Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning by Jakub Horvath, Pavel Jedlicka, Marie Kratka, Zdenek Kubat, Eduard Kejnovsky, Matej Lexa

“…Our work shows the applicability of machine learning models to regulatory sequence analysis and classification, and demonstrates the important role of the identified motifs in LTR detection.…”
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Trajectory of breastfeeding among Chinese women and risk prediction models based on machine learning: a cohort study by Yi Liu, Jie Xiang, Ping Yan, Yuanqiong Liu, Peng Chen, Yujia Song, Jianhua Ren

“…Exclusively breastfeeding(EBF) rates were recorded at 91%, 64%,72% and 58% for the same intervals. Among the five machine learning methods employed, Random Forest (RF) demonstrated superior accuracy in predicting breastfeeding patterns, with classification accuracies of 0.629 and an area under the receiver operating characteristic curve (AUC) of 0.8122 at 42 days, 0.925 and an AUC of 0.9800 at 3 months, and 0.836 and an AUC of 0.9463 at 6 months postpartum, respectively. …”
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Global soil moisture mapping at 5 km by combining GNSS reflectometry and machine learning in view of HydroGNSS by Emanuele Santi, Davide Comite, Laura Dente, Leila Guerriero, Nazzareno Pierdicca, Maria Paola Clarizia, Nicolas Floury

“…Moreover, the SM maps at 0.05° revealed some finer details and small-scale patterns that are not shown by the original SMAP SM data at 36 km. …”
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Machine learning based insights into cardiomyopathy and heart failure research: a bibliometric analysis from 2005 to 2024 by Muhammad Junaid Akram, Muhammad Junaid Akram, Asad Nawaz, Asad Nawaz, Yuan Yuxing, Yuan Yuxing, Jinpeng Zhang, Jinpeng Zhang, Huang Haixin, Huang Haixin, Lingjuan Liu, Lingjuan Liu, Xu Qian, Jie Tian, Jie Tian

“…The study utilized the R- package bibliometrix-biblioshiny, VOSviewer, Scimago Graphica and CiteSpace to analyze the publications on cardiomyopathy, heart failure, machine learning, and artificial intelligence. Key metrics examined included top institutions, countries, journals, keywords, co-authorship networks, and keyword co-occurrence patterns. …”
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A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles by Yanping Hu, Bo Wang, Chao Shi, Pengfei Ren, Chengjuan Zhang, Zhizhong Wang, Jiuzhou Zhao, Jiawen Zheng, Tingjie Wang, Bing Wei, He Zhang, Rentao Yu, Yihang Shen, Jie Ma, Yongjun Guo

“…Patients from the three clusters had varied pathologies, MMR statuses and TIIC distribution patterns (p < .05). Kaplan–Meier’s analysis showed that the prognosis of C3 was inferior compared to C1 and C2 (p = .0025). …”
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Real-Time Defect Detection for Fast-Moving Fabrics on Circular Knitting Machine Under Various Illumination Conditions by Yan-Qin Ni, Pei-Kai Huang, Ching-Han Yang, Chin-Chun Chang, Wei-Jen Wang, Deron Liang

“…In industrial production, automated inspection methods for circular knitting machines often encounter several challenges. First, the rapid movement of fabrics on these machines makes it difficult for existing fabric defect detection methods to effectively capture and process the motion. …”
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Development of a Diagnostic Model for Focal Segmental Glomerulosclerosis: Integrating Machine Learning on Activated Pathways and Clinical Validation by Ge Y, Liu X, Shu J, Jiang X, Wu Y

“…Additionally, immune cell infiltration patterns, particularly those involving natural killer (NK) cells, were explored, revealing the complex interplay between genetics and the immune response in FSGS patients. …”
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Development and validation of machine learning-based diagnostic models using blood transcriptomics for early childhood diabetes prediction by Xin Huang, Xin Huang, Di Ouyang, Weiming Xie, Huawei Zhuang, Siyu Gao, Pan Liu, Lizhong Guo

“…Model validation was conducted using quantitative polymerase chain reaction (qPCR) in an independent cohort of six children (three healthy, three diabetic).ResultsTranscriptomic analysis revealed significant differential expression patterns between pre-diabetic and control groups. Four model combinations demonstrated superior predictive performance: Lasso+K-Nearest Neighbors, Elastic Net + K-Nearest Neighbors, Elastic Net + Random Forest, and Support Vector Machine+K-Nearest Neighbors. …”
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From lab to field with machine learning – Bridging the gap for movement analysis in real-world environments: A commentary by Carlo Dindorf, Fabian Horst, Djordje Slijepčević, Bernhard Dumphart, Jonas Dully, Matthias Zeppelzauer, Brian Horsak, Michael Fröhlich

“…J., & Delp, S. L. (2018). Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities. …”
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Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study by Juan Chen, Xiong Chen, Kai Fu, Lan Zhou, Shichao Long, Mengsi Li, Linhui Zhong, Aerzuguli Abudulimu, Wenguang Liu, Deng Pan, Ganmian Dai, Yigang Pei, Wenzheng Li

“…However, the immunological pattern of a high peritumoral TLS (pTLS) density and its clinical potential in hepatocellular carcinoma (HCC) remain poor. …”
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Rotating Machinery Structural Faults Feature Enhancement and Diagnosis Based on Multi-Sensor Information Fusion by Baozhu Jia, Guanlong Liang, Zhende Huang, Xuewei Song, Zhiqiang Liao

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Research on feature extraction and intelligent diagnosis method of reciprocating compressor bearing clearance fault by Feng Yan

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Recognition of Acoustic Signals of Loaded Synchronous Motor Using FFT, MSAF-5 and LSVM by Adam GLOWACZ

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Ukrainian Folk Ornaments in Modern Knitting by Liudmyla Melnyk, Olena Kyzymchuk, Liudmyla Zubkova

“…However, the use of a knitting machine allows creating a pattern during the item production. …”
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Decoding event-related potentials: single-dose energy dietary supplement acts on earlier brain processes than we thought by Karina J. Maciejewska

“…IntroductionThis paper describes an experimental work using machine learning (ML) as a “decoding for interpretation” to understand the brain’s physiology better.MethodsMultivariate pattern analysis (MVPA) was used to decode the patterns of event-related potentials (ERPs, brain responses to stimuli) in a visual oddball task. …”
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Empirical studies on the impact of filter‐based ranking feature selection on security vulnerability prediction by Xiang Chen, Zhidan Yuan, Zhanqi Cui, Dun Zhang, Xiaolin Ju

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A systematic review of multi-mode analytics for enhanced plant stress evaluation by Abdolrahim Zandi, Seyedali Hosseinirad, Hossein Kashani Zadeh, Kouhyar Tavakolian, Byoung-Kwan Cho, Fartash Vasefi, Moon S. Kim, Pantea Tavakolian, Pantea Tavakolian

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A new multistep Si{1 0 0} machining process for aspheric cavities: the role of high-index surfaces by Wilfrido Calleja-Arriaga

“…Regarding microlens fabrication, we note the lack of thorough studies on this type of concave structure, particularly when another pattern is machined over the aspheric surface. By adding lithography and etching steps to this well-known process, the aspheric morphology can be improved. …”
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A scoping review and bibliometric analysis (ScoRBA) of machine learning in genetic data analysis: unveiling the transformative potential by Zakaria et al.

“…This study uses scoping review and bibliometric analysis; ScoRBA, to comprehensively highlight the recurrent themes linked to machine learning (ML) applications in genetic data analytics. …”

Grassland biome fragmentation analysis using sentinal-2 images and support vector machine learning model in South Africa by Andisani Netsianda, Paidamwoyo Mhangara, Eskinder Gidey

“…The inconsistency in the pattern or trend of the grassland class is likely attributable to the expansion of the other classes. …”
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