Multiple predictive models are generated by using various multi-omics data types; then a final predictive model is generated by using the multiple models. Predictive models can be consolidated from various multi-omics data types, and each data type can be gathered from a various set of patients with same phenotype. Multiple data matrices of different multi-omics data types are incorporated into a large input matrix; then a predictive model is generated by using the large input matrix. It is fairly easy to leverage various machine learning methods for analyzing continuous or categorical data once a large input matrix is formed. It may be challenging to combine a large input matrix. Datasets for various multi-omics data types are first converted into intermediate forms, which are united into a large input matrix; then a predictive model is generated by using the large input matrix. Unique variables such as patient identifiers can be used to link multi-omics data types and integrate a variety of continuous or categorical data values. It may be challenging to transform into intermediate forms.
If the barrier to precision medicine is data handling, then artificial intelligence (AI) may be the logical solution. Machine learning and deep learning are making inroads in a variety of industries, and seem poised to have a big impact in medicine, a process that is already in motion – and perhaps not a moment too soon. "Your chance in your lifetime of getting a false diagnosis, if you look at the data, is 100 percent," said Thomas Wilckens, founder and CEO at InnVentis to the audience at the recently-concluded Precision Medicine Leadership Summit in San Diego. "There's a lot to improve." Wilckens moderated Going Deep in the Fast Lane – the Rise of AI in Precision Medicine, which combined experts from industry and academia to parse this evolving segment.
Looking at patient clinical and biomedical data we try to dig into molecular-level detail to redefine the disease and better endotyping for two main purposes: target identification and designing better clinical trials. There is an abundance of patient data created e.g. With increasing amounts of data being generated, we need AI models to help make meaningful discoveries. Precision medicine is the future of medicine. Our belief is that by better understanding the underlying mechanisms of diseases in patients and identifying more specific and precise endotypes, we will be able to provide better medicines that are efficient in the specific patients groups they are developed for.
As far back as the 1970s, doctors have pondered whether one day, as medical technology barrels ahead, the patient history and physical examination (H&P) would eventually become obsolete. And yet, we were all told in medical school that a proper history is enough to make X percent of diagnoses, which increases further when you work in physical findings. But today we are on the brink of the era of multiomics, a term encompassing the numerous data available for patients, from genomics, epigenomics, proteomics, microbiomics, metabolomics, and an array of other omics. These days, a health dataset from a single patient can be immense, to be sure. Advances in artificial intelligence and machine learning, however, are making it possible to organize and filter multiomic data from a patient in ways that make them useful to physicians--ways that can personalize diagnosis and care, and bypass the often imperfect recollections of patients and patients' families obtained during a history.