Using fishery-related data, scientific expertise, and machine learning to improve marine habitat mapping in northeastern Mediterranean waters by Loukas Katikas, Sofia Reizopoulou, Paraskevi Drakopoulou, Vassiliki Vassilopoulou

“…Two machine-learning algorithms, i.e., random forest classifier (RFC) and gradient boosting classifier, were trained on the entire national-scale dataset and subsequently applied to assess their performance in predicting habitat types in the Saronikos Gulf (regional scale) using various environmental factors as predictors. …”
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Identification of neoantigen epitopes in cervical cancer by multi-omics analysis by Jing Yuan, Na Xu, Xueqi Gong, Jihui Ai, Kezhen Li, Yingyan Han

“…Focusing on MHC class I epitopes recognized by CD8 + T cells, we predicted potential neoantigen peptides using the NetMHCpan-4.0 and NetCTL-1.2 algorithms. …”
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The good and the bad: using C reactive protein to distinguish bacterial from non-bacterial infection among febrile patients in low-resource settings by Camille Escadafal, Sabine Dittrich, Sandra Incardona, B Leticia Fernandez-Carballo

“…CRP testing may be best used as part of a panel of diagnostic tests and algorithms. Further studies in low-resource settings, particularly with regard to impact on antibiotic prescribing and cost-effectiveness of CRP testing, are warranted.…”
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False-positive tolerant model misconduct mitigation in distributed federated learning on electronic health record data across clinical institutions by Maxim Edelson, Anh Pham, Tsung-Ting Kuo

“…Abstract As collaborative Machine Learning on cross-institutional, fully distributed networks become an important tool in predictive health modeling, its inherent security risks must be addressed. …”
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SurVIndel2: improving copy number variant calling from next-generation sequencing using hidden split reads by Ramesh Rajaby, Wing-Kin Sung

“…We also show that SurVIndel2 is able to complement small indels predicted by Google DeepVariant, and the two software used in tandem produce a remarkably complete catalogue of variants in an individual. …”
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Machine Learning Applications in Gray, Blue, and Green Hydrogen Production: A Comprehensive Review by Xuejia Du, Shihui Gao, Gang Yang

“…Among these, green hydrogen—particularly via water electrolysis and biomass gasification—received the most attention, reflecting its central role in decarbonization strategies. ML algorithms such as artificial neural networks (ANNs), random forest (RF), and gradient boosting regression (GBR) have been widely applied to predict hydrogen yield, optimize operational conditions, reduce emissions, and improve process efficiency. …”
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Integrating Machine Learning and IoT for Effective Plant Disease Management by Bhoi Manjulata, Dubey Ahilya

“…This data is collected and transmitted to a central node for analysis by these sensors. ML algorithms at the advanced level such as convolutional neural networks (CNNs) and decision trees are used to find patterns in the data which signal the presence of possible diseases in the plant. …”
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Human-AI collaboration is not very collaborative yet: a taxonomy of interaction patterns in AI-assisted decision making from a systematic review by Catalina Gomez, Sue Min Cho, Shichang Ke, Chien-Ming Huang, Mathias Unberath

“…Leveraging Artificial Intelligence (AI) in decision support systems has disproportionately focused on technological advancements, often overlooking the alignment between algorithmic outputs and human expectations. A human-centered perspective attempts to alleviate this concern by designing AI solutions for seamless integration with existing processes. …”
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Use of Data Mining for Intelligent Evaluation of Imputation Methods by David Red, Carlos R. Primorac

“…Data imputation techniques allow the estimation of MV using different algorithms, by means of which important data can be imputed for a particular instance. …”
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Smart Farming: AI and IoT-Based Solutions for Real-Time Agriculture Monitoring by Kadao Anjali Krushna, Shivaji Ghorpade Bipin

“…These data streams are analyzed by advanced AI algorithms that generate actionable insights. The basic intention is to minimize costs of irrigation, fertilizer and pest control and to maximize crop yield through minimizing wastage of resources. …”
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Explainable data mining model for hyperinsulinemia diagnostics by Nevena Rankovic, Dragica Rankovic, Mirjana Ivanovic, Igor Lukic

“…Additionally, we have incorporated the post-hoc explanatory method SHAP (SHapley Additive exPlanations) alongside algorithms such as Random Forest, XGBoost, and LightGBM to provide deeper insights into our model, identifying the most contributory features for the development of hyperinsulinemia. …”
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Data-driven decoding of quantum error correcting codes using graph neural networks by Moritz Lange, Pontus Havström, Basudha Srivastava, Isak Bengtsson, Valdemar Bergentall, Karl Hammar, Olivia Heuts, Evert van Nieuwenburg, Mats Granath

“…Accurate, maximum likelihood, decoders are computationally very expensive whereas decoders based on more efficient algorithms give sub-optimal performance. In addition, the accuracy will depend on the quality of models and estimates of error rates for idling qubits, gates, measurements, and resets, and will typically assume symmetric error channels. …”
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Features of mammalian microRNA promoters emerge from polymerase II chromatin immunoprecipitation data. by David L Corcoran, Kusum V Pandit, Ben Gordon, Arindam Bhattacharjee, Naftali Kaminski, Panayiotis V Benos

“…<h4>Background</h4>MicroRNAs (miRNAs) are short, non-coding RNA regulators of protein coding genes. miRNAs play a very important role in diverse biological processes and various diseases. Many algorithms are able to predict miRNA genes and their targets, but their transcription regulation is still under investigation. …”
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Preliminary analysis of acoustic detection of the Red-throated Caracara in northern Costa Rica by Roberto Vargas-Masís, Diego Quesada

“…Advances in automatic acoustic detection have transformed bird ecology, allowing researchers to analyze bird populations using pattern matching algorithms, machine learning, and random forest models. …”
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IMPROVEMENT OF THE RUSSIAN CITIES’ TRANSPORT INFRASTRUCTURE by E. A. Safronov, K. E. Safronov

“…Therefore, the developed algorithms and recommendations for reducing the volume of transport infrastructure helps to improve its performance and availability. …”
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Learning-based parallel acceleration for HaplotypeCaller by Xiangxing Lai, Minguang Xiao, Lingling Weng, Zhiguang Chen

“…This paper introduces a learning-based framework LPA (learning-based parallel acceleration), leveraging model to accurately predict the computational complexity of data. By employing adaptive data segmentation algorithms and Multi-Knapsack Problem (MKP) based task scheduling, LPA significantly alleviates computational skew. …”
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Integrative machine learning identifies robust inflammation-related diagnostic biomarkers and stratifies immune-heterogeneous subtypes in Kawasaki disease by Xia Wang, Lin Zhang

“…Differential analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (LASSO, Boruta, SVM-RFE, Random Forest) were applied to screen diagnostic biomarkers. …”
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Enhanced Brain Tumor Segmentation Using CBAM-Integrated Deep Learning and Area Quantification by Rafiqul Islam, Sazzad Hossain

“…The qualitative metrics typically analyze the overlap between predicted tumor masks and ground truth annotations, providing information on the segmentation algorithms’ accuracy and dependability. …”
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A Multi-Model Machine Learning Framework for Identifying Raloxifene as a Novel RNA Polymerase Inhibitor from FDA-Approved Drugs by Nhung Thi Hong Van, Minh Tuan Nguyen

“…We developed a multi-model machine learning framework combining five traditional algorithms (ExtraTreesClassifier, RandomForestClassifier, LGBMClassifier, BernoulliNB, and BaggingClassifier) with a CNN deep learning model to identify potential RdRP inhibitors among FDA-approved drugs. …”
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TSCAPE: time series clustering with curve analysis and projection on an Euclidean space by Jeremy Renaud, Raphaël Couturier, Christophe Guyeux, Benoit Courjal

“…The results demonstrate the method's superiority over “only distance comparison clustering techniques”, like dynamic time warping and K-Means, with future prospects aimed at predictive applications and refining the clustering process by exploring alternative, more powerful clustering algorithms.…”
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