A Computational Method for Predicting Tumour Cells in

Arachis hypogaea root nodules are specialized structures that entail complex plant-microbe interactions in processes such as stress responses and transcriptional regulation. The root nodules of Arachis hypogaea entail complex cellular proliferation and differentiation, and in certain stress conditions, the growth pat-terns may be similar to those of tumors. The molecular processes that mediate these changes are of equal importance in improving legume productivity as they are in the conceptual framework of abnormal cell proliferation in higher organisms.Despite some progress in the area of transcriptomics, the present bioin-formatics tools for the analysis of differentially expressed genes (DEGs) and non-coding RNAs (ncRNAs) are still heavily reliant on static RNA-Seq data and do not take into account morphological data. This study aims to introduce a two-step approach to combine transcriptomic and morphological data effectively for improved prediction of abnormal growth patterns in peanut root nodules. Phase I of this study will include the analysis of RNA-Seq data obtained from root nodules under environmental stress and nodulation pro-cesses using standardized pipelines such as HISAT2 for alignment, StringTie for transcript assembly, and DESeq2 for differential expression analysis. Simultaneously, plant morphological characteristics will be as-sessed through imaging and sensor analysis to record growth. Phase II will concentrate on the establishment of a predictive computational model that integrates gene expression profiles with quantitative morphological parameters. Supervised machine learning algorithms will be trained on labeled data sets generated from transcriptomic profiles and quantitative morphological parameters to establish patterns linked with tumor-like growth patterns. Preliminary results obtained from Phase I suggest the existence of stress-mediated transcriptional processes involving genes linked with cell cycle regulation and signaling pathways. By integrating molecular and imaging data sets in a single analytical platform, this research aims to improve the detection of complex growth anomalies. The current research study contributes greatly to the comprehension of plant developmental regulation under stress conditions and provides a computational model that may provide additional insights into the mechanisms of abnormal cellular proliferation.