The role of epigenetic regulation in pancreatic ductal adenocarcinoma progression and drug response: an integrative genomic and pharmacological prognostic prediction model by Kang Fu, Junzhe Su, Yiming Zhou, Xiaotong Chen, Xiao Hu

“…Weighted gene co-expression network analysis (WGCNA) identified key epigenetic modules. A machine learning-based prognostic model was constructed using multiple algorithms, including Lasso and Random Survival Forest. …”
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Leveraging environmental microbial indicators in wastewater for data-driven disease diagnostics by Gayatri Gogoi, Gayatri Gogoi, Sarangthem Dinamani Singh, Sarangthem Dinamani Singh, Devpratim Koch, Devpratim Koch, Emon Kalyan, Rashmi Rani Boro, Aradhana Devi, Hridoy Jyoti Mahanta, Hridoy Jyoti Mahanta, Pankaj Bharali, Pankaj Bharali

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Smartphone sensor-based depression detection in campus environments: a proof-of-concept study with small-sample behavioral analysis by Yichen Bai, Yueze Liu, Yang Zhang, Amr Tolba

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Comparing 2D and 3D Feature Extraction Methods for Lung Adenocarcinoma Prediction Using CT Scans: A Cross-Cohort Study by Margarida Gouveia, Tânia Mendes, Eduardo M. Rodrigues, Hélder P. Oliveira, Tania Pereira

“…Computed Tomography (CT) is widely used for detecting tumours and their phenotype characteristics, for an early and accurate diagnosis that impacts patient outcomes. Machine learning algorithms have already shown the potential to recognize patterns in CT scans to classify the cancer subtype. …”
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Development and Use of Biotechnological System Models in Applied Scientific Research by M. A. Kerimov

“…(Research purpose) The research aims to substantiate the conceptual approach to the functioning of an «operator-machine-animal» biotechnical system, taking into account the subsystem interaction patterns. …”
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Exploring the Global and Regional Factors Influencing the Density of <i>Trachurus japonicus</i> in the South China Sea by Mingshuai Sun, Yaquan Li, Zuozhi Chen, Youwei Xu, Yutao Yang, Yan Zhang, Yalan Peng, Haoda Zhou

“…In this cross-disciplinary investigation, we uncover a suite of previously unexamined factors and their intricate interplay that hold causal relationships with the distribution of <i>Trachurus japonicus</i> in the northern reaches of the South China Sea, thereby extending the existing research paradigms. Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of <i>T. japonicus</i> density. …”
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Unveiling diagnostic biomarkers and therapeutic targets in lung adenocarcinoma using bioinformatics and experimental validation by Sixuan Wu, Yuanbin Tang, Qihong Pan, Yaqin Zheng, Yeru Tan, Junfan Pan, Yuehua Li

“…Ten of the gene co-expression modules constructed by WGCNA were identified, with the red module having the most significant correlation with clinical features. In addition, a machine learning model constructed based on Stepglm[backward] with the random forest algorithm achieved the highest C-index (0.999) and screened eight core genes, among which ST14 was noted for its excellent predictive ability. …”
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Graph Neural Network Aided Detection for the Multi-User Multi-Dimensional Index Modulated Uplink by Xinyu Feng, Mohammed El-Hajjar, Chao Xu, Lajos Hanzo

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Preoperative digital 6-minute walk test reveals risk of postoperative pulmonary complications in patients undergoing heart valve surgery: a pilot feasibility study by Lixuan Li, Yuqiang Wang, Zhengbo Zhang, Zeruxin Luo, Wenqing Wang, Jiachen Wang, Xiaoli Liu, Ying Shi, Tian Yuan, Yong Fan, Hong Liang, Yingqiang Guo, Buqing Wang, Jing Wang, Jiaoxue Deng

“…We extracted 94 physiological features across 6MWT phases (baseline, walking, recovery) and clinical variables, developing predictive models using five machine learning algorithms evaluated through rigorous five-fold cross-validation. …”
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Early Diabetic Retinopathy Detection from OCT Images Using Multifractal Analysis and Multi-Layer Perceptron Classification by Ahlem Aziz, Necmi Serkan Tezel, Seydi Kaçmaz, Youcef Attallah

“…<b>Results:</b> A comparative evaluation of several machine learning algorithms was conducted to assess classification performance. …”
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Hierarchizing multi-scale environmental effects on agricultural pest population dynamics: a case study on the annual onset of Bactrocera dorsalis population growth in Senegalese or... by Caumette, Cécile, Diatta, Paterne, Piry, Sylvain, Chapuis, Marie-Pierre, Faye, Emile, Sigrist, Fabio, Martin, Olivier, Papaïx, Julien, Brévault, Thierry, Berthier, Karine

“…We then developed a flexible analysis pipeline centred on a recent machine learning algorithm, which allows the combination of gradient boosting and grouped random effects models or Gaussian processes, to hierarchize the effects of multi-scale environmental variables on the onset of annual BD population growth in orchards. …”
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Rolling window for detecting multiple Chan signatures to diagnose excessive water production by Ahmed MohamedSalih Musa Hamdoon, A. P. Mohammed Abdalla Ayoub Mohammed, A. P. Khaled Abdalla Elraies

“…Throughout, an iterative optimization process, window size was determined as seven points, considering pattern duration. Eight algorithms were evaluated, with Support Vector Machines (SVM) and Random Forest (RF) achieving a remarkable 94% F1 score while the remaining algorithms averaged 93%.…”
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Hybrid AI and semiconductor approaches for power quality improvement by Ravikumar Chinthaginjala, Asadi Srinivasulu, Anupam Agrawal, Tae Hoon Kim, Sivarama Prasad Tera, Shafiq Ahmad

“…The research addresses key power quality challenges - including voltage sags, swells, harmonics, and transient disturbances - through a data-driven framework that combines traditional control techniques with adaptive learning models. A variety of algorithms, including Support Vector Machines (SVM), Random Forests, Neural Networks, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, were tested using real-time data. …”
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Prescribing the Future: The Role of Artificial Intelligence in Pharmacy by Hesham Allam

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Cost-Efficient RSSI-Based Indoor Proximity Positioning, for Large/Complex Museum Exhibition Spaces by Panos I. Philippopoulos, Kostas N. Koutrakis, Efstathios D. Tsafaras, Evangelia G. Papadopoulou, Dimitrios Sigalas, Nikolaos D. Tselikas, Stefanos Ougiaroglou, Costas Vassilakis

“…A total 15 methods/algorithms were evaluated against prediction accuracy across 20 RSSI datasets, incorporating diverse hall cell allocations and visitor movement patterns. …”
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AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP by Dr. Bharti Khemani, Dr. Sachin Malave, Samyukta Shinde, Mandvi Shukla, Razzaq Shikalgar, Harshita Talwar

“…This research underscores the potential of predictive modeling to enhance pharmacovigilance efforts and ensure safer clinical trial outcomes. • The research methodology includes a comparison of supervised learning algorithms, such as Logistic Regression, Random Forest, Gradient Boost, CNN, and genetic algorithms, to identify patterns and anomalies in clinical trial data. …”
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Analyzing the performance of biomedical time-series segmentation with electrophysiology data by Richard Redina, Jakub Hejc, Marina Filipenska, Zdenek Starek

“…Traditional rule-based and feature engineering approaches often struggle with complex clinical patterns and noise. Recent deep learning advancements offer solutions, showing various benefits and drawbacks in segmentation tasks. …”
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Background for modeling the dynamic characteristics of advanced spacecraft drives considering the operation of oscillators by A. N. Sova, M. I. Stepanov, V. A. Sova, A. I. Bykov

“…Rational versions of layout and approximate cycle patterns of the operation of advanced space vehicles are formed to reduce microperturbations from driving gear with rotating masses.Research Results. …”
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Deep-m6Am: a deep learning model for identifying N6, 2′-O-Dimethyladenosine (m6Am) sites using hybrid features by Islam Uddin, Salman A. AlQahtani, Sumaiya Noor, Salman Khan

“…Finally, a multilayer deep neural network (DNN) is used as a classification algorithm for identifying m6Am sites. The performance of the proposed model was evaluated in comparison with traditional machine learning (ML) algorithms and existing models. …”
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Feature extraction and fault diagnosis of gearbox based on ICEEMDAN, MPE, RF and SVM by DING Xiaofeng, ZHANG Yuhua

“…Finally, the importance of such features was evaluated by the RF algorithm, and the sensitive features with high importance were selected to form the optimal feature subset as the input to SVM for fault pattern recognition. …”
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