Identification of lethality-related m7G methylation modification patterns and the regulatory features of immune microenvironment in sepsis by Dan Wang, Rujie Huo, Lu Ye

“…Lethality-related m7G signature genes were then screened using machine learning methods, followed by the construction of a survival recognition model. …”
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Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles by Khaled Tarmissi, Hanan Abdullah Mengash, Noha Negm, Yahia Said, Ali M. Al-Sharafi

“…Nowadays, deep learning (DL) and machine learning (ML) models are critical to enhancing object detection in AVs, particularly in adversarial weather conditions. …”
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FedDrip: Federated Learning With Diffusion-Generated Synthetic Image by Karin Huangsuwan, Timothy Liu, Simon See, Aik Beng Ng, Peerapon Vateekul

“…In the realm of machine learning in healthcare, federated learning (FL) is often recognized as a practical solution for addressing issues related to data privacy and data distribution. …”
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Insight on the Genetics of Atrial Fibrillation in Puerto Rican Hispanics by Ariel F. Gonzalez-Cordero, Jorge Duconge-Soler, Hilton Franqui-Rivera, Roberto Feliu-Maldonado, Abiel Roche-Lima, Israel Almodovar-Rivera

“…Furthermore, we performed a machine learning analysis and determined that Native American admixture and heart failure were strongly predictive of AF in PHR. …”
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Risk prediction tools for pressure injury occurrence: an umbrella review of systematic reviews reporting model development and validation methods by Bethany Hillier, Katie Scandrett, April Coombe, Tina Hernandez-Boussard, Ewout Steyerberg, Yemisi Takwoingi, Vladica Velickovic, Jacqueline Dinnes

“…Of the seven reviews reporting model development and validation, six included only machine learning models. Two reviews included external validations of models, although only one review reported any details on external validation methods or results. …”
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Novel (±)-trans-β-lactam ureas: Synthesis, in silico and in vitro biological profiling by Jurin Mladenka, Stepanić Višnja, Bojanić Krunoslav, Vadlja Denis, Kontrec Darko, Dražić Tonko, Roje Marin

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Molecularly redefining small bowel adenocarcinoma to accelerate precision patient care – protocol of a multicenter observational cohort biomarker study by Rasmus Haunstrup Døssing, Julia Johanna Almer Broman, Colm J. O’Rourke, Elizaveta Mitkina Tabaksblat, Jesper Bøje Andersen, Carsten Palnæs Hansen, Tim Svenstrup Poulsen, Estrid V. S. Høgdall, Jakob Hagen Vasehus Schou, Dan Høgdall

“…Secondary outcomes include correlating these subgroups with clinical features, treatment responses, and patient outcomes. Machine learning algorithms will be employed for bioinformatic analyses to interpret molecular data. …”
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Spatiotemporal changes of ecosystem health and the impact of its driving factors on the Loess Plateau in China by Xuepeng Zhang, Taixia Wu, Qiqi Du, Ninglei Ouyang, Wei Nie, Yang Liu, Peng Gou, Guangchao Li

“…Based on this idea, a new framework for assessing EH is proposed. The efficient machine learning model (light gradient boosting machine model) and the variable explanation model (SHapley Additive exPlanation model) are combined to quantify the functional relationships between various driving factors and EH, thereby exploring the impacts of climatic factors, socioeconomic development (SED), and ecological restoration projects (ERP) on EH. …”
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Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone by Christophe Bonneville, Nathan Bieberdorf, Arun Hegde, Mark Asta, Habib N. Najm, Laurent Capolungo, Cosmin Safta

“…In this paper, we propose a U-shaped adaptive Fourier neural operator (U-AFNO), a machine learning (ML) based model inspired by recent advances in neural operator learning. …”
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Characterization of G2/M checkpoint classifier for personalized treatment in uterine corpus endometrial carcinoma by Yiming Liu, Yusi Wang, Shu Tan, Xiaochen Shi, Jinglin Wen, Dejia Chen, Yue Zhao, Wenjing Pan, Zhaoyang Jia, Chunru Lu, Ge Lou

“…Ultimately, an artificial neural network (ANN) and machine learning were employed to develop the G2MC subtypes classifier. …”
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Resolving ambiguity in natural language for enhancement of aspect-based sentiment analysis of hotel reviews by Asma Nadeem, Malik Muhammad Saad Missen, Mana Saleh Al Reshan, Muhammad Ali Memon, Yousef Asiri, Muhammad Ali Nizamani, Mohammad Alsulami, Asadullah Shaikh

“…It emphasizes that if we combine the potential of NLP techniques along with state-of-the-art machine learning frameworks, we can shape the future of this field.…”
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Classifying Sunn Pest Damaged and Healthy Wheat Grains Across Different Species with YOLOV8 and Vision Transformers by Melike Çolak, Özgü Özkan, Ali Berkol, Nergis Pervan Akman, Murat Ardıç, Okan Sezer, Nazife Gözde Ayter Arpacıoğlu, Zekiye Budak Başçiftçi, Murat Olgun

“…Over time, the number of researchers focusing on this problem by using various machine learning algorithms and image processing techniques has increased. …”
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MMP9-Associated Tumor Stem Cells, CCL1-Silenced Dendritic Cells, and Cytokine-Induced Killer Cells Have a Remarkable Therapeutic Efficacy for Acute Myeloid Leukemia by Activating T... by Min Dong, Guozhen Zhang, Jie Meng, Biou Liu, Duanfeng Jiang, Feng Liu

“…Gene expression profiles of leukemia patients were obtained from TCGA, DC cell components were evaluated using the quanTIseq method, and cancer stem cell scores were estimated using machine learning methods. The transcriptomes were obtained in DC-CIK cells from normal and AML patients by high-throughput sequencing. …”
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Prediction of lithium-ion battery SOC based on IGA-GRU and the fusion of multi-head attention mechanism by Pei Tang, Minnan Jiang, Weikai Xu, Zhengyu Ding, Mao Lv

“…The data preprocessing part adopts the sliding window technique, through which the original time series data is converted into several different training samples when training the machine learning model, as a way to increase the diversity of the dataset and improve the robustness of the model. …”
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Evaluation of Rainfall-Induced Accumulation Landslide Susceptibility Based on Remote Sensing Interpretation by Zhen Wu, Runqing Ye, Jue Huang, Xiaolin Fu, Yao Chen

“…Eight evaluation factors were extracted using multi-source data, including lithology, elevation, slope, remote sensing image texture features, and the normalized difference vegetation index (NDVI). Various machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and BP Neural Network models, were employed to assess the susceptibility of rainfall-induced accumulation landslides in the study area. …”
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Exploring the predictive power of antinuclear antibodies and Rheumatoid factor correlations in anticipating therapeutic outcomes for female patients with coexisting Sjögren's syndr... by Anitha Krishnan Pandarathodiyil, Hema Shree K, Pratibha Ramani, B. Sivapathasundharam, Ramya Ramadoss

“…These findings suggest that machine learning models could enhance personalized treatment strategies for patients with SS and RA, but additional validation with larger datasets is required.…”
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Revolutionizing ESCC prognosis: the efficiency of tumor-infiltrating immune cells (TIIC) signature score by Haixia Wang, Shaowei Ma, Zixin Yang, Ren Niu, Haiyong Zhu, Shujun Li, Shaolin Gao, Zhirong Li, Yanhua Tian

“…Tissue Specific Index (TSI) was defined to identify potential TIIC-RNAs from the TME. Twenty machine learning algorithms were further applied to evaluate the prognostic efficacy of TIIC signature score. …”
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Precision fetal cardiology detects cyanotic congenital heart disease using maternal saliva metabolome and artificial intelligence by Ray Bahado-Singh, Nadia Ashrafi, Amin Ibrahim, Buket Aydas, Ali Yilmaz, Perry Friedman, Stewart F. Graham, Onur Turkoglu

“…We therefore sought to use a Precision Fetal Cardiology based approach combining metabolomic profiling of maternal saliva and machine learning, a major branch of artificial intelligence (AI), for the prenatal detection of isolated, non-syndromic cyanotic CHD. …”
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Exploring RNA cargo in extracellular vesicles for pleural mesothelioma detection by Agnieszka Kraft, Michaela B. Kirschner, Vanessa Orlowski, Manuel Ronner, Caroline Bodmer, Valentina Boeva, Isabelle Opitz, Mayura Meerang

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A Novel Stacked Model for Classification of Vocal Cord Paralysis Over Imbalanced Vocal Data by K. Jayashree Hegde, K. Manjula Shenoy, K. Devaraja

“…Over time, many classification systems have been developed for voice-related disorders using machine learning methods and limited usage of deep learning techniques. …”
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