Gas adsorption meets geometric deep learning: points, set and match by Antonios P. Sarikas, Konstantinos Gkagkas, George E. Froudakis

“…Recently, machine learning (ML) pipelines have been established as the go-to method for large scale screening by means of predictive models. These are typically built in a descriptor-based manner, meaning that the structure must be first coarse-grained into a 1D fingerprint before it is fed to the ML algorithm. …”
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Oxidative stress gene expression in ulcerative colitis: implications for colon cancer biomarker discovery by Ting Yan, Ting Su, Miaomiao Zhu, Qiyuan Qing, Binjie Huang, Jun Liu, Tenghui Ma

“…Subsequently, we performed Gene Ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) analyses, followed by immune infiltration analysis using the single-sample gene-set enrichment analysis (ssGSEA) and CIBERSORT algorithms. By constructing a multivariate Cox prognostic model using Kaplan–Meier curves and least absolute shrinkage and selection operator (LASSO) regression analysis, we assessed the model’s prognostic capability. …”
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Integrated analysis of single-cell RNA-seq and spatial transcriptomics to identify the lactylation-related protein TUBB2A as a potential biomarker for glioblastoma in cancer cells... by Yifan Xu, Chonghui Zhang, Jinpeng Wu, Pin Guo, Nan Jiang, Chao Wang, Yugong Feng

“…Notably, the expression levels of these three hub genes and the lactylation level of TUBB2A in GBM tissues were significantly higher compared to those in normal tissues.ConclusionsWe propose and validate a IQR lactylation screening method that provides potential insights for GBM therapy and an effective framework for developing gene screening models applicable to other diseases and pathogenic mechanisms.…”
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Bioinformatics analysis of potential targets influencing the prognosis of OSCC by ZOU Xian, SONG Tao

“…The Timer website was used to analyze the relationship between hub genes and immune cell infiltration and immune checkpoints. Based on Lasso-Cox algorithm, a prognostic risk model of related genes was constructed. …”
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Exploring biomarkers and molecular mechanisms of Type 2 diabetes mellitus promotes colorectal cancer progression based on transcriptomics by Simin Luo, Yuhong Zhu, Zhanli Guo, Chuan Zheng, Xi Fu, Fengming You, Xueke Li

“…The diagnostic performance was assessed by supplementing external datasets to draw ROC curves on the diagnostic model. The diagnostic model was further screened for key genes by prognostic analysis. …”
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Technical Code Analysis of Geomagnetic Flaw Detection of Suppression Rigging Defect Signal Based on Convolutional Neural Network by Gang Zhao, Changyu Han, Zhongxiang Yu, Zhipan Li, Guoao Yu, Hongmei Zhang, Dadong Zhao, Zhengyi Jiang

“…The single-stage object detection algorithm YOLOv5 (You Only Look Once) based on convolutional neural network model calculation is used, the scale detection layer and positioning loss function of the YOLOv5 algorithm are improved and optimized, and the improved YOLOv5 algorithm is used for experiments. …”
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Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for thyroid cancer by Peng Zhang, Meizhong Qin, Fen Li, Kunpeng Hu, He Huang, Cuicui Li

“…Abstract Background Thyroid cancer (THCA) exhibits high molecular heterogeneity, posing challenges for precise prognosis and personalized therapy. Most existing models rely on single-omics data and limited algorithms, reducing robustness and clinical value. …”
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The value of habitat analysis based on 18F-PSMA-1007 PET/CT images for prostate cancer risk grading by Yang Wang, Hongyue Zhao, Zhehao Lyu, Linhan Zhang, Wei Han, Zeyu Wang, Jiafu Wang, Xinyue Zhang, Shibo Guo, Peng Fu, Changjiu Zhao

“…Independent risk factors were screened and a combined model was constructed to predict GS grade by univariate logistic regression followed by multivariate logistic regression of habitat (1–4) and clinical factors (SUVmax, tPSA, fPSA/tPSA, age). …”
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Machine Learning and Deep Learning Techniques for Prediction and Diagnosis of Leptospirosis: Systematic Literature Review by Suhila Sawesi, Arya Jadhav, Bushra Rashrash

“…MethodsUsing Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), and Prediction model Risk of Bias Assessment Tool (PROBAST) tools, we conducted a comprehensive review of studies applying ML and DL models for leptospirosis detection and prediction, examining algorithm performance, data sources, and validation approaches. …”
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3D Film Animation Image Acquisition and Feature Processing Based on the Latest Virtual Reconstruction Technology by Siwei Wu, Shan Xiao, Yihua Di, Cheng Di

“…Finally, the target 3D face is reconstructed using the feature points of the target face for model matching. The experimental results show that the algorithm reconstructs faces with high realism and accuracy, and the algorithm can reconstruct expression faces.…”
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High‐resolution mapping of cancer cell networks using co‐functional interactions by Evan A Boyle, Jonathan K Pritchard, William J Greenleaf

“…This work establishes new algorithms for probing cancer cell networks and motivates the acquisition of further CRISPR screen data across diverse genotypes and cell types to further resolve complex cellular processes.…”
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An estimation method of lightning-voltage sag severity based on adaptive association rule mining by WANG Ying, LEI Lei, HU Wenxi, XIAO Xianyong

“…The key condition attributes that affect voltage sag severity are screened by attribute reduction algorithm. An association rule mining algorithm based on parameter adaption is proposed, which overcomes the problem that the results of traditional association rules mining methods are affected by non-uniform data. …”
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Predicting radiation pneumonitis in lung cancer using machine learning and multimodal features: a systematic review and meta-analysis of diagnostic accuracy by Zhi Chen, GuangMing Yi, XinYan Li, Bo Yi, XiaoHui Bao, Yin Zhang, XiaoYue Zhang, ZhenZhou Yang, Zhengjun Guo

“…By selecting multiple machine learning algorithm frameworks and competing for the best combination model based on research goals, the reliability and accuracy of the radiation pneumonitis prediction model can be greatly improved. …”
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Increasing comprehensiveness and reducing workload in a systematic review of complex interventions using automated machine learning by Olalekan A Uthman, Rachel Court, Jodie Enderby, Lena Al-Khudairy, Chidozie Nduka, Hema Mistry, GJ Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…Background As part of our ongoing systematic review of complex interventions for the primary prevention of cardiovascular diseases, we have developed and evaluated automated machine-learning classifiers for title and abstract screening. The aim was to develop a high-performing algorithm comparable to human screening. …”
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Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study by Weiyue Chen, Guihan Lin, Ye Feng, Yongjun Chen, Yanjun Li, Jianbin Li, Weibo Mao, Yang Jing, Chunli Kong, Yumin Hu, Minjiang Chen, Shuiwei Xia, Chenying Lu, Jianfei Tu, Jiansong Ji

“…The GPTV3 radiomics model using a support vector machine algorithm achieved the best predictive performance, with the highest average AUC of 0.811 in the validation sets. …”
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Role of arachidonic acid metabolism in osteosarcoma prognosis by integrating WGCNA and bioinformatics analysis by Yaling Wang, Peichun HSU, Haiyan Hu, Feng Lin, Xiaokang Wei

“…An AA metabolism predictive model of the five AAMRGs were established by Cox regression and the LASSO algorithm. …”
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Global trends in machine learning applications for single-cell transcriptomics research by Xinyu Liu, Zhen Zhang, Chao Tan, Yinquan Ai, Hao Liu, Yuan Li, Jin Yang, Yongyan Song

“…Research hotspots concentrated on random forest (RF) and deep learning models, showing transition from algorithm development to clinical applications (e.g., tumor immune microenvironment analysis). …”
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The CD163 + tissue-infiltrating macrophages regulate ferroptosis in thyroid-associated ophthalmopathy orbital fibroblasts via the TGF-β/Smad2/3 signaling pathway by Xuemei Li, Siyi Wang, Hanwen Cao, Simin Xu, Chao Xiong, Jinhai Yu, Yunxiu Chen, Zhangjun Ren, Min Li, Ying Hu, Puying Gan, Qihua Xu, Yaohua Wang, Hongfei Liao

“…Finally, potential clinical drugs targeting CD163 + macrophages with high ferroptosis activity in TAO were predicted using the Random Walk with Restart (RWR) algorithm combined with the DGIdb database. Results We first utilized TAO-related datasets from the GEO database, combined with the FerrDb ferroptosis database, to identify changes in iron metabolism genes during TAO progression through differential expression analysis, screening 7 key ferroptosis-related proteins. …”
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Enhancing glaucoma diagnosis: Generative adversarial networks in synthesized imagery and classification with pretrained MobileNetV2 by I. Govindharaj, D. Santhakumar, K. Pugazharasi, S. Ravichandran, R. Vijaya Prabhu, J. Raja

“…This approach does not only contribute to glaucoma screening but also can also reveal the benefits of the GANs and transfer learning in medical imaging. • A GAN approach to generate high-quality fundus image datasets in an attempt to minimize dataset differences. • Implemented improved Enhanced Level Set Algorithm for Optic Cup segmentation. • Built on top of the pretrained MobileNetV2 to obtain better results of glaucoma classification.…”
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Time-Distributed Vision Transformer Stacked With Transformer for Heart Failure Detection Based on Echocardiography Video by Mgs M. Luthfi Ramadhan, Adyatma W. A. Nugraha Yudha, Muhammad Febrian Rachmadi, Kevin Moses Hanky Jr Tandayu, Lies Dina Liastuti, Wisnu Jatmiko

“…This study proposed a novel deep learning model consisting of a time-distributed vision transformer stacked with a transformer. …”
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