Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma by Qingyan Kou, Zhichao Wu, Wenbin Zhao, Zhenyuan Liu, Shengxian Qiao, Qiang Mu, Xu Zhang

“…Machine learning analysis was performed on the genes identified through Mendelian randomization (MR) and survival association analysis, using 101 algorithms to construct a robust prognostic model. A novel riskScore model was developed by integrating genetic, clinical, and immune cell infiltration data. …”
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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

“…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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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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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

“…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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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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The photometry and kinematics studies of NGC 2509 derived from Gaia DR3 by Nasser M. Ahmed, A. L. Tadross

“…We employed the pyUPMASK Python package and HDBSCAN algorithms to identify the cluster member stars. The current analysis introduces a new method that connects the membership probability of stars in the cluster (using the pyUPMASK tool) with the number of stars predicted by the King model at different distances from the center of the cluster. …”
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EcoTaskSched: a hybrid machine learning approach for energy-efficient task scheduling in IoT-based fog-cloud environments by Asfandyar Khan, Faizan Ullah, Dilawar Shah, Muhammad Haris Khan, Shujaat Ali, Muhammad Tahir

“…We will also assess its applicability by integrating other ML models, which could provide enhanced insights for optimizing scheduling algorithms across diverse fog-cloud settings.…”
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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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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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Radiomic Analysis and Liquid Biopsy in Preoperative CT of NSCLC: An Explorative Experience by Maria Paola Belfiore, Mario Sansone, Giovanni Ciani, Vittorio Patanè, Carlotta Genco, Roberta Grassi, Giovanni Savarese, Marco Montella, Riccardo Monti, Salvatore Cappabianca, Alfonso Reginelli

“…Radiomic features were extracted from CT images, and circulating tumor DNA (ctDNA) was sequenced to identify genetic mutations. Machine learning algorithms were employed to assess the association between radiomic features and gene mutations. …”
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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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Construction and demolition waste material library based on vision systems dataZenodo by Maria Teresa Calcagni, Giovanni Salerno, Gloria Cosoli, Giuseppe Pandarese, Gian Marco Revel

“…In addition, the benefits of this resource for the scientific and industrial community are discussed, including the possibility of using the data to develop/fine-tune artificial intelligence (AI) algorithms capable of optimising sorting and recycling processes by recognition and discrimination among different types of CDW material using the aforementioned sensors. …”
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Identification of Diagnostic Biomarkers and Therapeutic Targets in Sepsis-Associated ARDS via Combining Bioinformatics with Machine Learning Analysis by Liu T, Gao L, Li X

“…Three machine learning algorithms were applied to refine the intersected genes. …”
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Teens and opioids postsurgery (TOPS): protocol for a prospective observational study describing associations between sleep deficiency and opioid use following outpatient surgery in... by Tonya Palermo, Jennifer A Rabbitts, Cornelius B Groenewald, Rebecca L Flack, Sophia L Kreider

“…We will apply modern machine learning algorithms to develop and validate models predicting adolescent prescription opioid misuse at 24 months from surgery.Ethics and dissemination This study was approved by Advarra’s Center for Institutional Review Board Intelligence (CIRBI) (Protocol 00072049), which serves as the single IRB of record for this multisite study.…”
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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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CYLD as a key regulator of myocardial infarction-to-heart failure transition revealed by multi-omics integration by Jingya Xu, Jingya Xu, Zhonghua Dong, Zhaodong Li, Xuan Wang, Xuan Wang

“…Single-cell transcriptomic analysis demonstrated that CYLD exhibited the strongest correlation with the transition from MI to HF; using random forest modelling, we validated its predictive value in this context.DiscussionIn conclusion, our study identified CYLD as a critical regulator of the transition from MI to HF. …”
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Advancing sustainability: The impact of emerging technologies in agriculture by Ashoka Gamage, Ruchira Gangahagedara, Shyamantha Subasinghe, Jeewan Gamage, Chamini Guruge, Sera Senaratne, Thevin Randika, Chamila Rathnayake, Zammil Hameed, Terrence Madhujith, Othmane Merah

“…The integration of data analytics and machine learning algorithms is transforming supply chain management and enhancing the capabilities of predictive analytics in the context of crop diseases. …”
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Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer by Rui Wang, Rui Wang, Guan-Hua Qin, Guan-Hua Qin, Yifei Jiang, Fu-Xiang Chen, Fu-Xiang Chen, Zi-Han Wang, Zi-Han Wang, Lin-Ling Ju, Lin Chen, Da Fu, En-Yu Liu, Su-Qing Zhang, Wei-Hua Cai

“…A 7-gene mCAF-associated risk model was constructed using advanced machine learning algorithms, and the biological significance of PHLDA1 was validated through co-culture experiments and pan-cancer analyses.ResultsOur multiomics analysis revealed that the novel 7-gene model (comprising USP36, KLF5, MT2A, KDM6B, PHLDA1, REL, and DDIT4) accurately predicts patient survival, immunotherapy response, and TME status. …”
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The Association of Aortic Stenosis Severity and Symptom Status With Morbidity and Mortality by Matthew D. Solomon, MD, PhD, Alan S. Go, MD, Thomas Leong, MPH, Elisha Garcia, BS, Kathy Le, MPH, Femi Philip, MD, Edward McNulty, MD, Jacob Mishell, MD, Andrew N. Rassi, MD, David C. Lange, MD, Catherine Lee, PhD, Anthony DeMaria, MD, Rick Nishimura, MD, Andrew P. Ambrosy, MD

“…Methods: In this retrospective cohort study from a large, integrated health care system serving >4.5 M individuals, we applied validated natural language processing algorithms to echocardiogram reports to identify physician-assessed AS severity and potential AS-related symptoms (eg, chest pain, syncope, dyspnea, worsening heart failure) via diagnosis codes and natural language processing-applied physician notes. …”
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