IMass Time: The Future, in Future!

Ma, Qiang; Adua, Eric; Boyce, Mary C.; Li, Xingang; Ji, Guang; Wang, Wei

doi:10.1089/omi.2018.0162

Cited by 14 publications

(8 citation statements)

References 170 publications

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“…Personalized medicine is, however, in need of a renewed vision that extends beyond genomics, and one that triangulates knowledge from multiple omics (multiomics) technologies (Ma et al, 2018) as well as social sciences and humanities (Conley, 2018;Fisher, 2018;Garvey and Maskal, 2020). To these ends, there is evidence, for example, that laboratory research thrives better upon integration with natural and social sciences, enhancing the creative processes in the laboratory and helping generate novel ideas in scientific practice (Conley, 2018;Fisher, 2018;Fisher et al, 2010;York et al, 2019a).…”

Section: A Science Of and Art On Variability Questionsmentioning

confidence: 99%

“…This question is critically relevant to the futures of personalized glycomedicine (Reily et al, 2019; and recent discussions on the concept of para-central dogma in cell biology (Ma et al, 2018;Wang, 2019). The question also relates well to efforts for deploying and integrating multiomics biomarker research across genomics, proteomics, and glycomics, and to account for not only genomic and proteomic contributions to drug response but also those from individual differences in glycan structure and function.…”

Section: A Questionmentioning

confidence: 99%

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Digging Deeper into Precision/Personalized Medicine: Cracking the Sugar Code, the Third Alphabet of Life, and Sociomateriality of the Cell

Özdemir

Arğa

Aziz

et al. 2020

OMICS: A Journal of Integrative Biology

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Precision/personalized medicine is a hot topic in health care. Often presented with the motto ''the right drug, for the right patient, at the right dose, and the right time,'' precision medicine is a theory for rational therapeutics as well as practice to individualize health interventions (e.g., drugs, food, vaccines, medical devices, and exercise programs) using biomarkers. Yet, an alien visitor to planet Earth reading the contemporary textbooks on diagnostics might think precision medicine requires only two biomolecules omnipresent in the literature: nucleic acids (e.g., DNA) and proteins, known as the first and second alphabet of biology, respectively. However, the precision/personalized medicine community has tended to underappreciate the third alphabet of life, the ''sugar code'' (i.e., the information stored in glycans, glycoproteins, and glycolipids). This article brings together experts in precision/personalized medicine science, pharmacoglycomics, emerging technology governance, cultural studies, contemporary art, and responsible innovation to critically comment on the sociomateriality of the three alphabets of life together. First, the current transformation of targeted therapies with personalized glycomedicine and glycan biomarkers is examined. Next, we discuss the reasons as to why unraveling of the sugar code might have lagged behind the DNA and protein codes. While social scientists have historically noted the importance of constructivism (e.g., how people interpret technology and build their values, hopes, and expectations into emerging technologies), life scientists relied on the material properties of technologies in explaining why some innovations emerge rapidly and are more popular than others. The concept of sociomateriality integrates these two explanations by highlighting the inherent entanglement of the social and the material contributions to knowledge and what is presented to us as reality from everyday laboratory life. Hence, we present a hypothesis based on a sociomaterial conceptual lens: because materiality and synthesis of glycans are not directly driven by a template, and thus more complex and open ended than sequencing of a finite length genome, social construction of expectations from unraveling of the sugar code versus the DNA code might have evolved differently, as being future-uncertain versus future-proof, respectively, thus potentially explaining the ''sugar lag'' in precision/personalized medicine diagnostics over the past decades. We conclude by introducing systems scientists, physicians, and biotechnology industry to the concept, practice, and value of responsible innovation, while glycomedicine and other emerging biomarker technologies (e.g., metagenomics and pharmacomicrobiomics) transition to applications in health care, ecology, pharmaceutical/diagnostic industries, agriculture, food, and bioengineering, among others.

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Section: A Science Of and Art On Variability Questionsmentioning

confidence: 99%

Section: A Questionmentioning

confidence: 99%

Digging Deeper into Precision/Personalized Medicine: Cracking the Sugar Code, the Third Alphabet of Life, and Sociomateriality of the Cell

Özdemir

Arğa

Aziz

et al. 2020

OMICS: A Journal of Integrative Biology

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“…IgG N-glycans were separated into 24 glycan peaks (GPs) by hydrophilic interaction liquid chromatography. The structures of glycans in each GP were determined by mass spectrometry (Ma et al, 2018), and the detailed description is shown in Table 1. GP3 and GP20 were excluded from all the statistical analysis due to the failure to quality control.…”

Section: Phenotype and Igg N-glycan Measurementsmentioning

confidence: 99%

Next-Generation (Glycomic) Biomarkers for Cardiometabolic Health: A Community-Based Study of Immunoglobulin G N-Glycans in a Chinese Han Population

Wang

et al. 2019

OMICS: A Journal of Integrative Biology

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Cardiovascular disease is a common complex trait that calls for next-generation biomarkers for precision diagnostics and therapeutics. The most common type of post-translational protein modification involves glycosylation. Glycans participate in key intercellular and intracellular functions, such as protein quality control, cell adhesion, cell-cell recognition, signal transduction, cell proliferation, and cell differentiation. In this context, immunoglobulin G (IgG) N-glycans affect the anti-inflammatory and proinflammatory responses of IgG, and are associated with cardiometabolic risk factors such as aging, central obesity, dyslipidemia, and hyperglycemia. Yet, the role of such glycomic biomarkers requires evaluation in diverse world populations. We report here original observations on association of IgG N-glycan biosignatures with 15 cardiometabolic risk factors in a community-based cross-sectional study conducted in 701 Chinese Han participants. After controlling for age and sex, we found that the 16, 21, and 18 IgG N-glycan traits were significantly different in participants with and without metabolic syndrome, hypertriglyceridemic waist phenotype, or abdominal obesity, respectively. The canonical correlation analysis showed that IgG N-glycan profiles were significantly associated with cardiometabolic risk factors (r = 0.469, p < 0.001). Classification models based on IgG N-glycan traits were able to differentiate participants with (1) metabolic syndrome, (2) hypertriglyceridemic waist phenotype, or (3) abdominal obesity from controls, with an area under receiver operating characteristic curves (AUC) of 0.632 (95% confidence interval [CI], 0.574-0.691, p < 0.001), 0.659 (95% CI, 0.587-0.730, p < 0.001), and 0.610 (95% CI, 0.565-0.656, p < 0.001), respectively. These new data suggest that IgG N-glycans may play an important role in cardiometabolic disease pathogenesis by regulating the proinflammatory or anti-inflammatory responses of IgG. Looking into the future, IgG N-glycan biosignatures warrant further research in other world population samples with a view to applications in clinical cardiology and public health practice.

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“…4 Because of tremendous developments in AI, 2016 is known as the "primary era of AI". 5 ML is the AI subset that focuses on building systems that can learn from historical data, recognize patterns, and make logical decisions with minimal human intervention. 6,7 As a problem-solving paradigm, ML has important applications in chemistry.…”

Section: ■ Introductionmentioning

confidence: 99%

Advances in the Application of Artificial Intelligence-Based Spectral Data Interpretation: A Perspective

Xue,

Sun,

Yang

et al. 2023

Anal. Chem.

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The interpretation of spectral data, including mass, nuclear magnetic resonance, infrared, and ultraviolet–visible spectra, is critical for obtaining molecular structural information. The development of advanced sensing technology has multiplied the amount of available spectral data. Chemical experts must use basic principles corresponding to the spectral information generated by molecular fragments and functional groups. This is a time-consuming process that requires a solid professional knowledge base. In recent years, the rapid development of computer science and its applications in cheminformatics and the emergence of computer-aided expert systems have greatly reduced the difficulty in analyzing large quantities of data. For expert systems, however, the problem-solving strategy must be known in advance or extracted by human experts and translated into algorithms. Gratifyingly, the development of artificial intelligence (AI) methods has shown great promise for solving such problems. Traditional algorithms, including the latest neural network algorithms, have shown great potential for both extracting useful information and processing massive quantities of data. This Perspective highlights recent innovations covering all of the emerging AI-based spectral interpretation techniques. In addition, the main limitations and current obstacles are presented, and the corresponding directions for further research are proposed. Moreover, this Perspective gives the authors’ personal outlook on the development and future applications of spectral interpretation.

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IMass Time: The Future, in Future!

Cited by 14 publications

References 170 publications

Digging Deeper into Precision/Personalized Medicine: Cracking the Sugar Code, the Third Alphabet of Life, and Sociomateriality of the Cell

Digging Deeper into Precision/Personalized Medicine: Cracking the Sugar Code, the Third Alphabet of Life, and Sociomateriality of the Cell

Next-Generation (Glycomic) Biomarkers for Cardiometabolic Health: A Community-Based Study of Immunoglobulin G N-Glycans in a Chinese Han Population

Advances in the Application of Artificial Intelligence-Based Spectral Data Interpretation: A Perspective

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