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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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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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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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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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Constitutive Model and Microstructure Evolution of Ti65 Titanium Alloy

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MATERIALS 2024年第10期17卷 2409页

作者： Sun, Tao Sun, Lili Teng, Haihao Liu, Wenhao Wang, Ruiqi Zhao, Xuanjie Zhou, Jie Chongqing Univ Coll Mat Sci & Engn Chongqing 400044 Peoples R China Sichuan Univ Coll Phys Chengdu 610064 Peoples R China China Natl Erzhong Grp Co Deyang Wanhang Die Forging Co Ltd Deyang 618000 Peoples R China

The hot deformation behavior and mechanism of Ti65 alloy with a bimodal microstructure were investigated by isothermal compression experiments conducted on the Thermecmastor-Z simulator equipment at temperatures ranging from 950 to 1110 degrees C and strain rates ranging from 0.01 to 10.0 s-1. The Arrhenius constitutive model, based on strain compensation, and Grey Wolf optimization-neural network with back propagation model (GWO-BP), were both established. The differences between the experimental and predicted value of flow stress were compared and analyzed using the two models. The results show that the prediction accuracy of GWO-BP in the two-phase region is higher than that of Arrhenius model. In the single-phase region, both methods demonstrated high prediction accuracy. Compared to the single-phase region, the flow stress of Ti65 alloy shows a higher degree of softening in the two-phase region. During deformation in the two-phase region, the initial lamellar alpha phase transformed from a kinked and elongated morphology to a globularized topography as the strain rate decreased. Boundary-splitting was the primary mechanism leading to the spheroidization process. The degree of recrystallization increased with the increase in strain rate during the deformation in the single-phase region, while dynamic recovery and strain-induced grain boundary migration were the main deformation mechanisms at a lower strain rate. Discontinuous dynamic recrystallization may be the dominant recrystallization mechanism under a high strain rate of 10 s-1.

关键词： constitutive equation Ti65 alloy machine-learning algorithm hot deformation

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Estimation of soil erodability parameters based on different machine algorithms integrated with remote sensing techniques

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2024年第15期21卷 9527-9540页

作者： Saygin, F. Aksoy, H. Alaboz, P. Birol, M. Dengiz, O. Sivas Univ Sci & Technol Fac Agr Sci & Technol Plant Prod & Technol Dept Sivas Turkiye Sinop Univ Ayancik Vocat Sch Dept Forestry & Forest Prod Sinop Turkiye Isparta Univ Appl Sci Fac Agr Dept Soil Sci & Plant Nutr Isparta Turkiye Minist Agr & Forestry Soil & Water Resources Dept Black Sea Agr Res Inst Samsun Turkiye Ondokuz Mayis Univ Agr Fac Plant Nutr & Soil Sci Dept Samsun Turkiye

Erosion causes significant damage to life and nature every year;therefore, controlling erosion is of great importance. Therefore, maintaining the balance between soil, plants, and water plays a vital role in controlling erosion. Aim of this study was to estimate some erodability parameters (structural stability index-SSI, aggregate stability-AS, and erosion ratio-ER) with indices and reflectance obtained via TripleSat satellite imagery using machine learning algorithms (support vector regression-SVR, artificial neural network-ANN, and K-nearest neighbors-KNN) in Samsun Province, Vezirkopru, Turkiye. Various interpolation methods (inverse distance weighting-IDW, radial basis function-RBF, and kriging) were also used to create spatial distribution maps of the study area for observed and predicted values. Estimates were made using NDVI, SAVI, and ASVI indices obtained from satellite images and NIR reflectance. Accordingly, the ANN algorithm yielded the lowest MAE (2.86%), MAPE (9.46%), and highest R2 (0.82) for SSI estimation. For AS and ER estimation, SVR had the highest predictive accuracy. Given the RMSE values in spatial distribution maps for observed and estimated values (SSI 7.861-7.248%, AS 14.485-14.536%, and ER 4.919-3.742%), the highest predictive accuracy was obtained with kriging. Thus, it was concluded that erosion parameters can be successfully estimated with reflectance and index values obtained from satellite images using SVR and ANN algorithms, and low-error distribution maps can be created using the kriging method.

关键词： Soil properties Erodibility factors machine-learning algorithm Remote sensing Kriging

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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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Integrated machine learning developed a prognosis-related gene signature to predict prognosis in oesophageal squamous cell carcinoma

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JOURNAL OF CELLULAR AND MOLECULAR MEDICINE 2024年第21期28卷 e70171页

作者： Tang, Peng Li, Baihui Zhou, Zijing Wang, Haitong Ma, Mingquan Gong, Lei Qiao, Yufeng Ren, Peng Zhang, Hongdian Tianjin Med Univ Natl Clin Res Ctr Canc Tianjins Clin Res Ctr Canc Tianjin Key Lab Digest CancDept Esophageal CancC Tianjin Peoples R China Tianjin Med Univ Natl Clin Res Ctr Canc Tianjins Clin Res Ctr Canc Dept Radiat OncolKey Lab Canc Prevent & TherapyC Tianjin Peoples R China

The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM systems cannot accurately predict its prognosis, thus necessitating a predictive model. In this study, a 17-gene prognosis-related gene signature (PRS) predictive model was constructed using the random survival forest algorithm as the optimal algorithm among 99 machine-learning algorithm combinations based on data from 260 patients obtained from TCGA and GEO. The PRS model consistently outperformed other clinicopathological features and previously published signatures with superior prognostic accuracy, as evidenced by the receiver operating characteristic curve, C-index and decision curve analysis in both training and validation cohorts. In the Cox regression analysis, PRS score was an independent adverse prognostic factor. The 17 genes of PRS were predominantly expressed in malignant cells by single-cell RNA-seq analysis via the TISCH2 database. They were involved in immunological and metabolic pathways according to GSEA and GSVA. The high-risk group exhibited increased immune cell infiltration based on seven immunological algorithms, accompanied by a complex immune function status and elevated immune factor expression. Overall, the PRS model can serve as an excellent tool for overall survival prediction in ESCC and may facilitate individualized treatment strategies and predction of immunotherapy for patients with ESCC.

关键词： machine-learning algorithm oesophageal squamous cell carcinoma predictive model random survival forest tumour-infiltrating immune cells

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Applying machine learning techniques to detect the deployment of spatial working memory from the spiking activity of MT neurons

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第2期20卷 3216-3236页

作者： Vivekanandhan, Gayathri Mehrabbeik, Mahtab Rajagopal, Karthikeya Jafari, Saja Lomber, Stephen G. Merrikhi, Yaser Chennai Inst Technol Ctr Artificial Intelligence Chennai India Amirkabir Univ Technol Dept Biomed Engn Tehran Polytech Tehran Iran Chennai Inst Technol Ctr Nonlinear Syst Chennai India Chandigarh Univ Dept Elect & Commun Engn Mohali 140413 Punjab India Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 Punjab India Amirkabir Univ Technol Hlth Technol Res Inst Tehran Polytech Tehran Iran McGill Univ Fac Med Dept Physiol Montreal PQ 316 Canada

Neural signatures of working memory have been frequently identified in the spiking activity of different brain areas. However, some studies reported no memory-related change in the spiking activity of the middle temporal (MT) area in the visual cortex. However, recently it was shown that the content of working memory is reflected as an increase in the dimensionality of the average spiking activity of the MT neurons. This study aimed to find the features that can reveal memory-related changes with the help of machine-learning algorithms. In this regard, different linear and nonlinear features were obtained from the neuronal spiking activity during the presence and absence of working memory. To select the optimum features, the Genetic algorithm, Particle Swarm Optimization, and Ant Colony Optimization methods were employed. The classification was performed using the Support Vector machine (SVM) and the K-Nearest Neighbor (KNN) classifiers. Our results suggest that the deployment of spatial working memory can be perfectly detected from spiking patterns of MT neurons with an accuracy of 99.65 +/- 0.12 using the KNN and 99.50 +/- 0.26 using the SVM classifiers.

关键词： working memory middle temporal area firing rate signal machine-learning algorithm

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