Assistant Professor Boston University Boston, Massachusetts, United States
Abstract Text: In recent years, there has been a significant increase in the amount of data being generated in the biomedical field. This data is crucial for advancing our understanding of health and disease mechanisms, as well as predicting clinical outcomes. However, the lack of powerful analytical tools has slowed the translation of this knowledge. To address this issue, a software called SIMON has been developed. This software uses over 180 state-of-the-art machine learning algorithms to aid in pattern recognition and knowledge extraction from multiple types of biomedical data. SIMON has a user-friendly interface, standardized pipelines, and automated machine learning methods to help identify optimal algorithms. This allows both technical and non-technical researchers to easily identify important patterns in biomedical data. The software has been tested on various types of biomedical datasets and has been shown to be accurate, easy to use, and powerful. It has been used to identify patterns associated with favorable outcomes for patients with SARS-CoV-2 and influenza, which can help in the development of more effective vaccines and understanding the long-term impact of these viruses on the immune system. SIMON has the potential to accelerate the development of vaccines that provide long-term protection against pandemic viruses.
