- author = "Mohana Naga Vamsi and Allam Apparao",
- title = "Genetic Programming for Gene Regulatory Networks: {IRMA} Data Analysis",
- title2 = "Decoding the Secrets of Gene Regulation: A Cutting-Edge Genetic Programming Approach",
- year = "2023",
- address = "Moldova",
- month = "4 " # aug,
- publisher = "LAP Lambert Academic Publishing",
- keywords = "genetic algorithms, genetic programming, RPN",
- isbn13 = "978-6206766223",
-
URL = "
https://www.amazon.com/Genetic-Programming-Gene-Regulatory-Networks/dp/B0CGT6GJ34",
-
URL = "https://my.lap-publishing.com/catalog/details//store/gb/book/978-620-6-76622-3/genetic-programming-for-gene-regulatory-networks:-irma-data-analysis",
- size = "136 pages",
- abstract = "the application of Computational Intelligence techniques in biology, particularly in understanding gene regulatory networks and homeostatic regulation. These techniques, such as genetic algorithms and genetic programming, play a crucial role in analyzing gene expression data and inferring regulatory networks. Polish notation to fit ordinary differential equation models to gene regulatory systems based on time series gene expression data. The algorithm shows promising results in inferring gene regulatory networks in both Switch ON and Switch OFF conditions. It outperforms existing state-of-the-art methods like BANJO, NIR+TSNI, and GRNGen in terms of precision, sensitivity, and accuracy metrics (F-score). also applied to infer the gene regulatory network of the GABA signalling family in the Rat Central Nervous System.",
-
notes = "
See also \cite{Vamsi:2015:IJCA}",
