBN: 978-3-8484-3247-9.
- Gibbs, J. W. Elementary Principles in Statistical Mechanics. – New York, Dover Publications, 1960 (Reprint of 1902). ISBN: 10: 0486607070.
- Das, B., D. Majumder. Information Theory-Based Analysis for Understanding the Regulation of HLA Gene Expression in Human Leukemia. – International Journal of Information Sciences and Techniques, Vol. 2, 2012, No 5, pp. 39-50.
- Bansall, M., V. Belcastro, A. A. Impiombato, D. D. Bernardo. How to Infer Gene Networks from Expression Profiles. – Molecular Systems Biology, EMBO, Vol. 3, 2007, No 78, pp. 1-10.
- Teschendorff, A. E., S. Severini. Increased Entropy of Signal Transduction in the Cancer Metastasis Phenotype. – BMC Systems Biology, Vol. 4, 2010, No 1, 104.
- Majumder, D., A. Mukherjee. A Passage through Systems Biology to Systems Medicine: Adoption of Middle-Out Rational Approaches towards the Understanding of Clinical Outcome in Cancer Therapy. – Analyst, Vol. 136, 2011, pp. 663-678.
- Majumder, D., A. Mukherjee. Multi-Scale Modeling Approaches in Systems Biology Towards the Assessment of Cancer Treatment Dynamics: Adoption of Middle-out Rationalist Approach. – In: Advances in Cancer: Research & Treatment, 2013, Article ID 587889.
- Wieringen, V., V. D. Vaart. Statistical Analysis of the Cancer Cell’s Molecular Entropy Using High-Throughput Data. – Bioinformatics, Vol. 27, 2011, No 4, pp. 556-563.
- Margolin, A. A., K. Wang, A. Califano, I. Nemenman. Multivariate Dependence and Genetic Networks Inference, – IET Systems Biology, Vol. 4, No 6, 2010, pp. 428-440.
- GNU Octave Wiki (Assessed on 06.05.2024). https://wiki.octave.org/Publications_using_Octave,
- Prinz, H. Numerical Methods for the Life Scientist: Binding and Enzyme Kinetics Calculated with GNU Octave and MATLAB. Springer, Heidelberg, Dordrecht, London, New York, 2011.
- Ranjan, M. K., K. Barot, V. Khairnar, V. Rawal, A. Pimpalgaonkar, S. Saxena, A. M. Sattar. Python: Empowering Data Science Applications and Research. – Journal of Operating Systems Development & Trends, Vol. 10, 2023, No 1, pp. 27-33.
- Singh, P., A. E. Oke, A. F. Kineber, O. I. Olanrewaju, O. Omole, M. S. Samsurijan, R. A. Ramli. A Mathematical Analysis of 4IR Innovation Barriers in Developmental Social Work – A Structural Equation Modeling Approach. – In: Article in Mathematics, Vol. 11, 2023, No 1003, pp. 1-20.
- West, J., G. Bianconi, S. Severini, A. E. Teschendorff. Differential Network Entropy Reveals Cancer System Hallmarks. – Scientific Reports. Vol. 2, 2012, No 1, p.802.
- Barnes, N. Publish Your Computer Code: It Is Good Enough. – Nature, Vol. 467, 2010, No 7317, 753.
- Roberts, M., D. Driggs, M. Thorpe et al. Common Pitfalls and Recommendations for Using Machine Learning to Detect and Prognosticate for COVID-19 Using Chest Radiographs and CT Scans. – Nature Machine Intelligence, Vol. 3, 2021, pp. 199-217.
