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Artificial Intelligence in Systems Biology
Published in P. Kaliraj, T. Devi, Artificial Intelligence Theory, Models, and Applications, 2021
S. Dhivya, S. Hari Priya, R. Sathishkumar
Systems biology can solve the most complex problems in medicine, drug discovery, and biomedical engineering. The basic problem lies in the convolution of biotic systems that have emerged over billions of years. AI technology application in systems biology includes text-mining, qualitative physics, statistical interpretations, machine learning, and deep learning algorithms, etc. Hence, AI technology in systems biology is helping to do quality research leading to solutions that are unresolved until now. The key characteristics of AI are shown in Figure 7.2. For example, AI technology has been widely used to resolve complex problems in diverse areas such as drug discovery and coral reef systems biology. AI also makes a significant contribution to capturing, distributing, developing, and usage of these resourceful data for human welfare.
Personomics: The Missing Link in the Evolution from Precision Medicine to Personalized Medicine
Published in Shaker A. Mousa, Raj Bawa, Gerald F. Audette, The Road from Nanomedicine to Precision Medicine, 2020
As we consider the practice of medicine today and the ideal practice of medicine in the future, we should begin by defining the terms precision medicine and personalized medicine. This commentary proposes an operational definition of these terms and the use of a recently coined term—personomics [1]—to help differentiate the two terms. Herein, precision medicine is defined as the application of recent advances in medical science to characterize individuals based on the unique biological characteristics of the individual or of specimens obtained from that individual. Precision medicine uses information derived from genomics, proteomics, metabolomics, epigenomics, pharmacogenomics, and other “-omics” to derive more precisely tailored diagnostics and therapeutics and thereby improve human health [2]. For the data from precision medicine to be used optimally to improve human health, systems biology will need to collect and integrate data “…from the molecular level, through cells, tissues and organisms, to the population level” [3]. Systems biology will need to be applied to human disease in what has been termed systems medicine, [4] using high throughput technologies to produce and integrate enormous data sets that lead to an improved understanding of human biology. It has been suggested that as digital technology allows these data to be communicated more readily to activated patients and consumers, the possibility of a new healthcare system may be realized that is predictive, preventive, personalized, and participatory (P4) [3].
Personomics: The Missing Link in the Evolution from Precision Medicine to Personalized Medicine
Published in Shaker A. Mousa, Raj Bawa, Gerald F. Audette, The Road from Nanomedicine to Precision Medicine, 2019
As we consider the practice of medicine today and the ideal practice of medicine in the future, we should begin by defining the terms precision medicine and personalized medicine. This commentary proposes an operational definition of these terms and the use of a recently coined term—personomics [1]—to help differentiate the two terms. Herein, precision medicine is defined as the application of recent advances in medical science to characterize individuals based on the unique biological characteristics of the individual or of specimens obtained from that individual. Precision medicine uses information derived from genomics, proteomics, metabolomics, epigenomics, pharmacogenomics, and other “-omics” to derive more precisely tailored diagnostics and therapeutics and thereby improve human health [2]. For the data from precision medicine to be used optimally to improve human health, systems biology will need to collect and integrate data “…from the molecular level, through cells, tissues and organisms, to the population level” [3]. Systems biology will need to be applied to human disease in what has been termed systems medicine, [4] using high throughput technologies to produce and integrate enormous data sets that lead to an improved understanding of human biology. It has been suggested that as digital technology allows these data to be communicated more readily to activated patients and consumers, the possibility of a new healthcare system may be realized that is predictive, preventive, personalized, and participatory (P4) [3].
An overview of the current progress, challenges, and prospects of human biomonitoring and exposome studies
Published in Journal of Toxicology and Environmental Health, Part B, 2019
Mariana Zuccherato Bocato, João Paulo Bianchi Ximenez, Christian Hoffmann, Fernando Barbosa
For the internal exposome (body constituents), target or nontarget proteins, metabolites, expressed genes, or microbes are measured in blood, plasma, urine, and feces by utilizing several “omics” high-throughput platforms, in addition to levels of target pollutants and nutrients traditionally assessed in HB studies. The strategy uses the concept of “system biology” (Badimon, Vilahur, and Padro 2016). Systems biology conglomerates a quantitative analysis of large networks of molecular and functional changes that occur in multiple levels of biological organization (Sauer et al. 2015; Sturla et al. 2014; Van Ommen et al. 2017). Systems biology also offers advanced strategies to obtain mechanistic knowledge, combining molecular data acquisition on a large scale, through multi-omic platforms (transcriptomic, proteomic, metabolomic/adductomic/lipidomic, metallomic) with data integration by employing data mining. Selected algorithms may characterize (classify) a specific health condition, with multiples and specific biological markers of disease (Azuaje, Devaux, and Wagner 2009; Caberlotto and Lauria 2014; Wang et al. 2010). Figure 2 depicts the design of an exposome study.