Translational biomedical research
The rapid development and implementation of data repositories that contain representations of complex objects, such as time series or spatial data, has not been accompanied by a greater availability of tools that allow the search of these databases in terms close to the language of and based on criteria that are meaningful to the intended users of those data collections. These shortcomings are evident in particular fields such as biology or medicine, where a large amount of data accumulates. However, retrieved knowledge often frustrates the expectations of interested users because it consists of familiar, homogenized patterns or long and intractable descriptions. Therefore, we believe that one of the challenges in the post-genomic era is to extract useful knowledge and, more importantly, to transform this knowledge into actionable knowledge. That is, deciphering the available information and converting it into hypotheses that can predict new knowledge that would eventually improve the decision-making systems that affect our lifestyle. Our biomedical research has been based on multiple measurements including but not limited to multiple imaging, genetics, molecular biology, multi-omics, clinical diagnoses and comorbid phenotypes, phenomics, real-time physiological signs, EHR and EMR, etc. The different target diseases include dementias, mental disorders, cardiovascular diseases and various types of cancer, among others. Our challenge is to generate novel artificial intelligence and machine learning tools that integrate the data available in knowledge bases utilized by regular as well as AI clinicians. The total or partial coincidence with interconnected pre-existing knowledge bases will reduce the universe of possible diagnoses of a patient and, consequently, will shorten the time that the patient needs to be treated. A decision-making performed by an AI clinician will help identify the treatments that are best suited to diagnosis and patient follow-up.
Contact: Igor Zwir
