Deep learning with data transformation improves cancer risk prediction in oral precancerous conditions by John Adeoye, Yuxiong Su

“…Background: Oral cancer is the most common head and neck malignancy and may develop from oral leukoplakia (OL) and oral lichenoid disease (OLD). Machine learning classifiers using structured (tabular) data have been employed to predict malignant transformation in OL and OLD. …”
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PCOS and Inositols – Advances and Lessons We are Learning. A Narrative Review by Lentini G, Querqui A, Monti N, Bizzarri M

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Metformin improves HPRT1-targeted purine metabolism and repairs NR4A1-mediated autophagic flux by modulating FoxO1 nucleocytoplasmic shuttling to treat postmenopausal osteoporosis by Keda Yang, Xiaochuan Wang, Chi Zhang, Dian Liu, Lin Tao

“…Through energy metabolism-targeted metabolomics, we revealed that purine metabolism disorder is the main mechanism involved in inducing oxidative damage in bone tissue, which was verified via the use of machine-learning data from human databases. Xanthine and xanthine oxidase were used to treat osteoblasts to construct a purine metabolism disorder model. …”
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Aggregation-Based Ensemble Classifier Versus Neural Networks Models for Recognizing Phishing Attacks by Wojciech Galka, Jan G. Bazan, Urszula Bentkowska, Kamil Szwed, Marcin Mrukowicz, Pawel Drygas, Lech Zareba, Marcin Szpyrka, Piotr Suszalski, Sebastian Obara

“…Unlike other phishing datasets, this data provides dates which is important for the incremental learning approach. The proposed approach of ensemble learning models, based on aggregation functions, is compared to the well-known neural network models which may be treated as state of the art models in recognizing phishing attacks. …”
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Finite‐temperature properties of NbO2 from a deep‐learning interatomic potential by Xinhang Li, Yongqiang Wang, Tianyu Jiao, Zhaoxin Liu, Chuanle Yang, Ri He, Liang Si

“…Treating the DFT results of the low‐ and intermediate‐temperature phases of NbO2 as training data in the deep‐learning model, we successfully constructed an interatomic potential capable of accurately reproducing the phase transitions from low‐temperature (pressure) to high‐temperature (pressure) regimes. …”
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Clinical efficacy and learning curve of percutaneous endoscopic cervical discectomy for symptomatic cervical spondylotic radiculopathy by Xiao Sun, Lijuan Zhan, Zhongxin Tang, Mingkui Shen, Haijun Ma, Jun Tan

“…Abstract Background This study aimed to evaluate the clinical efficacy and learning curve of percutaneous endoscopic cervical discectomy (PECD) in treating patients with symptomatic cervical spondylotic radiculopathy (CSR). …”
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History Repeats, We Forget: Short Memories When It Comes to K-12 Distance Learning by Michael K. Barbour, Charles B. Hodges

“…The article concludes that realizing the potential of K-12 online learning requires addressing fundamental issues in research, practice, and policy while learning from past experiences rather than treating each implementation as novel.…”
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Classification of Retinoblastoma Eye Disease on Digital Fundus Images Using Geometric Features and Machine Learning by Arif Setiawan

“…The classification of normal and retinoblastoma-affected retinas is a preliminary step in treating retinoblastoma tumors. Therefore, this study aimed to propose a new method for classifying normal and retinoblastoma-affected retinas using geometric feature extraction and machine learning. …”
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DPFunc: accurately predicting protein function via deep learning with domain-guided structure information by Wenkang Wang, Yunyan Shuai, Min Zeng, Wei Fan, Min Li

“…In this study, we propose a deep learning-based solution, named DPFunc, for accurate protein function prediction with domain-guided structure information. …”
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Explainable AI in early autism detection: a literature review of interpretable machine learning approaches by Renuka Agrawal, Rucha Agrawal

“…The article goes into additional detail in a specialized assessment of AI and XAI applications in research on ASD, showing how XAI can offer vital insights into identifying, diagnosing, and treating autism.…”
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Structured hashing with deep learning for modality, organ, and disease content sensitive medical image retrieval by Asim Manna, Dipayan Dewan, Debdoot Sheet

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Enhanced prediction of hemolytic activity in antimicrobial peptides using deep learning-based sequence analysis by Ibrahim Abdelbaky, Mohamed Elhakeem, Hilal Tayara, Elsayed Badr, Mustafa Abdul Salam

“…To address this issue, we propose a deep learning model based on convolutional neural networks (CNNs) for predicting the hemolytic activity of AMPs. …”
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Learnings from the 2024 Utah Cardiac Recovery Symposium: A Roadmap for the Field of Myocardial Recovery by Muthu Kumar Krishnamoorthi, Konstantinos Sideris, Arvind Bhimaraj, Stavros G. Drakos

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Enhanced Heart Disease Diagnosis Using Machine Learning Algorithms: A Comparison of Feature Selection by Hirmayanti, Ema Utami

“…Therefore, with the increasingly rapid development of technology, especially in the health sector, it is hoped that it can help medical personnel in treating patients suffering from various diseases, especially heart disease. …”
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Accurate prediction of virulence factors using pre-train protein language model and ensemble learning by Guanghui Li, Jian Zhou, Jiawei Luo, Cheng Liang

“…Results To address this limitation, we developed a protein language model and ensemble learning approach for VF identification (PLMVF). Specifically, we extracted features from protein sequences using ESM-2 and their three-dimensional (3D) structures using ESMFold. …”
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Tackling difficult changes in digital education through playful learning and gamification: the case of generative AI by Maria O'Hara, Cecilia Lo

“…We have been using approaches drawn from gamification and playful learning theories to create spaces for colleagues to explore generative AI (GenAI) in a way which is naturally fun. …”
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Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma Variants by Sakhr Alshwayyat, Haya Kamal, Owais Ghammaz, Tala Abdulsalam Alshwayyat, Mustafa Alshwayyat, Ramez M. Odat, Hamdah Hanifa, Wafa Asha, Nesreen A. Saadeh

“…Aims This study used machine learning (ML) to evaluate treatment effectiveness and develop prognostic models. …”
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How machine learning on real world clinical data improves adverse event recording for endoscopy by Stefan Wittlinger, Isabella C. Wiest, Mahboubeh Jannesari Ladani, Jakob Nikolas Kather, Matthias P. Ebert, Fabian Siegel, Sebastian Belle

“…Abstract Endoscopic interventions are essential for diagnosing and treating gastrointestinal conditions. Accurate and comprehensive documentation is crucial for enhancing patient safety and optimizing clinical outcomes; however, adverse events remain underreported. …”
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Hybrid deep learning approach for rock tunnel deformation prediction based on spatio-temporal patterns by Junfeng Sun, Yong Fang, Hu Luo, Zhigang Yao, Long Xiang, Jianfeng Wang, Yubo Wang, Yifan Jiang

“…Integrating GAT-LSTM with transfer learning technology, remains stable performance when extended to other tunnels with limited data.…”
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Research on the influence of Siberian fir polyprenols on learning and memory of mice with an experimental model of Alzheimer's disease by Nikolay I. Suslov, Yulia S. Fedorova, Maxim L. Korobov

“…Attempts to create a pharmacotherapeutic agent that provides an effective result in treating this pathology have so far been unsuccessful. …”
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