Identification of glycolysis-related molecular subtypes and prognostic model in intrahepatic cholangiocarcinoma by Yue Wang, Pengxiang Wang, Hui Liu, Haokang Feng, Muzi Cao, Zefan Zhang, Keqiang Rao, Jia Fan, Xiutao Fu, Yunfan Sun

“…A 9-gene risk model (ALDH1B1, DDIT4, GALE, HK1, HMMR, PGAM1, PGK1, PLOD1, SAP30) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and multivariate Cox regression analysis. …”
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EEMtoolbox: A user‐friendly R package for flexible ensemble ecosystem modelling by Luz Valerie Pascal, Sarah A. Vollert, Malyon D. Bimler, Christopher M. Baker, Maude Vernet, Stefano Canessa, Christopher Drovandi, Matthew P. Adams

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Concerning the Use of Indicators’ Marginal (Threshold) Values of the Russian Federation Economic Security by A. B. Vissarionov, R. R. Gumerov

“…In conclusion, the article substantiates a multistage algorithm for analyzing and assessing the values of economic security indicators.…”
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Development of metastasis and survival prediction model of luminal and non-luminal breast cancer with weakly supervised learning based on pathomics by Hui Liu, Linlin Ying, Xing Song, Xueping Xiang, Shumei Wei

“…The WSI was then partitioned into smaller tiles measuring 512 × 512 pixels. Three networks, namely Densely Connected Convolutional Network 121 (DenseNet121), Residual Network (ResNet50), and Inception_v3, were assessed. …”
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Real-Time Estimation Method of P-Wave Slowness Based on Kalman Filtering and STC by SUN Hao, CHEN Wenhui, SUN Xuekai, YU Weidong, LI Siyi, ZHAO Peng

“…Aiming at the above problems, a novel method combining the Kalman filter algorithm with the slowness-time coherence (STC) method is proposed. …”
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Predictive machine-learning model for screening iron deficiency without anaemia: a retrospective cohort study by Girish N Nadkarni, Orly Efros, Eyal Klang, Shelly Soffer, Gili Kenet, Aya Mudrik, Renana Robinson

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Machine learning approaches for predicting the link of the global trade network of liquefied natural gas. by Pei Zhao, Hao Song, Guang Ling

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Predicting biogas production in real scale anaerobic digester under dynamic conditions with machine learning approach by M. Erdem Isenkul, Sevgi Güneş-Durak, Yasemin Poyraz Kocak, İnci Pir, Mertol Tüfekci, Güler Türkoğlu Demirkol, Selçuk Sevgen, Aslı Seyhan Çığgın, Neşe Tüfekci

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KERATOCONUS DETECTION USING DEEP LEARNING by Younis Abbosh, Shatha Ali, Dia Ali, Iman Jasim

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A machine learning approach to identifying key predictors of Peruvian school principals' job satisfaction by Luis Alberto Holgado-Apaza, Dany Dorian Isuiza-Perez, Nelly Jacqueline Ulloa-Gallardo, Yban Vilchez-Navarro, Ruth Nataly Aragon-Navarrete, Wilian Quispe Layme, Marleny Quispe-Layme, Danger David Castellon-Apaza, Remo Choquejahua-Acero, Jaime Cesar Prieto-Luna

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Development and validation of a quick screening tool for predicting neck pain patients benefiting from spinal manipulation: a machine learning study by Changxiao Han, Guangyi Yang, Haibao Wen, Minrui Fu, Bochen Peng, Bo Xu, Xunlu Yin, Ping Wang, Liguo Zhu, Minshan Feng

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Policies and programmes to improve preconception nutrition in South Asia by Avishek Hazra, Tashi Choedon, Monica Shrivastav, Raj Kumar Verma, Cheshta Gulati, Dhammica Rowel, Abner Daniel, Preetu Mishra, Naveen Paudyal, Naureen Arshad, Muhammad Salman, Wisal Khan, Khadija Khalif Osman Warfa, Muhammad Amin, Ahmadwali Aminee, Ireen Akhter Chowdhury, Kinley Dorji, Indrani Chakma, Aishath Shahula Ahmed, Hari Prasad Pokhrel, Danielle Schoenaker, Jane Hirst, Ranadip Chowdhury, Naomi M. Saville, Faith Miller, Zivai Murira, Vani Sethi

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OncoPSM: an interactive tool for cost-effectiveness analysis using partitioned survival models in oncology trial by Xulong Qiu, Xulong Qiu, Jidi Chen, Jidi Chen, Muting Wang, Kaixin Zheng, Ruixiong Li

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Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative‌ brain metastasis risk in non-small cell lung cancer by Ye Niu, Hao-Bo Jia, Xue-Meng Li, Wen-Juan Huang, Ping-Ping Liu, Le Liu, Zeng-Yao Liu, Qiu-Jun Wang, Yuan-Zhou Li, Shi-Di Miao, Rui-Tao Wang, Ze-Xun Duan

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Drought Detection in Satellite Imagery: A Layered Ensemble Machine Learning Approach by Muhammad Owais Raza, Naeem Ahmed Mahoto, Mana Saleh Al Reshan, Ali Alqazzaz, Adel Rajab, Asadullah Shaikh

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Construction of a machine learning-based prediction model for mitral annular calcification by LI Runqian, TAN Yanyi, GE Tiantian, QI Lei, BAI Song, TONG Jiayi

“…The subjects were randomly divided into a training set (350 cases) and a test set (150 cases) at a 7∶3 ratio. Nine machine learning algorithms, including logistic regression, relaxed support vector machines (RSVM) , decision tree, elastic net, multilayer perceptron, K-nearest neighbors, random forest, extreme gradient boosting (XGBoost) , and light gradient boosting machine (LightGBM) , were used to build prediction models for MAC. …”
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FLIGHT EXPERT RISK ASSESSMENT OF AIRCRAFT GROUP AT THEIR PROXIMITY USING A PROGRAM-MANAGER by D. A. Mikhaylin

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Rationale and feasibility of a rapid integral biomarker program that informs immune-oncology clinical trials: the ADVISE trial by Akshita Gupta, Riyue Bao, F Stephen Hodi, Jaclyn Neely, Jason J Luke, Ashish Massey, Santanu Dutta, Janis Taube, George Lee, Katherine Bever, Peter Szabo, David Yao, Rachel Tam, Tim Reilly

“…Tumor tissue blocks of melanoma, non-small cell lung cancer, renal cell carcinoma, urothelial carcinoma, squamous cell carcinoma of the head and neck, and gastroesophageal junction/gastric cancer were stained by IHC to assess expression of CD8, colony-stimulating factor 1 receptor, glucocorticoid-induced tumor necrosis factor receptor (GITR), indoleamine 2,3-dioxygenase 1, lymphocyte-activation gene 3, NKp46, forkhead box P3, and PD-L1. …”
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A novel model for predicting immunotherapy response and prognosis in NSCLC patients by Ting Zang, Xiaorong Luo, Yangyu Mo, Jietao Lin, Weiguo Lu, Zhiling Li, Yingchun Zhou, Shulin Chen

“…Results The RF model incorporated RDW-SD, MCV, PDW, CD3+CD8+, APTT, P-LCR, Ca, MPV, CD4+/CD8+ ratio, and AST. …”
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Numerical-analytical modeling of oil extraction from oil fields with a gas cap using horizontal wells with automatic history matching by D. V. Shevchenko, A. A. Salamatin, A. D. Yarullin, S. A. Usmanov, V. V. Saveliev, V. A. Sudakov, A. P. Roschektaev, E. V. Yudin, D. S. Vorobyov, V. V. Sorokina, A. A. Sveshnikova

“…The numerical scheme of the model was implemented as a computational library in the Python 3.6 programming language.The algorithm was tested on oil deposits with a gas cap in the South Yamal oil and gas region and showed good results in history matching and forecasting calculations. …”
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