Computational gastronomy: web development and database creation

Abstract

In this project, we have developed a comprehensive database and web platform for multiclass toxin protiens and peptides by integrating data from trusted and reputed sources such as Arach noServer and UniProt. The objective of our project is to provide easy access to efficiently predict the toxicity of the peptide using Sequence to the facilitating research and development in related fields. we started from collecting and classifying the details information about various toxin pro tein,we gathered over 7,000 data entries, which were then filtered and classified into five main categories: Neurotoxin, Cytotoxin, Hemotoxin, Enterotoxin, and Cardiotoxin. We used the MERN (MongoDB, Express.js, React.js, Node.js) stack, we created a dynamic website that al lows users to search and retrieve toxin information based on various criteria. We integrated a machine learning ensemble model with an accuracy of 91.05% on training data and 87.36% on testing data to predict the toxicity of peptide sequences. This tool enhances the platform’s utility by providing predictive insights into sequences not already present in the database. This Implimentation holds a significant resource for researchers and advance the understanding of toxin peptide.