Sensing using SERS-substrate and machine learning approaches by Bakal, Victoria Alexandrovna, Gusliakova, Olga Igorevna, Prikhozhdenko, Ekaterina Sergeevna

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Utilisation of Deep Neural Networks for Estimation of Cajal Cells in the Anal Canal Wall of Patients with Advanced Haemorrhoidal Disease Treated by LigaSure Surgery by Inese Fišere, Edgars Edelmers, Šimons Svirskis, Valērija Groma

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‘Fully Diagnosed, Fully Stabilised and Fully Treated’: Succeeding in a Claim for a Disability Support Pension in Australia for Endometriosis and Chronic Pelvic Pain by Karena Viglianti

“…In this article, I consider the AAT’s determinations and set out what applicants, their legal representatives and their treating doctors can learn from the cases on applicants for a DSP with endometriosis and chronic pelvic pain.…”
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Clinical and economic effectiveness of Schroth therapy in adolescent idiopathic scoliosis: insights from a machine learning- and active learning-based real-world study by Erdal Ayvaz, Merve Uca, Ednan Ayvaz, Zafer Yıldız

“…Recent advancements in active learning (AL) and machine learning (ML) techniques offer the potential to optimize treatment protocols by uncovering hidden predictors and enhancing model efficiency. …”
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Blood from septic patients with necrotising soft tissue infection treated with hyperbaric oxygen reveal different gene expression patterns compared to standard treatment by Julie Vinkel, Alfonso Buil, Ole Hyldegaard

“…Results We identified differences in gene expression profiles at follow-up between HBO2-treated patients and patients not treated with HBO2. …”
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Eight quick tips for biologically and medically informed machine learning. by Luca Oneto, Davide Chicco

“…This integration has give rise to informed machine learning, in contrast to studies that lack domain knowledge and treat all variables equally (uninformed machine learning). …”
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Visual Experience in Strabismus – A Concept-Based Learning for Postgraduates by Anjali Khadia, Veena Kannusamy, Fredrick Moutappa, Janani Muralikrishnan, G S Lavanya, Sivaraja Gowthaman, R. Poorani, Gururaj N. Deshpande, Rengaraj Venkatesh

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Blessing of dimensionality in spiking neural networks: the by-chance functional learning by Valeri A. Makarov, Sergey A. Lobov, Sergey A. Lobov

“…However, their training is challenging since most of the learning principles known from artificial neural networks are hardly applicable. …”
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Information Assisted Dictionary Learning for fMRI Data Analysis by Manuel Morante, Yannis Kopsinis, Sergios Theodoridis, Athanassios Protopapas

“…In this paper, the task-related fMRI problem is treated in its matrix factorization form, focusing on the Dictionary Learning (DL) approach. …”
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Deep Learning-Based Spectrum Sensing for Cognitive Radio Applications by Sara E. Abdelbaset, Hossam M. Kasem, Ashraf A. Khalaf, Amr H. Hussein, Ahmed A. Kabeel

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Brain Tumor Classification Using an Ensemble of Deep Learning Techniques by S Gopal Krishna Patro, Nikhil Govil, Surabhi Saxena, Brojo Kishore Mishra, Abu Taha Zamani, Achraf Ben Miled, Nikhat Parveen, Hashim Elshafie, Mosab Hamdan

“…The article reflects on the classification of brain tumors where several deep learning (DL) approaches are used. Both primary and secondary brain tumors reduce the patient’s quality of life, and therefore, any sign of the tumor should be treated immediately for adequate response and survival rates. …”
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An Optimized Model for Identification of Cerebral Palsy Using Deep Learning by Md Anjar Ahsan, Hassan bin Mohammed, Abhilash Maroju, Nurhafizah Moziyana Mohd Yusop, Wan Su Emi Yusnita Wan Yusof, Najjah Salwa Abd Razak, Mohd Arif Dar

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Scalable geometric learning with correlation-based functional brain networks by Kisung You, Yelim Lee, Hae-Jeong Park

“…Conventional analyses often treat pairwise interactions independently within Euclidean space, neglecting the underlying geometry of correlation structures. …”
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IDBR: Interaction-Aware Dual-Granularity Learning for Bundle Recommendation by Jinqing Wang, Yuan Cao, Fan Zhang, Feifei Kou, Kaimin Wei, Jinghui Zhang, Jinpeng Chen

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Proinflammatory Factors Mediate Paclitaxel-Induced Impairment of Learning and Memory by Zhao Li, Shuang Zhao, Hai-Lin Zhang, Peng Liu, Fei-Fei Liu, Yue-Xian Guo, Xiu-Li Wang

“…In addition, the TNF-α synthesis inhibitor thalidomide significantly attenuated the number of paclitaxel-induced TUNEL-positive neurons in the hippocampus and restored the impaired spatial learning and memory function in paclitaxel-treated rats. …”
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MLAD: A Multi-Task Learning Framework for Anomaly Detection by Kunqi Li, Zhiqin Tang, Shuming Liang, Zhidong Li, Bin Liang

“…MLAD consists of four key modules: (1) sensor clustering based on sensors’ time series, (2) representation learning with a cluster-constrained graph neural network, (3) multi-task forecasting with shared and cluster-specific learning layers, and (4) anomaly scoring. …”
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Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning by Ilya Larin, Alexander Karabelsky

“…Graph-based segmentation has a long history, e.g., the normalized cuts algorithm treated segmentation as a graph partitioning problem—but only recently have such ideas merged with deep learning in an unsupervised manner. …”
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Prediction of hepatitis-C virus using statistical learning models by Shalini Kumari, Subhajit Das, Prashant Kumar Sonker, Agni Saroj, Mukesh Kumar

“…The study includes dataset of 615 HCV patients from the UCI Machine Learning Repository for illustrative purposes and analyzed it through machine learning models such as naive Bayes (NB), random forest (RF), support vector machine (SVM), logistic regression (LR), decision trees (DT), and artificial neural network (ANN). …”
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Machine learning for predicting distant metastasis in nasopharyngeal carcinoma patients by Hong Sun, Jijie Zhu, Ling Li, Xiu Xin, Jingchao Yan, Taomin Huang

“…The aim of this study was to explore the risk factors for distant metastasis in NPC patients using machine learning (ML) methods.MethodsWe collected data from NPC patients who were treated at the Eye Ear Nose Throat Hospital of Fudan University between September 2017 and June 2024. …”
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Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC by Maliazurina B. Saad, Qasem Al-Tashi, Lingzhi Hong, Vivek Verma, Wentao Li, Daniel Boiarsky, Shenduo Li, Milena Petranovic, Carol C. Wu, Brett W. Carter, Girish S. Shroff, Tina Cascone, Xiuning Le, Yasir Y. Elamin, Mehmet Altan, Simon Heeke, Ajay Sheshadri, Joe Y. Chang, Percy P. Lee, Zhongxing Liao, Don L. Gibbons, Ara A. Vaporciyan, J. Jack Lee, Ignacio I. Wistuba, Cara Haymaker, Seyedali Mirjalili, David Jaffray, Justin F. Gainor, Yanyan Lou, Alessandro Di Federico, Federica Pecci, Mark Awad, Biagio Ricciuti, John V. Heymach, Natalie I. Vokes, Jianjun Zhang, Jia Wu

“…We develop a machine learning model using clinicogenomic data from four cohorts (MD Anderson n = 750; Mayo Clinic n = 80; Dana-Farber n = 1077; Stand Up To Cancer n = 393) to predict individual benefit from adding chemotherapy. …”
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