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

“…Therefore, based on the remote sensing data of land use and nighttime light, this study developed two methods: the factor averaging method (FAM) and grid averaging method (GAM), and used Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms to jointly construct the spatial model of GDP, so as to produce China’s 1 km gridded GDP in 2020. …”
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Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases by Zhiyuan Ning, Xuanfei Jiang, Huan Huang, Honggang Ma, Ji Luo, Xiangyan Yang, Bing Zhang, Ying Liu

“…The optimal features predicting NT-proBNP levels were identified using univariate and step-forward multivariate models. …”
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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

“…Feature selection method impacts radiomics models’ performance more than ML algorithms. Best feature selection methods: RFE, LASSO, RF, and Boruta. …”
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Using artificial intelligence and promoter-level transcriptome analysis to identify a biomarker as a possible prognostic predictor of cardiac complications in male patients with Fa... by Hiroshi Kobayashi, Norio Nakata, Sayoko Izuka, Kenichi Hongo, Masako Nishikawa

“…Cardiac complications, such as cardiomyopathy, cardiac muscle fibrosis, and severe arrhythmia, are the most common mortality causes in patients with Fabry disease. To predict cardiac complications of Fabry disease, we extracted RNA from the venous blood of patients for cap analysis of gene expression (CAGE), performed likelihood ratio tests for each RNA expression dataset obtained from individuals with and without cardiac complications, and analyzed the correlation between cardiac functional factors observed using magnetic resonance imaging data extracted using artificial intelligence algorithms and RNA expression. …”
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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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Development of a PPP1R14B-associated immune prognostic model for hepatocellular carcinoma by Ligang Zhao, Xiaoling Jin, Qiushi Yu, Yigang He, Facai Yang, Weihu Ma, Zhengqing Lei, Jiahua Zhou

“…The study constructed a PPP1R14B-linked immune prediction model, demonstrating acceptable prognostic capability for HCC patients. …”
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Deep learning in microbiome analysis: a comprehensive review of neural network models by Piotr Przymus, Krzysztof Rykaczewski, Adrián Martín-Segura, Jaak Truu, Enrique Carrillo De Santa Pau, Mikhail Kolev, Mikhail Kolev, Irina Naskinova, Aleksandra Gruca, Alexia Sampri, Alexia Sampri, Marcus Frohme, Alina Nechyporenko, Alina Nechyporenko

“…These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. …”
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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

“…Although many deep-learning-based molecular optimization algorithms have been proposed and may perform well on benchmarks, they usually do not pay sufficient attention to the synthesizability of molecules, resulting in optimized compounds difficult to be synthesized. …”
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Using Features Extracted From Upper Limb Reaching Tasks to Detect Parkinson’s Disease by Means of Machine Learning Models by Giuseppe Cesarelli, Leandro Donisi, Francesco Amato, Maria Romano, Mario Cesarelli, Giovanni D'Addio, Alfonso M. Ponsiglione, Carlo Ricciardi

“…The investigation carried out in our work has proved the predictive power of the features, extracted from the reaching tasks involving the upper limbs, to distinguish HCs and PD patients.…”
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Comprehensive profiling of chemokine and NETosis-associated genes in sarcopenia: construction of a machine learning-based diagnostic nomogram by Yingwei Wang, Le Wang, Yan Zhang, Minghui Wang, Huaying Zhao, Cheng Huang, Huaiyang Cai, Shuangyang Mo

“…Two machine learning algorithms and univariate analysis were integrated to screen signature genes, which were subsequently used to construct diagnostic nomogram models for sarcopenia. …”
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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

“…We also screened for differentially expressed genes and constructed a robust HCC prognosis model using 101 machine-learning algorithms. Results Our results demonstrated that high MRE11 expression is strongly associated with poor prognosis in HCC. …”
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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

“…This study aimed to estimate the prevalence of CD in DS patients and compare the diagnostic performance of the screening algorithms. Material and Method: A cohort of 1117 DS patients were included. …”
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Unveiling the pathogenic mechanisms of polyethylene terephthalate-microplastic-driven osteoarthritis and rheumatoid arthritis: PTGS2 signaling hub-oriented toxicity profiling by Jingkai Di, Shuang Wang, Lujia Liu, Keying Rong, Zijian Guo, Yingda Qin, Feida Wang, Chuan Xiang

“…Western blot (WB) and quantitative real-time polymerase chain reaction (qRT-PCR) experiments were conducted to verify the predicted results. The study identified 59 potential PET targets related to OA and 53 targets related to RA. …”
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Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis by Xingren Liu, Junmei Song, Shujin Guo, Yi Liao, Jun Zou, Liqing Yang, Caiyu Jiang

“…Genes from this module were used to construct diagnostic and prognostic models, which demonstrated strong predictive performance across multiple validation datasets. …”
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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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Critical review of patient outcome study in head and neck cancer radiotherapy by Jingyuan Chen, Yunze Yang, Chenbin Liu, Hongying Feng, Jason M. Holmes, Lian Zhang, Steven J. Frank, Charles B. Simone, II, Daniel J. Ma, Samir H. Patel, Wei Liu

“…This review critically evaluates the evolution of data-driven approaches in predicting patient outcomes in head and neck cancer patients treated with radiation therapy. …”
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Visible, near-infrared, and shortwave-infrared spectra as an input variable for digital mapping of soil organic carbon by Vahid Khosravi, Asa Gholizadeh, Radka Kodešová, Prince Chapman Agyeman, Mohammadmehdi Saberioon, Luboš Borůvka

“…Thirty rasters were then created using interpolation of the selected spectra and served as the input variables – with and without EPCs – to test and compare the developed models and SOC predictive maps with each other and with those retrieved from the third approach: iii) kriging using OK of the measured and ML-predicted SOC. …”
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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

“…We identified T-cell-associated metabolic differentially expressed genes (TCM–DEGs) through integrated differential expression analysis and machine learning algorithms (XGBoost, SVM–RFE, and Boruta). These TCM–DEGs were then used to construct a diagnostic model and evaluate its clinical applicability. …”
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Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach by Sakhr Alshwayyat, Mahmoud Bashar Abu Al Hawa, Mustafa Alshwayyat, Tala Abdulsalam Alshwayyat, Siya sawan, Ghaith Heilat, Hanan M. Hammouri, Sara Mheid, Batool Al Shweiat, Hamdah Hanifa

“…To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five Machine Learning (ML) algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of ML models. …”
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SECONDGRAM: Self-conditioned diffusion with gradient manipulation for longitudinal MRI imputation by Brandon Theodorou, Anant Dadu, Mike Nalls, Faraz Faghri, Jimeng Sun

“…These models are computer algorithms that simulate how information changes. SECONDGRAM addresses data scarcity by generating realistic follow-up MRI imaging features, thereby enriching limited datasets. …”
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