Plant Protein Classification Using K-mer Encoding

Proteins play an important role in the human body and in plants. A lack of expertise in protein labeling in plants can make it extremely difficult to characterize and comprehend the precise roles and activities of different proteins. Furthermore, it restricts development in fields like biotechnology, disease resistance, and crop enhancement. The presented project focuses on plant protein classification, aiming to overcome the challenges arising from limited protein labeling knowledge. Advanced machine learning techniques, including various classification algorithms such as Logistic Regression, Decision Tree, K-nearest neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), Multinomial Naive Bayes (NB), AdaBoost, and XGBoost, are employed to accurately classify protein sequences into their respective families. This classification approach provides valuable insights into the functions and roles of proteins within plants, ultimately advancing our understanding of plant biology. This attempt offers new possibilities for advancement in critical sectors such as agriculture, drug discovery, and genomic research by eliminating the limitations associated with limited protein labeling knowledge.