Disease Prediction
Using Machine Learning
Novel Approach to Disease Prediction
Ayass Research Institute implementing machine learning models that can efficiently predict (based on our selection of biomarkers) the disease presence and progression in patients.
Scoring System Using Quantum Mechanics
Our software can help form a biological scoring system by integrating antibodies data, T cell profiling data, transcriptomes data, mitochondrial DNA data, and hereditary NGS genetic data. This scoring system is designed to have a more accurate diagnosis and to help in disease therapy monitoring.
We know that the cells that play a role in certain areas can also be present in other places. If these cells share similar quantum characteristics, then they can manifest with disease presentation in different parts of the body due to their similarities, via quantum superposition, entanglement, and tunneling.
Quantum Characterization
We can understand the disease manifestation through Quantum characterization of the mtDNA of the T cell (T cells play the most important function in our immune system and ultimately in our wellness vs illness). This can be applied to understand the side effect manifestation of drug therapy as well, where people taking certain medications for certain illnesses experience side effects based on quantum. Drugs target different biomarkers, so we can monitor how well the therapy is working by measuring the level of different biomarkers. Using our model, we are also able to predict what therapy is recommended to the patient and provided personalized medicine.