Predicting SARS-CoV-2 Protein Interactions: Insights from Machine Learning
Authors: Dadheech Nihal 
Published: September 2023

In our research journey, we undertook a comprehensive exploration of protein-protein interaction (PPI) prediction, with a primary focus on unraveling the intricate web of interactions involving the SARS-CoV-2 virus. Our research endeavor encompassed a multi-faceted approach that seamlessly integrated data preprocessing, feature engineering, the application of machine learning models, deep learning techniques, and extensive data visualization to gain profound insights into the complex realm of molecular interactions.The journey commenced with the acquisition of data from the IntAct database, a repository brimming with protein interaction information. However, before diving into analysis, rigorous data preprocessing was imperative. We meticulously scrubbed the data, eliminating undesired identifiers, and harnessed the power of regular expressions to extract and retain only the numeric values crucial for our predictive models. Feature engineering emerged as a pivotal step, allowing us to craft informative data representations conducive to effective model training. Our transformation of the "Confidence Value" variable, extracted from IntAct, into a structured and insightful feature set, was enhanced through one-hot encoding, facilitating our predictive endeavors. Our quest for predictive excellence took two distinct paths: traditional machine learning and deep learning. Utilizing a Random Forest Classifier, we explored classical machine learning methods, achieving commendable accuracy in predicting protein interactions. The introduction of Support Vector Machine (SVM) classifiers further underscored the robustness of our approach, reinforcing its potential in this critical domain. However, the allure of deep learning beckoned, and we ventured into the realm of neural networks. Leveraging TensorFlow and Keras, we meticulously crafted a neural architecture that exhibited remarkable prowess in discerning protein interactions. Our deep learning model unveiled intricate patterns, allowing us to delve deeper into the enigmatic world of PPIs. The culmination of our efforts was showcased through a captivating array of visualizations, each offering unique insights into the realm of protein interactions. Heatmaps artfully painted the picture of confusion matrices, elucidating the strengths and nuances of our predictive models. ROC curves, precision-recall curves, and bar plots masterfully illustrated the intricate interplay between true positives, false positives, and other critical metrics. Our research transcended the realms of technical prowess. It delved into the ethical dimensions of data integration, underscoring the importance of adhering to the highest standards of scientific ethics. We acknowledged the vital need for data privacy and the responsible handling of biological data, anchoring our research in a strong commitment to ethical conduct. In conclusion, our research stands as a testament to the transformative power of computational approaches in deciphering the complex tapestry of protein interactions. It not only provides predictive models but also serves as a blueprint for ethical data management and interpretation. As we bring this journey to a close, we invite fellow researchers to embark on this captivating voyage of discovery, shedding light on the molecular intricacies of SARS-CoV-2 and paving the way for a deeper understanding of host-virus interactions.