Mechanisms of drug resistance to neoadjuvant chemotherapy in breast cancer by K. A. Aliev, E. Yu. Zyablitskaya, T. P. Makalish, L. E. Sorokina, E. R. Asanova

“…The presented results can serve as the basis for the creation of diagnostic algorithms that have predictive value regarding the effectiveness of NCT, and also to help identify new targets to justify the use of combined breast cancer treatments in the early stage.…”
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Emerging Healthcare Technologies in Gastrointestinal Endoscopy: 75 Years of Evolution by Irina Florina CHERCIU HARBIYELI, Elena Daniela BURTEA, Vlad IOVANESCU, Dan Nicolae FLORESCU, Ion ROGOVEANU, Dan Ionut GHEONEA, Adrian SAFTOIU

“…One of the most significant advancements is AI-driven endoscopy systems (EndoBrain, NvisionVLE, EndoAngel, GI Genius). Machine learning algorithms assist in real-time polyp detection, classification, and predictive analytics, enhancing diagnostic accuracy while reducing inter-observer variability and human error. …”
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Deployment of real-time particle detection monitoring system in operating theatres for airborne contamination assessments: a methodological evaluation by Frans Stålfelt, Johan Tenghamn, Henrik Malchau, Karin Svensson Malchau

“…Future research should focus on integrating predictive algorithms and machine-learning to enhance clinical utility and drive improvements in surgical safety. …”
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Digital Self-Management Intervention Paths for Early Breast Cancer Patients: Results of a Pilot Study by Paula Poikonen-Saksela, Evangelos Karademas, Leena Vehmanen, Meri Utriainen, Haridimos Kondylakis, Konstadina Kourou, Georgios C. Manikis, Eleni Kolokotroni, Panagiotis Argyropaidas, Berta Sousa, Ruth Pat Horenczyk, Ketti Mazzocco, Johanna Mattson

“…Based on these answers and predictive algorithms for anxiety and depression, they were guided onto one or several digital intervention paths. …”
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Identification and validation of biomarkers, construction of diagnostic models, and investigation of immunological infiltration characteristics for idiopathic frozen shoulder by Han-tao Jiang, Li-ping Shen, Meng-Qi Pang, Min-jiao Wu, Jiang Li, Wei-jie Gong, Gang Jin, Rang-teng Zhu

“…The nomogram constructed based on them had a good predictive value for the occurrence of FS. Except for DCN, the other four genes were upregulated in FS samples, and the expression of SNAI1, TWIST1, and TUBB2B was also observed to be significantly upregulated in RT-qPCR. …”
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Mapping Gridded GDP Distribution of China Based on Remote Sensing Data and Machine Learning Methods by Saimiao Liu, Wenliang Liu, Yi Zhou, Shixin Wang, Futao Wang, Zhenqing Wang

“…Consequently, both methods are combined to construct the overall GDP spatialization model. (3) The accuracy of the GDP spatialization results is evaluated based on town-level GDP statistics, with an <i>R</i>² value of 0.78, indicating its reliable predictive capability. (4) Compared with publicly available GDP datasets, our dataset exhibits consistent spatial distribution patterns and aggregation trends. …”
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Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences by Eugenia Mylona, Dimitrios I. Zaridis, Charalampos Ν. Kalantzopoulos, Nikolaos S. Tachos, Daniele Regge, Nikolaos Papanikolaou, Manolis Tsiknakis, Kostas Marias, ProCAncer-I Consortium, Dimitrios I. Fotiadis

“…., four ML classifiers, namely SVM, RF, LASSO, and boosted generalized linear model (GLM), and three sets of radiomics features, derived from T2w images, ADC maps, and their combination, were used to develop predictive models of csPCa. Their performance was evaluated in a nested cross-validation and externally, using seven performance metrics. …”
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IoT-driven smart agricultural technology for real-time soil and crop optimization by Hammad Shahab, Muhammad Naeem, Muhammad Iqbal, Muhammad Aqeel, Syed Sajid Ullah

“…By integrating advanced IoT technologies, cloud computing, predictive algorithms, and a smart soil sensor, this system revolutionizes agriculture by enabling real-time monitoring of critical factors influencing rice crops metabolism. …”
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Syn-MolOpt: a synthesis planning-driven molecular optimization method using data-derived functional reaction templates by Xiaodan Yin, Xiaorui Wang, Zhenxing Wu, Qin Li, Yu Kang, Yafeng Deng, Pei Luo, Huanxiang Liu, Guqin Shi, Zheng Wang, Xiaojun Yao, Chang-Yu Hsieh, Tingjun Hou

“…To address this issue, we first developed a general pipeline capable of constructing functional reaction template library specific to any property where a predictive model can be built. Based on these functional templates, we introduced Syn-MolOpt, a synthesis planning-oriented molecular optimization method. …”
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Elucidating the dynamic tumor microenvironment through deep transcriptomic analysis and therapeutic implication of MRE11 expression patterns in hepatocellular carcinoma by Ruiqiu Chen, Chaohui Xiao, Zizheng Wang, Guineng Zeng, Shaoming Song, Gong Zhang, Lin Zhu, Penghui Yang, Rong Liu

“…Finally, our prognostic model exhibited strong predictive accuracy across multiple datasets. Conclusion High MRE11 expression is crucial in regulating the immune microenvironment in HCC, fostering immune evasion and driving tumor progression. …”
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CELIAC DISEASE SCREENING IN A LARGE DOWN SYNDROME COHORT: COMPARISON OF DIAGNOSTIC YIELD OF DIFFERENT SEROLOGICAL SCREENING TESTS by Dilek Uludağ Alkaya, Seçil Sözen, Birol Öztürk, Nuray Kepil, Tülay Erkan, Hüseyin Tufan Kutlu, Beyhan Tüysüz

“…Overall, 2.3% of patients were diagnosed with CD. The positive predictive value of AGA-IgA and AGA-IgG was low (13.6% and 7.2%, respectively) compared to EMA (69.6%) and tTG-IgA (66.7%). …”
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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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Exploring T-cell metabolism in tuberculosis: development of a diagnostic model using metabolic genes by Shoupeng Ding, Chunxiao Huang, Jinghua Gao, Chun Bi, Yuyang Zhou, Zihan Cai

“…Clinical validation substantiated the differential expression patterns of T-cell-related metabolic differentially expressed genes (TCM–DEGs; p < 0.05), while the nomogram predictive model achieved exceptional discriminative capacity (C-index = 0.944), demonstrating superior clinical applicability through decision curve analysis. …”
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Construction and demolition waste material library based on vision systems dataZenodo by Maria Teresa Calcagni, Giovanni Salerno, Gloria Cosoli, Giuseppe Pandarese, Gian Marco Revel

“…The data obtained were standardised and organised in a format compatible with the main statistical analysis and machine learning tools to facilitate their integration into predictive models and decision-making processes. The article describes in detail the library structure, data collection protocols, and practical applications in the fields of waste management and sustainable construction. …”
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C2 pars interarticularis length on the side of high-riding vertebral artery with implications for pars screw insertion by Tomasz Klepinowski, Miszela Kałachurska, Michał Chylewski, Natalia Żyłka, Dominik Taterra, Kajetan Łątka, Bartłomiej Pala, Wojciech Poncyljusz, Leszek Sagan

“…Sample size was estimated with pwr package and C2PIL was measured. Cut-off value and predictive statistics of C2PIL for HRVA were computed with cutpointr package. …”
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AUTOMATIC CONSTRUCTION METHODS OF ABSTRACTS ON THE BASIS OF THE TEXTS FREQUENCY ANALYSIS by F. I. Tretyakov, L. V. Serebryanaya

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A Novel Fault Diagnosis of Induction Motor by Using Various Soft Computation Techniques: BESO-RDFA by Kapu V. Sri Ram Prasad, K. Dhananjay Rao, Guruvulu Naidu Ponnada, Umit Cali, Taha Selim Ustun

“…The established hybrid forecast scheme signifies the combined execution of Bald-Eagle- Search-Optimization (BESO) and Random-Decision-Forest-Algorithm (RDFA), called as BESO-RDFA prediction scheme. …”
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Graph convolution network for fraud detection in bitcoin transactions by Ahmad Asiri, K. Somasundaram

“…We have run different algorithms for predicting illicit transactions like Logistic Regression, Long Short Term Memory, Support Vector Machine, Random Forest, and a variation of Graph Neural Networks, which is called Graph Convolution Network (GCN). …”
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Detection of Transformer Faults: AI-Supported Machine Learning Application in Sweep Frequency Response Analysis by Hakan Çuhadaroğlu, Yılmaz Uyaroğlu

“…Among them, the Gradient Boost Classifier showed the best performance in classifying faults. This algorithm accurately predicted the health status of transformers by learning from large datasets. …”
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Coastal Urban Ecological Security Pattern Identification Integrating Land Subsidence Factors: A Deep Learning-Based Case Study of Zhuhai City by Yuan Shaoxiong, Gong Qinghua, Ye Yuyao, Wang Jun, Hao Yinlei, Zhang Yaze, Liu Bowen

“…Results showed that the MLP model achieved an average prediction accuracy of 84.5% with an F1-score of 0.844, demonstrating the feasibility of deep learning approaches in ESP construction. …”
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