There are many specific problems in life sciences that need their specific solutions. We develop our own code to create tailored solutions, mainly based on machine learning and AI, that are needed in many scenarios that generic libraries and tools do not work as efficient and accurate as needed. We have developed solutions for various problem sets such as biomarker discovery, signal and image processing from biomedical diagnostic instruments, anomaly detection and more. We at Denapsis AI turn data into accurate predictors that are relevant for each individual patient. Through multivariate analyses, we advance the field towards true Precision Medicine, both for diagnosis and treatment choice. While we see improvements in the medical field as most worthy goal, we recognise that there are many other areas that can benefit from our work.
Real-life data is often "noisy", since it is a superposition of a number of factors. Biomedical measurements, for example, can be distorted by genetic variation, life style, age, gender, the environment, statistical uncertainty etc. Our methods are optimized for just that: unsupervised AI not only learns medically relevant patterns, but also how to deal with the "background" of other factors.
Each type of data requires its own careful interpretation: we have experience with genomic and genetic variants, epigenetic modifications such as DNA methylation or protein binding, RNA expression, protein biomarkers etc. - and, of course, combinations thereof. Whatever signals our AI finds, we will link it up to disease, disease susceptibility, or phenotype.
"The knowledgeable team of Denapsis Artificial Intelligence has helped us with our huge datasets to go from Data Analysis to Data Science and Machine Learning. They have enabled us to, not only evaluate clinical datasets to learn from it, but also to forecast the future trends."
Maryam Poorafshar, PhD.
Director, Product Innovation, RnD at Thermo Fisher Scientific