Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds by Mengdie Fan, Chenhui Sang, Hua Li, Yue Wei, Bin Zhang, Yang Xing, Jing Zhang, Jie Yin, Wei An, Bing Shao

“…Traditional retention time approaches heavily rely on the use of standard compounds, which is limited by the speed of synthesis and manufacture of standard products, and is time-consuming and labor-intensive. Recently, machine learning and artificial intelligence algorithms have been applied to retention time prediction, which show unparalleled advantages over traditional experimental methods. …”
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Question–Answer Methodology for Vulnerable Source Code Review via Prototype-Based Model-Agnostic Meta-Learning by Pablo Corona-Fraga, Aldo Hernandez-Suarez, Gabriel Sanchez-Perez, Linda Karina Toscano-Medina, Hector Perez-Meana, Jose Portillo-Portillo, Jesus Olivares-Mercado, Luis Javier García Villalba

“…Traditional static and dynamic analysis techniques, although widely used, often exhibit high false-positive rates, elevated costs, and limited interpretability. Machine Learning (ML)-based approaches aim to overcome these limitations but encounter challenges related to scalability and adaptability due to their reliance on large labeled datasets and their limited alignment with the requirements of secure development teams. …”
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End-to-End Stroke Imaging Analysis Using Effective Connectivity and Interpretable Artificial Intelligence by Wojciech Ciezobka, Joan Falco-Roget, Cemal Koba, Alessandro Crimi

“…This transparent analytical framework not only enhances clinical interpretability but also instills confidence in decision-making processes, crucial for translating research findings into clinical practice. Our proposed machine learning pipeline showcases the potential of reservoir computing to define causality and therefore directed graph networks, which can in turn be used in a directed graph classifier and explainable analysis of neuroimaging data. …”
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An Explainable Artificial Intelligence Text Classifier for Suicidality Prediction in Youth Crisis Text Line Users: Development and Validation Study by Julia Thomas, Antonia Lucht, Jacob Segler, Richard Wundrack, Marcel Miché, Roselind Lieb, Lars Kuchinke, Gunther Meinlschmidt

“… BackgroundSuicide represents a critical public health concern, and machine learning (ML) models offer the potential for identifying at-risk individuals. …”
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Development and validation of an integrated model for the diagnosis of liver cirrhosis with portal vein thrombosis combined with endoscopic characters and blood biochemistry data:... by Jie Yang, Xu Zhang, Jia Chen, Xianghong Hou, Minghong Shi, Longlong Yin, Longchun Hua, Cheng Wang, Xiaolong Han, Shuyan Zhao, Guolan Kang, Ping Mai, Rui Jiang, Hongwei Tian

“…Variables were collected from blood test and endoscopic signs using machine learning method (ML). Logistic regression determined risk factors. …”
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Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousness by Hui Li, Hui Li, Hui Li, Linghui Dong, Linghui Dong, Linghui Dong, Wenlong Su, Wenlong Su, Ying Liu, Ying Liu, Zhiqing Tang, Zhiqing Tang, Xingxing Liao, Xingxing Liao, Junzi Long, Junzi Long, Xiaonian Zhang, Xinting Sun, Hao Zhang, Hao Zhang, Hao Zhang, Hao Zhang

“…A combination of machine learning and SHapley Additive exPlanations (SHAP) were used to develop predictive model and interpret the results.ResultsThe results indicated significant abnormalities in low-frequency spectral power, microstate parameters, and amplitudes of MMN and P3a in MCS and UWS. …”
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A CNN-LSTM Phase Compensation Method for Unidirectional Two-way Radio Frequency Transmission System by Jiahui Cheng, Zhengkang Wang, Yaojun Qiao, Hao Gao, Chenxia Liu, Zhuoze Zhao, Jie Zhang, Baodong Zhao, Bin Luo, Song Yu

“…This is the first-time machine learning (ML) has been used to mitigate the effects of optical path asymmetry caused by temperature variations on radio frequency (RF) transmission systems. …”
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Synthesizing Local Capacities, Multi-Source Remote Sensing and Meta-Learning to Optimize Forest Carbon Assessment in Data-Poor Regions by Kamaldeen Mohammed, Daniel Kpienbaareh, Jinfei Wang, David Goldblum, Isaac Luginaah, Esther Lupafya, Laifolo Dakishoni

“…To improve forest carbon assessment, we employed stacked generalization, combining multiple machine learning algorithms to leverage their complementary strengths and address individual limitations. …”
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Mortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis by Sharad Patel, Gurkeerat Singh, Samson Zarbiv, Kia Ghiassi, Jean-Sebastien Rachoin

“…We hypothesize that S/F is noninferior to P/F as a predictive feature for ICU mortality. Using a machine-learning approach, we hope to demonstrate the relative mortality predictive capacities of S/F and P/F. …”
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Evaluating the level of digitalization of the innovation process with artificial intelligence approach in the digital transformation of knowledge-based companies by ali Bagheri, reza radfar, sepehr ghazinoory

“…As a subset of artificial intelligence, machine learning algorithms create a mathematical model based on sample data or "training data" in order to predict or make decisions without overt planning (Du et al., 2019). …”
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Identification of EARS2 as a Potential Biomarker with Diagnostic, Prognostic, and Therapeutic Implications in Colorectal Cancer by Wang L, Deng X, Tang J, Gong Y, Bu S, Li Z, Liao B, Ding Y, Dai T, Liao Y, Li Y

“…This study identifies key genes associated with lactic acid metabolism and explore their impact on CRC.Patients and Methods: This study utilized data from The Cancer Genome Atlas, Gene Expression Omnibus, other public databases, and our institutional resources. Machine learning identified key lactate metabolism-related genes. …”
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Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals by Gulay Tasci, Prabal Datta Barua, Dahiru Tanko, Tugce Keles, Suat Tas, Ilknur Sercek, Suheda Kaya, Kubra Yildirim, Yunus Talu, Burak Tasci, Filiz Ozsoy, Nida Gonen, Irem Tasci, Sengul Dogan, Turker Tuncer

“…<b>Background:</b> Electroencephalography (EEG) signal-based machine learning models are among the most cost-effective methods for information retrieval. …”
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Spatial deconvolution from bulk DNA methylation profiles determines intratumoral epigenetic heterogeneity by Binbin Liu, Yumo Xie, Yu Zhang, Guannan Tang, Jinxin Lin, Ze Yuan, Xiaoxia Liu, Xiaolin Wang, Meijin Huang, Yanxin Luo, Huichuan Yu

“…Conclusion By developing a 7-loci panel using a machine learning approach combined with the QASM assay for PCR-based application, we present a valuable method for evaluating intratumoral heterogeneity. …”
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Quo vadis autoimmune hepatitis? - Summary of the 5th international autoimmune hepatitis group research workshop 2024Keypoints by Bastian Engel, David N. Assis, Mamatha Bhat, Jan Clusmann, Joost PH. Drenth, Alessio Gerussi, María-Carlota Londoño, Ye Htun Oo, Ida Schregel, Marcial Sebode, Richard Taubert

“…The specific objectives of this year's 5th Workshop were: (1) To further improve diagnostics. (2) Initiate clinical trials including knowledge transfer on drugs from extrahepatic immune-mediated diseases, including B cell-depleting CAR T cells. (3) Utilisation of multi-omics approaches to improve the understanding of disease pathogenesis. (4) Application of machine learning-based approaches established in oncology or transplantation medicine to improve diagnosis and outcome prediction in AIH.…”
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AI prediction model for endovascular treatment of vertebrobasilar occlusion with atrial fibrillation by Zhi-Xin Huang, Andrea M. Alexandre, Alessandro Pedicelli, Xuying He, Quanlong Hong, Yongkun Li, Ping Chen, Qiankun Cai, Aldobrando Broccolini, Luca Scarcia, Serena Abruzzese, Carlo Cirelli, Mauro Bergui, Andrea Romi, Erwah Kalsoum, Giulia Frauenfelder, Grzegorz Meder, Simona Scalise, Maria Porzia Ganimede, Luigi Bellini, Bruno Del Sette, Francesco Arba, Susanna Sammali, Andrea Salcuni, Sergio Lucio Vinci, Giacomo Cester, Luisa Roveri, Xianjun Huang, Wen Sun

“…While 45.1% achieved favorable outcomes at 90 days, our advanced machine learning approach unveiled subtle interaction effects among clinical variables not captured by traditional statistical methods. …”
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QUIET WARRIOR – Rationale and design: An ancillary study to the Women's IschemiA TRial to Reduce Events in Nonobstructive CAD (WARRIOR) by Osama Dasa, Eileen Handberg, Damini Dey, Pinaki Sarder, Margaret C. Lo, Balaji K. Tamarappoo, Steven M. Smith, Leslee J. Shaw, C. Noel Bairey Merz, Carl J. Pepine

“…Advanced imaging techniques and machine-learning models will be employed to quantify plaque features and predict clinical outcomes. …”
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In-Season Automated Mapping of Xinjiang Cotton Based on Cumulative Spectral and Phenological Characteristics by Yongsheng Huang, Yaozhong Pan, Yu Zhu, Xiufang Zhu, Xingsheng Xia, Qiong Chen, Jufang Hu, Hongyan Che, Xuechang Zheng, Lingang Wang

“…Methods based on machine learning, and deep learning, rely on a large number of training samples, which is time-consuming and laborious. …”
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Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska by Pratima Khatri-Chhetri, Hans-Erik Andersen, Bruce Cook, Sean M. Hendryx, Liz van Wagtendonk, Van R. Kane

“…In this study, we present a framework for forest type classification combining field plots and high-resolution remote sensing data using machine learning models in the boreal forest of Interior Alaska. …”
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Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial. by Hee Yun Seol, Pragya Shrestha, Joy Fladager Muth, Chung-Il Wi, Sunghwan Sohn, Euijung Ryu, Miguel Park, Kathy Ihrke, Sungrim Moon, Katherine King, Philip Wheeler, Bijan Borah, James Moriarty, Jordan Rosedahl, Hongfang Liu, Deborah B McWilliams, Young J Juhn

“…<h4>Measurements</h4>Intervention was a quarterly A-GPS report to clinicians including relevant clinical information for asthma management from EHRs and machine learning-based prediction for risk of asthma exacerbation (AE). …”
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Empirical estimation of saturated soil-paste electrical conductivity in the EU using pedotransfer functions and Quantile Regression Forests: A mapping approach based on LUCAS topso... by Calogero Schillaci, Simone Scarpa, Felipe Yunta, Aldo Lipani, Fernando Visconti, Gábor Szatmári, Kitti Balog, Triven Koganti, Mogens Greve, Giulia Bondi, Georgios Kargas, Paraskevi Londra, Fuat Kaya, Giuseppe Lo Papa, Panos Panagos, Luca Montanarella, Arwyn Jones

“…In this work, using the LUCAS 2018 dataset, we provide an empirically-derivedpedotransfer function to convert diluted EC1:5 to saturated ECe using the LUCAS soil texture and soil organic carbon, and a framework for ECe mapping with a machine-learning algorithm named Quantile Regression Forest. …”
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