Depletion of core microbiome forms the shared background against diverging dysbiosis patterns in Crohn’s disease and intestinal tuberculosis: insights from an integrated multi-coho... by Aditya Bajaj, Manasvini Markandey, Amit Samal, Sourav Goswami, Sudheer K. Vuyyuru, Srikant Mohta, Bhaskar Kante, Peeyush Kumar, Govind Makharia, Saurabh Kedia, Tarini Shankar Ghosh, Vineet Ahuja

“…Methods Disease-associated gut microbial modules were identified using statistical machine learning and co-abundance network analysis in controls, CD and ITB patients recruited as part of this study. …”
Get full text

Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation by Yoonsung Kwon, Asta Blazyte, Yeonsu Jeon, Yeo Jin Kim, Kyungwhan An, Sungwon Jeon, Hyojung Ryu, Dong-Hyun Shin, Jihye Ahn, Hyojin Um, Younghui Kang, Hyebin Bak, Byoung-Chul Kim, Semin Lee, Hyung-Tae Jung, Eun-Seok Shin, Jong Bhak

“…Abstract Background The changes in DNA methylation patterns may reflect both physical and mental well-being, the latter being a relatively unexplored avenue in terms of clinical utility for psychiatric disorders. …”
Get full text

Association of dietary quality, biological aging, progression and mortality of cardiovascular-kidney-metabolic syndrome: insights from mediation and machine learning approaches by Junfeng Ge, Lin Zhu, Sijie Jiang, Wenyan Li, Rongzhan Lin, Jun Wu, Fengying Dong, Jin Deng, Yi Lu

“…Conclusion DII, a marker of pro-inflammatory dietary patterns, was significantly linked to CKM syndrome progression and mortality, partly by influencing biological aging. …”
Get full text

Immune-related adverse events of neoadjuvant immunotherapy in patients with perioperative cancer: a machine-learning-driven, decade-long informatics investigation by Yuan Meng, Rong Hu, Song-Bin Guo, Deng-Yao Liu, Zhen-Zhong Zhou, Hai-Long Li, Wei-Juan Huang, Xiao-Peng Tian

“…However, many unknowns remain in this field. Hence, through the machine learning (ML)-driven informatics analysis, this study aimed to profile the global decade-long scientific landscape of AEs of NAI and further reveal its critical issues and directions that deserve deeper exploration. …”
Get full text

Machine learning analysis of kinematic movement features during functional tasks to discriminate chronic neck pain patients from asymptomatic controls by Filippo Moggioli, Óscar Rodríguez-López, Elena Bocos-Corredor, Constantino Antonio García, Sonia Liébana, Tomás Pérez-Fernández, Cristina Sánchez, Susan Armijo-Olivo, José Angel Santos-Paz, Aitor Martín-Pintado-Zugasti

“…Movement was characterized using fifteen features, incorporated into machine learning models to assess how movement patterns relate to patient condition. …”
Get full text

Effects of sandblasting and acid etching on the surface properties of additively manufactured and machined titanium and their consequences for osteoblast adhesion under different s... by Osman Akbas, Amit Gaikwad, Amit Gaikwad, Leif Reck, Nina Ehlert, Nina Ehlert, Anne Jahn, Jörg Hermsdorf, Andreas Winkel, Andreas Winkel, Meike Stiesch, Meike Stiesch, Andreas Greuling

“…For this purpose, the parameters cell adhesion, morphology, and membrane integrity were investigated using confocal laser microscopy and LDH assay.ResultsInitial high roughness of AM titanium surfaces was decreased by sandblasting, while initial smooth machined surfaces (MM) increased in roughness. Acid etching introduced characteristic irregular patterns on the surface with only marginal consequences for the resulting overall roughness. …”
Get full text

Association between metal mixture in urine and abnormal blood pressure and mediated effect of oxidative stress based on BKMR and Machine learning method by Junjie Chen, Hao Zeng, Zhanglei Pan, Miao Li, Qingfeng Zhou, Kaichen Chen, Yulan Hao, Xiangke Cao, Lei Zhang, Qian Wang

“…These participants were followed up in 4 seasons for physical examination and blood and urine samples collection between December 2017 and October 2018. we employed linear mixed effect model (LME), Bayesian kernel-machine regression (BKMR) and Machine learning (ML) to evaluate complex exposure-response relationships between multi-metal mixtures and blood pressure outcomes. …”
Get full text

Dynamic monitoring of fine-grained ecological vulnerability in dryland urban agglomeration integrating novel remote sensing index and explainable machine learning by Chunqiang Li, Shanchuan Guo, Qin Huang, Haowei Mu, Bo Yuan, Zilong Xia, Hong Fang, Wei Zhang, Pengfei Tang, Peijun Du

“…However, persistent technological gaps in large-scale, fine-grained and long-term monitoring hinder a comprehensive understanding of vulnerability patterns in these fragile regions. To address this, a novel Dryland Ecological Vulnerability Index (DEVI) is proposed by integrating six key indicators and combining remote sensing and machine learning to simplify the complex vulnerability scoping diagram (VSD). …”
Get full text

Microbiome and fragmentation pattern of blood cell-free DNA and fecal metagenome enhance colorectal cancer micro-dysbiosis and diagnosis analysis: a proof-of-concept study by Zhongkun Zhou, Yunhao Ma, Dekui Zhang, Rui Ji, Yiqing Wang, Jianfang Zhao, Chi Ma, Hongmei Zhu, Haofei Shen, Xinrong Jiang, Yuqing Niu, Juan Lu, Baizhuo Zhang, Lixue Tu, Hua Zhang, Xin Ma, Peng Chen

“…Machine learning models based on these differential characteristics achieve high diagnostic accuracy, especially when they are integrated with fragmentation patterns. …”
Get full text

Dual pathways of forest landscape dynamics in China: Integrating ecological land evolution Index (ELEI) and machine learning to decipher fragmentation and restoration by Yifan Xie, Sui Liu, Bowen Pang, Nannan Wang, Jiwei Xu, He Huang, Yao Cui, Jiaming Yang, Yaolin Liu, Yanfang Liu

Get full text

Machine Learning Creates a Simple Endoscopic Classification System that Improves Dysplasia Detection in Barrett’s Oesophagus amongst Non-expert Endoscopists by Vinay Sehgal, Avi Rosenfeld, David G. Graham, Gideon Lipman, Raf Bisschops, Krish Ragunath, Manuel Rodriguez-Justo, Marco Novelli, Matthew R. Banks, Rehan J. Haidry, Laurence B. Lovat

“…At present, there are no guidelines on who should perform surveillance endoscopy in BE. Machine learning (ML) is a branch of artificial intelligence (AI) that generates simple rules, known as decision trees (DTs). …”
Get full text

WetCH₄: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 by Q. Ying, Q. Ying, B. Poulter, J. D. Watts, K. A. Arndt, A.-M. Virkkala, L. Bruhwiler, Y. Oh, Y. Oh, B. M. Rogers, S. M. Natali, H. Sullivan, A. Armstrong, A. Armstrong, E. J. Ward, E. J. Ward, L. D. Schiferl, C. D. Elder, C. D. Elder, O. Peltola, A. Bartsch, A. R. Desai, E. Euskirchen, M. Göckede, B. Lehner, M. B. Nilsson, M. Peichl, O. Sonnentag, E.-S. Tuittila, T. Sachs, T. Sachs, A. Kalhori, M. Ueyama, Z. Zhang, Z. Zhang

“…However, the magnitude and spatial patterns of high-latitude CH<span class="inline-formula">₄</span> emissions remain relatively uncertain. …”
Get full text

Evaluating climatic variability's impact on milk yield across climate zones: A machine learning-based comparative study of Switzerland and Thailand by Boonyarat Phadermrod, Varunya Attasena

“…Across all scenarios, previous milk yield is a stronger predictor than short-term meteorological variables, suggesting that recent production trends already reflect key weather effects. This pattern also holds within homogeneous sub-datasets. …”
Get full text

Double-diffusive magnetoconvection in a tilted porous parallelogrammic domain with discrete heated-cooled segments: Leveraging machine learning and CFD approach by P. Ravindra, M. Sankar, Suresh Rasappan, Wardah Abdullah Al Majrafi, Pugalarasu Rajan, S. Sanal Kumar

Get full text

Geospatial analysis of short sleep duration and cognitive disability in US adults: a multi-state study using machine learning techniques by Tue T. Te, Alex A. T. Bui, Constance H. Fung, Mary Regina Boland

Get full text

ATP6AP1 drives pyroptosis-mediated immune evasion in hepatocellular carcinoma: a machine learning-guided therapeutic target by Lei Tang, Xiyue Wang, Zhengzheng Xia, Jiayu Yan, Shanshan Lin

“…Methods We integrated large-scale datasets from TCGA and GEO databases to identify core modules by weighted gene co-expression network analysis (WGCNA), while mutation profiling and survival analysis verified clinical relevance. Multiple machine learning techniques, including GBM (gradient boosting machine), XGBoost (extreme gradient boosting machine), SVM (support vector machine), LASSO (least absolute shrinkage and selection operator) and random forest, as well as functional analysis, were used to systematically investigate the role of ATP6AP1 in HCC. …”
Get full text

Impact of computing platforms on classifier performance in heart disease prediction by Beenish Ayesha Akram, Muhammad Irfan, Amna Zafar, Sidra Khan, Rubina Shaheen

“…Prediction and classification, a supervised learning technique in machine learning, addresses various challenges related to finding useful patterns present in data. …”
Get full text

DESIGN OF STUDENT SUCCESS PREDICTION APPLICATION IN ONLINE LEARNING USING FUZZY-KNN by Selly Anastassia Amellia Kharis, Gatot Fatwanto Hertono, Endang Wahyuningrum, Yumiati Yumiati, Sam Rizky Irawan, T Ahmad Danial, Dimas Septian Saputra

“…Data mining techniques as known as Educational Data Mining (EDM) collect, process, report and used to find the unseen patterns in the student dataset. EDM uses machine learning techniques to dig out useful data from multiple levels of meaningful hierarchy. …”
Get full text

Comparative analysis of force sensitive resistor circuitry for use in force myography systems for hand gesture recognition by Giancarlo K. Sagastume, Peyton R. Young, Marcus A. Battraw, Justin G. Kwong, Jonathon S. Schofield

Get full text

Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis by ShinYe Kim, Winson Fu Zun Yang, Zishan Jiwani, Emily Hamm, Shreya Singh

“…We also evaluated the predictive power of these linguistic features using machine learning and identified key thematic structures through semantic network analysis. …”
Get full text