scholarly journals Machine learning discovery of missing links that mediate alternative branches to plant alkaloids

2021 ◽  
Author(s):  
Christopher Vavricka ◽  
Shunsuke Takahashi ◽  
Naoki Watanabe ◽  
Musashi Takenaka ◽  
Mami Matsuda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alkaloid Production ◽  
Selection Algorithm ◽  
Model Application ◽  
Microbial Enzymes ◽  
Enhanced Production ◽  
Benzylisoquinoline Alkaloid ◽  
Plant Alkaloids ◽  
Homologous Enzyme

Abstract Engineering the microbial production of secondary metabolites is limited by the known reactions of correctly annotated enzymes in sequence databases. To expand the range of biosynthesis pathways, machine learning is herein demonstrated for the discovery of missing link enzymes, using benzylisoquinoline alkaloid production as a model application with potential to revolutionize the paradigm of sustainable biomanufacturing. Bacterial studies utilize a tetrahydropapaveroline pathway, whereas plants are reported to contain a more stable norcoclaurine pathway, which is exploited in yeast. However, committed aromatic precursors are currently produced by microbial enzymes that remain elusive in plants. Accordingly, the machine learning enzyme selection algorithm is first applied to clarify the early missing links in plant alkaloid pathways. Characterization of predicted sequences via metabolomics reveals distinct oxidases and carboxy-lyases, which complete a plant gene-only benzylisoquinoline alkaloid pathway from tyrosine. Synergistic application of aryl acetaldehyde producing enzymes results in enhanced production through hybrid norcoclaurine and tetrahydropapaveroline pathways. Transplantation of features into homologous enzyme templates leads to the highest levels of bacterial norcoclaurine and N-methylcoclaurine. Mechanism-directed isotope tracing patterns confirm alternative flux branches from aromatic precursors to alkaloids. This machine learning-driven workflow can be adapted to numerous pathways.

Characterization of Citrullination Sites in Neutrophils and Mast Cells Activated by Ionomycin via Integration of Mass Spectrometry and Machine Learning

2021 ◽  
Author(s):  
Raghothama Chaerkady ◽  
Yebin Zhou ◽  
Jared A. Delmar ◽  
Shao Huan Samuel Weng ◽  
Junmin Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Mast Cells

scholarly journals Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
José Castela Forte ◽  
Galiya Yeshmagambetova ◽  
Maureen L. van der Grinten ◽  
Bart Hiemstra ◽  
Thomas Kaufmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Mortality Risk ◽  
Clustering Algorithms ◽  
Kidney Injury ◽  
Risk Groups ◽  
Admission Diagnosis ◽  
Dutch Hospital ◽  
Cluster Membership

AbstractCritically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk of mortality and kidney injury. We analysed prospectively collected data including co-morbidities, clinical examination, and laboratory parameters from a minimally-selected population of 743 patients admitted to the ICU of a Dutch hospital between 2015 and 2017. We compared four clustering methodologies and trained a classifier to predict and validate cluster membership. The contribution of different variables to the predicted cluster membership was assessed using SHapley Additive exPlanations values. We found that deep embedded clustering yielded better results compared to the traditional clustering algorithms. The best cluster configuration was achieved for 6 clusters. All clusters were clinically recognizable, and differed in in-ICU, 30-day, and 90-day mortality, as well as incidence of acute kidney injury. We identified two high mortality risk clusters with at least 60%, 40%, and 30% increased. ICU, 30-day and 90-day mortality, and a low risk cluster with 25–56% lower mortality risk. This machine learning methodology combining deep embedded clustering and variable importance analysis, which we made publicly available, is a possible solution to challenges previously encountered by clustering analyses in heterogeneous patient populations and may help improve the characterization of risk groups in critical care.

scholarly journals MACHINE-LEARNING CHARACTERIZATION OF SUBTLE HEMODYNAMIC INSTABILITY DIFFERENTIATES COVID-19 VERSUS SEASONAL INFLUENZA IN HOSPITALIZED PATIENTS

2021 ◽  
Vol 77 (18) ◽  
pp. 3087
Author(s):  
Naveena Yanamala ◽  
Nanda H. Krishna ◽  
Quincy Hathaway ◽  
Aditya Radhakrishnan ◽  
Srinidhi Sunkara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Seasonal Influenza ◽  
Hemodynamic Instability ◽  
Hospitalized Patients

scholarly journals Machine learning powered ellipsometry

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jinchao Liu ◽  
Di Zhang ◽  
Dianqiang Yu ◽  
Mengxin Ren ◽  
Jingjun Xu
Keyword(s):  
Machine Learning ◽  
Optical Constants ◽  
Optical Characterization ◽  
Superior Performance ◽  
Trial And Error ◽  
Powerful Method ◽  
Human In The Loop ◽  
Ill Posed ◽  
Fully Automatic

AbstractEllipsometry is a powerful method for determining both the optical constants and thickness of thin films. For decades, solutions to ill-posed inverse ellipsometric problems require substantial human–expert intervention and have become essentially human-in-the-loop trial-and-error processes that are not only tedious and time-consuming but also limit the applicability of ellipsometry. Here, we demonstrate a machine learning based approach for solving ellipsometric problems in an unambiguous and fully automatic manner while showing superior performance. The proposed approach is experimentally validated by using a broad range of films covering categories of metals, semiconductors, and dielectrics. This method is compatible with existing ellipsometers and paves the way for realizing the automatic, rapid, high-throughput optical characterization of films.

scholarly journals Accelerating diagnosis of Parkinson’s disease through risk prediction

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
William Yuan ◽  
Brett Beaulieu-Jones ◽  
Richard Krolewski ◽  
Nathan Palmer ◽  
Christine Veyrat-Follet ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Subsequent Development ◽  
Time Compression ◽  
Disease Modifying Therapies ◽  
Machine Learning Approach ◽  
Diagnostic Window

Abstract Background Characterization of prediagnostic Parkinson’s Disease (PD) and early prediction of subsequent development are critical for preventive interventions, risk stratification and understanding of disease pathology. This study aims to characterize the role of the prediagnostic period in PD and, using selected features from this period as novel interception points, construct a prediction model to accelerate the diagnosis in a real-world setting. Methods We constructed two sets of machine learning models: a retrospective approach highlighting exposures up to 5 years prior to PD diagnosis, and an alternative model that prospectively predicted future PD diagnosis from all individuals at their first diagnosis of a gait or tremor disorder, these being features that appeared to represent the initiation of a differential diagnostic window. Results We found many novel features captured by the retrospective models; however, the high accuracy was primarily driven from surrogate diagnoses for PD, such as gait and tremor disorders, suggesting the presence of a distinctive differential diagnostic period when the clinician already suspected PD. The model utilizing a gait/tremor diagnosis as the interception point, achieved a validation AUC of 0.874 with potential time compression to a future PD diagnosis of more than 300 days. Comparisons of predictive diagnoses between the prospective and prediagnostic cohorts suggest the presence of distinctive trajectories of PD progression based on comorbidity profiles. Conclusions Overall, our machine learning approach allows for both guiding clinical decisions such as the initiation of neuroprotective interventions and importantly, the possibility of earlier diagnosis for clinical trials for disease modifying therapies.

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