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Identification of subgroups of patients with type 2 diabetes with differences in renal function preservation, comparing patients receiving sodium-glucose co-transporter-2 inhibitors with those receiving dipeptidyl peptidase-4 inhibitors, using a supervised machine-learning algorithm (PROFILE study): A retrospective analysis of a Japanese commercial medical database

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DIABETES OBESITY & METABOLISM 2019年第8期21卷 1925-1934页

作者： Zhou, Fang L. Watada, Hirotaka Tajima, Yuki Berthelot, Mathilde Kang, Dian Esnault, Cyril Shuto, Yujin Maegawa, Hiroshi Koya, Daisuke Sanofi Real World Evidence Generat Bridgewater NJ USA Juntendo Univ Grad Sch Med Dept Metab & Endocrinol Tokyo Japan Sanofi KK Med Affairs Tokyo Japan Quinten Data Sci Consulting Paris France Shiga Univ Med Sci Dept Med Otsu Shiga Japan Kanazawa Med Univ Dept Diabetol & Endocrinol Uchinada Ishikawa Japan

Aims To investigate the effects of sodium-glucose co-transporter-2 (SGLT2) inhibitors vs. dipeptidyl peptidase-4 (DPP-4) inhibitors on renal function preservation (RFP) using real-world data of patients with type 2 diabetes in Japan, and to identify which subgroups of patients obtained greater RFP benefits with SGLT2 inhibitors vs. DPP-4 inhibitors. Methods We retrospectively analysed claims data recorded in the Medical Data Vision database in Japan of patients with type 2 diabetes (aged >= 18 years) prescribed any SGLT2 inhibitor or any DPP-4 inhibitor between May 2014 and September 2016 (identification period), in whom estimated glomerular filtration rate (eGFR) was measured at least twice (baseline, up to 6 months before the index date;follow-up, 9 to 15 months after the index date) with continuous treatment until the follow-up eGFR. The endpoint was the percentage of patients with RFP, defined as no change or an increase in eGFR from baseline to follow-up. A proprietary supervised learning algorithm (Q-Finder;Quinten, Paris, France) was used to identify the profiles of patients with an additional RFP benefit of SGLT2 inhibitors vs. DPP-4 inhibitors. Results Data were available for 990 patients prescribed SGLT2 inhibitors and 4257 prescribed DPP-4 inhibitors. The proportion of patients with RFP was significantly greater in the SGLT2 inhibitor group (odds ratio 1.27;P = 0.01). The Q-Finder algorithm identified four clinically relevant subgroups showing superior RFP with SGLT2 inhibitors (P < 0.1): no hyperlipidaemia and eGFR >= 79 mL/min/1.73 m(2);eGFR >= 79 mL/min/1.73 m(2) and diabetes duration <= 1.2 years;eGFR >= 75 mL/min/1.73 m(2) and use of antithrombotic agents;and haemoglobin <= 13.4 g/dL and LDL cholesterol >= 95.1 mg/dL. In each profile, glycaemic control was similar in the two groups. Conclusion SGLT2 inhibitors were associated with more favourable RFP vs. DPP-4 inhibitors in patients with certain profiles in real-world settings in Japan.

关键词： DPP-4 inhibitor machine-learning algorithm real-world clinical practice renal function SGLT2 inhibitor type 2 diabetes

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A Genome-Wide Association Study and machine-learning algorithm Analysis on the Prediction of Facial Phenotypes by Genotypes in Korean Women

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CLINICAL COSMETIC AND INVESTIGATIONAL DERMATOLOGY 2022年 15卷 433-445页

作者： Yoo, Hye-Young Lee, Ki-Chan Woo, Ji-Eun Park, Sung-Ha Lee, Sunghoon Joo, Joungsu Bae, Jin-Sik Kwon, Hyuk-Jung Park, Byoung-Jun Kolmar Korea Co Ltd Skin & Nat Prod Lab Seoul 06800 South Korea Eone Diagn Genome Ctr Co Ltd R&D Dept Songdo Incheon 22014 South Korea

Purpose: Changes in facial appearance are affected by various intrinsic and extrinsic factors, which vary from person to person. Therefore, each person needs to determine their skin condition accurately to care for their skin accordingly. Recently, genetic identification by skin-related phenotypes has become possible using genome-wide association studies (GWAS) and machine-learning algorithms. However, because most GWAS have focused on populations with American or European skin pigmentation, large-scale GWAS are needed for Asian populations. This study aimed to evaluate the correlation of facial phenotypes with candidate single-nucleotide polymorphisms (SNPs) to predict phenotype from genotype using machine learning. Materials and Methods: A total of 749 Korean women aged 30-50 years were enrolled in this study and evaluated for five facial phenotypes (melanin, gloss, hydration, wrinkle, and elasticity). To find highly related SNPs with each phenotype, GWAS analysis was used. In addition, phenotype prediction was performed using three machine-learning algorithms (linear, ridge, and linear support vector regressions) using five-fold cross-validation. Results: Using GWAS analysis, we found 46 novel highly associated SNPs (p < 1x10(-05)): 3, 20, 12, 6, and 5 SNPs for melanin, gloss, hydration, wrinkle, and elasticity, respectively. On comparing the performance of each model based on phenotypes using five-fold cross-validation, the ridge regression model showed the highest accuracy (r(2) = 0.6422-0.7266) in all skin traits. Therefore, the optimal solution for personal skin diagnosis using GWAS was with the ridge regression model. Conclusion: The proposed facial phenotype prediction model in this study provided the optimal solution for accurately predicting the skin condition of an individual by identifying genotype information of target characteristics and machine-learning methods. This model has potential utility for the development of customized cosmetics.

关键词： customized cosmetics single-nucleotide polymorphism genome-wide association study machine-learning algorithm microarray

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Microstructural evolution and constitutive analysis of Al-Mg-Zn-Er-Zr based on arrhenius-type and machine-learning algorithm

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MATERIALS TODAY COMMUNICATIONS 2022年 32卷

作者： Xue, Da Wei, Wu Shi, Wei Zhou, Xiaorong Rong, Li Wen, Shengping Wu, Xiaolan Qi, Peng Gao, Kunyuan Huang, Hui Nie, Zuoren Beijing Univ Technol Key Lab Adv Funct Mat Educ Minist China Beijing 100124 Peoples R China Inst Corros Sci & Technol Guangzhou 510530 Peoples R China Univ Manchester Dept Mat Manchester M13 9PL England

In this work, artificial neural network (ANN) is employed to predict the hot deformation behavior of Al-Mg-Zn alloys containing small amounts of Er and Zr. A comparative study between the experimental results and the computational results based on Arrhenius constitutive equation and an ANN model was performed, where the theoretical calculation was used to predict the hot deformation behavior of the alloy. The results showed that relative errors obtained from Arrhenius constitutive equation were in the range of -17.7% to + 13.6%, whereas the errors varied from -9.3% to + 9.7% via ANN model. It suggests that the ANN model can avoid some un-certainties of the constitutive equation and predict the thermal deformation behavior of alloys more effectively. The dislocation density has also decreased with an increasing temperature or a decreasing strain rate. The dy-namic aging effect and the dislocation density showed the opposite trend. As hot deformation can induce the intermittent precipitation of Mg-32(Al, Zn)(49) at the grain boundaries, it is expected to improve the corrosion performance of alloy materials.

关键词： Al-Mg-Zn-Er-Zr alloy machine-learning algorithm Mg-32(AlZn)(49) phase Dislocation

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Prioritization of Candidate Genes for Congenital Diaphragmatic Hernia in a Critical Region on Chromosome 4p16 using a machine-learning algorithm

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JOURNAL OF PEDIATRIC GENETICS 2018年第4期7卷 164-173页

作者： Callaway, Danielle A. Campbell, Ian M. Stover, Samantha R. Hernandez-Garcia, Andres Jhangiani, Shalini N. Punetha, Jaya Paine, Ingrid S. Posey, Jennifer E. Muzny, Donna Lally, Kevin P. Lupski, James R. Shaw, Chad A. Fernandes, Caraciolo J. Scott, Daryl A. Baylor Coll Med Dept Pediat One Baylor PlazaBCM 30 Houston TX 77030 USA Childrens Hosp Philadelphia Div Human Genet Philadelphia PA 19104 USA Baylor Coll Med Dept Mol & Human Genet Houston TX 77030 USA Baylor Coll Med Human Genome Sequencing Ctr Houston TX 77030 USA UT Hlth Dept Pediat Surg McGovern Med Sch Houston TX USA Baylor Coll Med Div Neonatol Dept Pediat Houston TX 77030 USA Baylor Coll Med Dept Mol Physiol & Biophys Houston TX 77030 USA

Wolf-Hirschhorn syndrome (WHS) is caused by partial deletion of the short arm of chromosome 4 and is characterized by dysmorphic facies, congenital heart defects, intellectual/developmental disability, and increased risk for congenital diaphragmatic hernia (CDH). In this report, we describe a stillborn girl with WHS and a large CDH. A literature review revealed 15 cases of WHS with CDH, which overlap a 2.3-Mb CDH critical region. We applied a machine-learning algorithm that integrates large-scale genomic knowledge to genes within the 4p16.3 CDH critical region and identified FGFRL1, CTBP1, NSD2, FGFR3, CPLX1, MAEA, CTBP1-AS2, and ZNF141 as genes whose haploinsufficiency may contribute to the development of CDH.

关键词： Wolf-Hirschhorn syndrome congenital diaphragmatic hernia machine-learning algorithm

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Gallic acid alleviates ferroptosis by negatively regulating APOC3 and improves nerve function deficit caused by traumatic brain injury

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SCIENTIFIC REPORTS 2025年第1期15卷 1-12页

作者： Liu, Yu Fu, Xiaojia Li, Jing Guo, Jianqiang Zhao, Zongren Zheng, Jinyu Xuzhou Med Univ Dept Neurosurg Affiliated Huaian Hosp Huaian 223022 Peoples R China Xuzhou Med Univ Xuzhou 221000 Peoples R China

Traumatic brain injury (TBI) is more common than ever and is becoming a global public health issue. A variety of secondary brain injuries occur after TBI, including ferroptosis characterized by iron-dependent lipid peroxidation. Gallic acid is a kind of traditional Chinese medicine, which has many biological effects such as anti-inflammatory and antioxidant. We further investigated whether Gallic acid can improve the neurological impairment caused by ferroptosis after TBI by targeting APOC3. Weighted gene coexpression network analyses (WGCNA) and 3 kinds of machine-learning algorithms were used to find the potential biomarkers. Then the HERB database was used to select the Chinese herb that acted on the target gene APOC3. Finally, we selected Gallic acid as a drug targeting APOC3 and verified by Western blotting. The effect of Gallic acid on the improvement of neurological function was studied by Nissl staining and FJB staining. Finally, the effect of Gallic acid on the cognitive ability of TBI mice was explored through behavioral experiments. Gallic acid can inhibit the expression level of APOC3 and thus inhibit the level of ferroptosis after TBI. It can also reduce the degeneration of nerve tissue by inhibiting ferroptosis and improve the neurological function deficit. The behavioral experiment proved that Gallic acid can alleviate the behavioral cognitive impairment caused by TBI. Gallic acid can reduce ferroptosis by inhibiting APOC3, and then alleviate neurological impairment after TBI.

关键词： Gallic acid APOC3 Traumatic brain injury machine-learning algorithm Ferroptosis

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A simplified machine learning model to forecast individual thermal comfort in older adults' residential spaces without relying on wearable devices

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SUSTAINABLE CITIES AND SOCIETY 2025年 119卷

作者： Lee, Junsoo Seo, Seungwon Han, Soeun Koo, Choongwan Incheon Natl Univ Div Architecture & Urban Design Incheon 22012 South Korea

The thermal environment significantly affects the psychological and emotional stability of older adults. Prior studies assessing personal parameters in thermal comfort relied on qualitative methods, failing to account for variations due to real-time activity levels. While wearable devices measuring real-time heart rates were used to estimate personalized thermal conditions, the low acceptance among older adults remains a challenge. To address this, a simplified machine learning model was developed to forecast individual thermal comfort in older adults' residential spaces without relying on wearable devices. The model utilized personal, environmental, and temporal variables as proxies to predict thermal comfort without real-time heart rate data. Conducted in a living- lab with eight older adults at the "G" senior welfare agency in Gimje, Korea, this study collected real-time environmental and personal data from March 2022 to February 2023. Key findings include: (i) variations in individual activity levels significantly impacted thermal comfort even under similar thermal environments;(ii) the proposed approach achieved high accuracy in predicting thermal comfort, with a mean absolute error of 0.048;(iii) error pattern analysis suggested strategies to refine forecast accuracy. This approach provides a practical and systematic solution for managing thermal comfort, addressing the wearable device acceptance challenge among older adults.

关键词： Older adults Residential spaces Individual thermal comfort Forecasting model machine-learning algorithm Predicted mean vote

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Landsat TM/ETM⁺ and machine-learning algorithms for limnological studies and algal bloom management of inland lakes

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JOURNAL OF APPLIED REMOTE SENSING 2018年第2期12卷

作者： Lin, Shengpan Novitski, Linda N. Qi, Jiaguo Stevenson, Robert Jan Michigan State Univ Dept Geog Environm & Spatial Sci E Lansing MI 48824 USA Michigan State Univ Dept Integrat Biol E Lansing MI 48824 USA Nanjing Agr Univ Coll Agr Nanjing Jiangsu Peoples R China

Accumulating remotely sensed and ground-measured data and improvements in data mining such as machine-learning techniques open new opportunities for monitoring and managing algal blooms over large spatial scales. The goal of this study was to test the accuracy of remotely sensed algal biomass determined with machine-learning algorithms and Landsat TM/ETM+ imagery. We used chlorophyll-a concentration data from the 2007 National Lake Assessment (NLA) (lake N = 1157) by the US Environmental Protection Agency to train and test Landsat TM/ETM+ algorithms. Results showed significant improvements in chlorophyll-a retrieval accuracy using machine-learning algorithms compared with traditional empirical models using linear regression. Specifically, the results from boosted regression trees and random forest explained, respectively, 45.8% and 44.5% of chlorophyll-a variation. Multiple linear regression could only explain 39.8% of chlorophyll-a variation. The chlorophyll-a concentration derived from Landsat TM/ETM+ and a simple to use Google Earth Engine application, accurately characterized a 2009 algal bloom in western Lake Erie to show the model worked well for the analysis of temporal changes in algal conditions. Compared with chlorophyll-a data from the NLA, chlorophyll-a measurements with our Landsat TM/ETM+ model had almost the same correlation with lake's total phosphorus concentrations, especially when using multiple Landsat images. Therefore, Landsat measurements of chlorophyll-a have value for ecological assessments and managing algal problems in lakes. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： remote sensing Landsat water quality chlorophyll-a machine-learning algorithm Google Earth Engine

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Three-dimensional realizations of flood flow in large-scale rivers using the neural fuzzy-based machine-learning algorithms

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COMPUTERS & FLUIDS 2022年 246卷

作者： Zhang, Zexia Limaye, Ajay B. Khosronejad, Ali SUNY Stony Brook Civil Engn Dept Stony Brook NY 11794 USA Univ Virginia Dept Environm Sci Charlottesville VA 22904 USA

machine learning methods have been extensively used to study the dynamics of complex fluid flows. One such algorithm, known as adaptive neural fuzzy inference system (ANFIS), can generate data-driven predictions for flow fields, but has not been applied to natural geophysical flows in large-scale rivers. Herein, we demonstrate the potential of ANFIS to produce three-dimensional (3D) realizations of the instantaneous flood flow field in several large-scale, virtual meandering rivers. The 3D dynamics of flood flow in large-scale rivers were obtained using large-eddy simulation (LES). The LES results, i.e., the 3D velocity components, were employed to train the learnable coefficients of an ANFIS. The trained ANFIS, along with a few time-steps of LES results (precursor data) were then used to produce 3D realizations of flood flow fields in large-scale rivers with geometries other than the one the ANFIS was trained with. We also used the trained ANFIS to generate 3D realizations of river flow at a discharge other than that the ANFIS was trained with. The flow field results obtained from ANFIS were validated using separate LES runs to assess the accuracy of the 3D instantaneous realizations of the machine learning algorithm. An error analysis was conducted to quantify the discrepancies among the ANFIS and LES results for various flood flow predictions in large-scale rivers.

关键词： machine-learning algorithm Large-scale rivers Flood flow predictions Large-eddy simulation

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Raman spectroscopy of follicular fluid and plasma with machine-learning algorithms for polycystic ovary syndrome screening

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MOLECULAR AND CELLULAR ENDOCRINOLOGY 2021年 523卷 111139页

作者： Zhang, Xinyi Liang, Bo Zhang, Jun Hao, Xinyao Xu, Xiaoyan Chang, Hsun-Ming Leung, Peter C. K. Tan, Jichun China Med Univ Ctr Reprod Med Dept Obstet & Gynecol Shengjing Hosp Shenyang Liaoning Peoples R China Key Lab Reprod Dysfunct Dis & Fertil Remodeling L Shenyang Liaoning Peoples R China Shanghai Jiao Tong Univ Sch Life Sci & Biotechnol Joint Int Res Lab Metab & Dev Sci State Key Lab Microbial Metab Shanghai Peoples R China Basecare Med Device Co Suzhou Jiangsu Peoples R China Univ British Columbia BC Childrens Hosp Res Inst Dept Obstet & Gynaecol Room 317950 West 28th Ave Vancouver BC V5Z 4H4 Canada

Polycystic ovary syndrome (PCOS) is the main cause of anovulatory infertility and affects women throughout their lives. The specific diagnostic method is still under investigation. In the present study, we aimed to identify the metabolic tracks of the follicular fluid and plasma samples from women with PCOS by performing Raman spectroscopy with principal component analysis and spectral classification models. Follicular fluid and plasma samples obtained from 50 healthy (non-PCOS) and 50 PCOS women were collected and measured by Raman spectroscopy. Multivariate statistical methods and different machine-learning algorithms based on the Raman spectra were established to analyze the results. The principal component analysis of the Raman spectra showed differences in the follicular fluid between the non-PCOS and PCOS groups. The stacking classification models based on the k-nearest-neighbor, random forests and extreme gradient boosting algorithms yielded a higher accuracy of 89.32% by using follicular fluid than the accuracy of 74.78% obtained with plasma samples in classifying the spectra from the two groups. In this regard, PCOS may lead to the changes of metabolic profiles that can be detected by Raman spectroscopy. As a novel, rapid and affordable method, Raman spectroscopy combined with advanced machine-learning algorithms have potential to analyze and characterize patients with PCOS.

关键词： Polycystic ovary syndrome Raman spectroscopy machine-learning algorithm Follicular fluid Screening

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Compared Insights on machine-learning Anomaly Detection for Process Control Feature

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Computers, Materials & Continua 2022年第11期73卷 4033-4049页

作者： Ming Wan Quanliang Li Jiangyuan Yao Yan Song Yang Liu Yuxin Wan School of Information Liaoning UniversityShenyang110036China School of Computer Science and Technology Hainan UniversityHaikou570228China School of Physics Liaoning UniversityShenyang110036China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016China Department of Electrical Engineering and Information Technology Karlsruhe Institute of TechnologyKarlsruhe76131Germany

Anomaly detection is becoming increasingly significant in industrial cyber security,and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber ***,different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature *** a sequence,after developing one feature generation approach,the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning *** on process control features generated by directed function transition diagrams,this paper introduces five different machine-learning algorithms as alternative detection engines to discuss their matching ***,this paper not only describes some qualitative properties to compare their advantages and disadvantages,but also gives an in-depth and meticulous research on their detection accuracies and consuming *** the verified experiments,two attack models and four different attack intensities are defined to facilitate all quantitative comparisons,and the impacts of detection accuracy caused by the feature parameter are also comparatively *** experimental results can clearly explain that SVM(Support Vector machine)and WNN(Wavelet Neural Network)are suggested as two applicable detection engines under differing cases.

关键词： Anomaly detection machine-learning algorithm process control feature qualitative and quantitative comparisons

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