Protein interaction forecast by AI and Machine Learning
The University Hospital Ulm has a permanent place in university health care, research and teaching. With 29 clinics and 16 institutes, it offers high -quality stationary and outpatient health care. Every year, around 50,000 patients are treated inpatient and almost 300,000 outpatient quarterly cases are looked after.
Our project was characterized by the realization that the identification of protein interactions using conventional biochemical methods is time -consuming, expensive and technically demanding. Due to the variety of proteins and existing data, we had to develop an innovative model that can precisely predict protein interactions based on the amino acid sequences.
We used various resources, including TPUs, Vertex Ai Notebooks and Cloud Storage, which we developed a model that acts as a proof-of-Concept and showed the feasibility of such a prediction. This approach opened new opportunities for protein interaction and brought the potential to increase the efficiency and accuracy of identifying protein interactions.
We were able to develop a prototype of a model that enabled the prediction of protein interactions based on amino acid sequences, whereby we also recognized some of our prototype model and already published models. It is important to make the identification of protein interactions more effective and inexpensive.
The successful development of a prototype model for predicting protein interactions based on amino acid sequences shows the enormous potential of artificial intelligence and machine learning in bio-science and especially in protein research.