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Tatonetti uses data science to inform drug design and to evaluate the effectiveness of potential pharmaceutical candidates for specific people. [2] His lab develops data mining approaches to understand clinical and molecular data. He combines electronic health records and genomics databases with artificial intelligence and machine learning. [2]
Machine learning in environmental metagenomics can help to answer questions related to the interactions between microbial communities and ecosystems, e.g. the work of Xun et al., in 2021 [50] where the use of different machine learning methods offered insights on the relationship among the soil, microbiome biodiversity, and ecosystem stability.
Shah's research towards combining machine learning, text-mining, and prior knowledge has shown that by using data from electronic medical records it is possible to build predictive models for guiding clinical care, [28] [29] such as early treatment of certain wounds, [30] finding undiagnosed genetic diseases, [31] [32] and prioritizing advance ...
The goal of predictive medicine is to predict the probability of future disease so that health care professionals and the patient themselves can be proactive in instituting lifestyle modifications and increased physician surveillance, such as bi-annual full body skin exams by a dermatologist or internist if their patient is found to have an increased risk of melanoma, an EKG and cardiology ...
Prompted by her experience with breast cancer, Barzilay is applying machine learning to oncology. She is collaborating with physicians and students to devise deep learning models that utilize images, text, and structured data to identify trends that affect early diagnosis, treatment, and disease prevention.
Computer-aided drug design (CADD) was introduced in the 1980s in order to screen for novel drugs. [3] The underlying premise is that by parsing an extremely large data set for chemical compounds which may be viable to make a certain pharmaceutical, researchers were able to minimize the amount of novel without testing them all experimentally.
The first wave of research in the field of computational epigenetics was driven by rapid progress of experimental methods for data generation, which required adequate computational methods for data processing and quality control, prompted epigenome prediction studies as a means of understanding the genomic distribution of epigenetic information ...
Accurate computer models of a treatment and its deployment, as well as patient characteristics, are necessary precursors for the development of in silico clinical trials. [5] [6] [8] [9] In such a scenario, ‘virtual’ patients would be given a ‘virtual’ treatment, enabling observation through a computer simulation of how the candidate biomedical product performs and whether it produces ...