The influence of pH and temperature on benthic chlorophyll-a: Insights from SHAP-XGBoost and random forest models by Sangar Khan, Noël P.D. Juvigny-Khenafou, Tatenda Dalu, Paul J. Milham, Yasir Hamid, Kamel Mohamed Eltohamy, Habib Ullah, Bahman Jabbarian Amiri, Hao Chen, Naicheng Wu

“…There is little information on machine learning predictive models of benthic chl–a and input parameters in lotic ecosystems, and to fill this gap, we predict benthic chl–a levels in China's Thousand Islands Lake (TIL) watershed using machine learning algorithms. Water samples for nutrient and metal analysis were collected across 147 sites in the TIL catchment. …”
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A multi-cohort validated OXPHOS signature predicts survival and immune profiles in grade II/III glioma patients by Jun Mou, Min Zhang, Fumin Qin, Yajie Cui, Keyou Xu, Baoye Pang, Xinyue Li, Wanyi Tan, Aiqi Yang, Yaxin Liu, Lingjun Shen, Yanting Liu, Kai Xu

“…The immune cell composition and tumor microenvironment (TME) characteristics were assessed using ESTIMATE, MCPcounter, and CIBERSORT algorithms. Based on prognostic DEGs, we constructed a four-gene prognostic signature (MAOB, IGFBP2, SERPINA1, and LGR6).ResultsThe C2 molecular subtype was associated with poorer prognosis, higher immune scores, and enrichment in tumor-promoting pathways. …”
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Lithological mapping and spectroscopic studies of carbonatite and clinopyroxenite from Hogenakkal carbonatite complex, India by Saraah Imran, Sourav Bhattacharjee, Ajanta Goswami, Aniket Chakrabarty

“…Petrography, Raman spectroscopy of minerals, and spectroradiometric measurements of rock samples support the interpretations derived from Principal Component Analysis (PCA), Spectral Angle Mapper (SAM), Support Vector Machine (SVM), Decision Tree, and Random Forest algorithms, thereby aiding in the identification of lithological variations and potential clinopyroxenite occurrences. …”
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LAF: Enhancing person re-identification via Latent-Assisted Feature Fusion by Minglang Li, Zhiyong Tao, Sen Lin, Kaihao Feng

“…To address this limitation, we propose a novel Latent-Assisted Fusion (LAF) framework that systematically mines discriminative cues from non-salient areas, which are critical for distinguishing challenging samples. Our approach introduces three key innovations: Lock-Drop, Outlook-Attention, and ML-Fusion. …”
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The Automatable Activity–Based Approach to Complexity Unit Scoring as a task-specific model approach to monetizing outcomes of pathology artificial intelligence solutions by Stavros Pantelakos, Martha Nifora, Georgios Agrogiannis

“…A hundred and thirty-two prostate core biopsy samples were encoded for workload using the Automatable Activity–Based Approach to Complexity Unit Scoring. …”
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Determination of high-confidence germline genetic variants in next-generation sequencing through machine learning models: an approach to reduce the burden of orthogonal confirmatio... by Muqing Yan, Qiandong Zeng, Zhenxi Zhang, Patricia Okamoto, Stanley Letovsky, Angela Kenyon, Natalia Leach, Jennifer Reiner

“…Improvements to early NGS methods and bioinformatics algorithms have dramatically improved variant calling accuracy, particularly for single nucleotide variants (SNVs), thus calling into question the necessity of confirmatory testing for all variant types. …”
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Optimizing Tumor Detection in Brain MRI with One-Class SVM and Convolutional Neural Network-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

“…However, medical imaging datasets frequently exhibit class imbalance, posing significant challenges for traditional classification algorithms that rely on balanced data distributions. …”
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BAMHealthCloud: A biometric authentication and data management system for healthcare data in cloud by Kashish A. Shakil, Farhana J. Zareen, Mansaf Alam, Suraiya Jabin

“…Performance comparison of the system with other state-of-art-algorithms shows that the proposed system preforms better than the existing systems in literature. …”
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MYB Proto-Oncogene Like 2 identified as a biomarker for uterine corpus endometrial carcinoma: evidence from bioinformatics and clinical validation by Jiaoyun Lu, Furong Luo

“…This study aimed to investigate the multifaceted roles of MYB Proto-Oncogene Like 2 (MYBL2) in uterine corpus endometrial carcinoma (UCEC).MethodsWe employed multiple bioinformatics algorithms (GEPIA, TCGA, TIMER2.0) to analyze MYBL2 expression across different cancer types and in UCEC specifically. …”
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Predicting Diabetic Retinopathy and Nephropathy Complications Using Machine Learning Techniques by D. R. Manjunath, J. J. Lohith, S. Selva Kumar, Abhijit Das

“…Through feature engineering, sampling strategies and hyperparameter tuning the models performed well on all the datasets. …”
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Genomic selection optimization in blueberry: Data‐driven methods for marker and training population design by Paul Adunola, Luis Felipe V. Ferrão, Juliana Benevenuto, Camila F. Azevedo, Patricio R. Munoz

“…Our contribution in this study is threefold: (i) for the genotyping resource allocation, the use of genetic data‐driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long‐term implication, we carried out a simulation study and emphasized that data‐driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. …”
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Differentiating coeliac disease from irritable bowel syndrome by urinary volatile organic compound analysis--a pilot study. by Ramesh P Arasaradnam, Eric Westenbrink, Michael J McFarlane, Ruth Harbord, Samantha Chambers, Nicola O'Connell, Catherine Bailey, Chuka U Nwokolo, Karna D Bardhan, Richard Savage, James A Covington

“…For assay, the specimens were heated to 40 ± 0.1°C and the headspace analysed by Field Asymmetric Ion Mobility Spectrometry (FAIMS). Machine learning algorithms were used for statistical evaluation. Samples were also analysed using Gas chromatography and mass spectroscopy (GC-MS). …”
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Identification of novel gut microbiota-related biomarkers in cerebral hemorrhagic stroke by Fengli Ye, Huili Li, Hongying Li, Xiue Mu

“…Hub genes were screened using LASSO, RandomForest, and SVM-RFE algorithms. Validation was conducted in plasma samples from ICH patients (n=20) and controls (n < 20) by qRT-PCR, and in a collagenase-induced ICH mouse model. …”
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HARDWARE AND SOFTWARE COMPLEX FOR FUNCTIONAL STATE MONITORING OF MOTHER AND FETUS by I. V. Tolmachyov, K. S. Brazovsky, A. S. Tsverova, V. S. Ripenko

“…Preprocessing is carried out in microcontroller by receiving signals from the analog-to-digital converter on the increased sampling rate, digital filtering, decimation.With the help of the developed complex two-stage study was conducted. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…A total of 39,000 data samples were matched with the initial zero-hour fields from RMAPS-NOW, with 80% (31,200 samples) used for training and 20% (7800 samples) for testing. …”
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Quantitative image analysis of the extracellular matrix of esophageal squamous cell carcinoma and high grade dysplasia via two-photon microscopy by Kausalya Neelavara Makkithaya, Wei-Chung Chen, Chun-Chieh Wu, Ming-Chi Chen, Wei-Hsun Wang, Jackson Rodrigues, Ming-Tsang Wu, Nirmal Mazumder, I-Chen Wu, Guan-Yu Zhuo

“…Unlike previous studies on cancer diagnosis using two-photon microscopy, quantitative analysis or machine learning (ML) algorithms need to be used to determine the subtle structural changes in images and the structural features that are statistically meaningful in cancer development. …”
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Feasibility of machine learning–based modeling and prediction to assess osteosarcoma outcomes by Qinfei Zhao, Weiquan Hu, Yu Xia, Shengyun Dai, Xiangsheng Wu, Jing Chen, Xiaoying Yuan, Tianyu Zhong, Xuxiang Xi, Qi Wang

“…Using 66 combinations of 10 machine learning (ML) algorithms, we developed a machine learning-derived prognostic signature (MLDPS) optimized by the average C-index across TARGET, GSE21257, and merged cohorts. …”
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Evaluation of Four Rapid Tests for Detection of Hepatitis B Surface Antigen in Ivory Coast by Bamory Dembele, Roseline Affi-Aboli, Mathieu Kabran, Daouda Sevede, Vanessa Goha, Aimé Cézaire Adiko, Rodrigue Kouamé, Emile Allah-Kouadio, Andre Inwoley

“…The diagnostic performance (sensitivity and specificity) was calculated in comparison to the reference sequential algorithms of two EIA tests (Dia.Pro HBsAg® one version ULTRA and Monolisa™ HBsAg ULTRA). …”
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Detecting Emerging DGA Malware in Federated Environments via Variational Autoencoder-Based Clustering and Resource-Aware Client Selection by Ma Viet Duc, Pham Minh Dang, Tran Thu Phuong, Truong Duc Truong, Vu Hai, Nguyen Huu Thanh

“…Domain Generation Algorithms (DGAs) remain a persistent technique used by modern malware to establish stealthy command-and-control (C&C) channels, thereby evading traditional blacklist-based defenses. …”
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Non-Destructive Monitoring of External Quality of Date Palm Fruit (<i>Phoenix dactylifera</i> L.) During Frozen Storage Using Digital Camera and Flatbed Scanner by Younes Noutfia, Ewa Ropelewska, Zbigniew Jóźwiak, Krzysztof Rutkowski

“…Then, extracted features were used as inputs for pre-established algorithms–groups within WEKA 3.9 software to classify frozen date fruit samples after 0, 2, 4, and 6 months of storage. …”
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