Identification of m5C RNA modification-related gene signature for predicting prognosis and immune microenvironment-related characteristics of heart failure by Zirui Liu, Rui Feng, Ying Xu, Meili Liu, Haocheng Wang, Yu Lu, Weiqi Wang, Jikai Wang, Cao Zou

“…Consensus clustering algorithms identified two m5C-related HF subtypes. Single-sample gene-set enrichment analysis (ssGSEA) and CIBERSORT deconvolution algorithm analyzed the IME in HF. …”
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Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds by Mengdie Fan, Chenhui Sang, Hua Li, Yue Wei, Bin Zhang, Yang Xing, Jing Zhang, Jie Yin, Wei An, Bing Shao

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Pathway activation model for personalized prediction of drug synergy by Quang Thinh Trac, Yue Huang, Tom Erkers, Päivi Östling, Anna Bohlin, Albin Osterroos, Mattias Vesterlund, Rozbeh Jafari, Ioannis Siavelis, Helena Backvall, Santeri Kiviluoto, Lukas Orre, Mattias Rantalainen, Janne Lehtiö, Soren Lehmann, Olli Kallioniemi, Yudi Pawitan, Trung Nghia Vu

“…We trained and validated DIPx in the AstraZeneca-Sanger (AZS) DREAM Challenge human cell-line dataset using two separate test sets: Test Set 1 comprised the combinations already present in the training set, while Test Set 2 contained combinations absent from the training set, thus indicating the model’s ability to handle novel combinations. …”
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Challenges, Difficulties, and Delayed Diagnosis of Multiple Myeloma by Tugba Zorlu, Merve Apaydin Kayer, Nazik Okumus, Turgay Ulaş, Mehmet Sinan Dal, Fevzi Altuntas

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The role of vascularity and the fibrovascular interface in interstitial lung diseases by Jana Dietrich, Alice Kang, Birger Tielemans, Stijn E. Verleden, Hassan Khalil, Florian Länger, Philipp Bruners, Steven J. Mentzer, Tobias Welte, Michael Dreher, Danny D. Jonigk, Maximilian Ackermann

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Fecal metabolites as early-phase biomarkers and prediction panel for ischemic stroke by Ke Xu, Zhe Ren, Shuang Zhao, Yi Ren, Jiaolin Wang, Wentao Wu, Zicheng Hu, Fei He, Dianji Tu, Qi Zhong, Jianjun Chen, Peng Xie

“…Specifically, six differential metabolites (Ganoderic acid theta, Fructose-lysine, Pentaethylene glycol, 2-Chlorooctadecanoic acid, PA(2:0/PGF1alpha), and 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol) were identified as potential independent stroke-associated metabolites. A prediction panel consisting of these six metabolites could yield an area under the curve of 0.989 in training set and 0.973 in testing set. …”
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Research on Intelligent Detection and Segmentation of Rock Joints Based on Deep Learning by Lei Peng, Haibo Wang, Chun Zhou, Feng Hu, Xiaoyang Tian, Zhu Hongtai

“…Additionally, to tackle the challenge of low detection accuracy in existing image processing methods, particularly for complex tunnel joint surfaces in dark environments, the paper introduces a path aggregation network (PANet) to enhance the fusion capability of feature information in Mask R-CNN, thereby improving the accuracy of the intelligent detection method. The algorithm was trained on a dataset of 800 tunnel face images, and the research findings demonstrate that it can quickly detect the position of joints on tunnel face images and assign masks to the joint pixel regions to achieve joint segmentation. …”
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RECOMMENDED TACTICS FOR THE EVALUATION OF POTENTIALLY MALIGNANT DISORDERS IN THE ORAL CAVITY by S.V. Kolomiiets, K.O. Udaltsova, V.I. Shynkevych

“…Those involve whole series of redirections of the patient among dentists themselves, before referring to the really necessary specialist, and it represents a negative item in the organization. In contrast to the algorithm provided by the Ministry of Healthcare of Ukraine, doing an immediate biopsy in patients with a suspicious oral lesion – or referring a patient to a specialist who can do that – remains the only most important recommendation made by the American Dental Association (ADA) in their updated clinical practice guidelines for the evaluation of potentially malignant disorders in the oral cavity. …”
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Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women by Peng Wang, Qiang Yin, Kangzhi Ding, Huaichang Zhong, Qundi Jia, Zhasang Xiao, Hai Xiong

“…First, we performed feature selection to identify factors associated with osteoporosis. Next, the eligible participants were randomly divided into a training set and a test set in a ratio of 8:2. …”
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Inflammation-Driven Prognosis in Advanced Heart Failure: A Machine Learning-Based Risk Prediction Model for One-Year Mortality by Zhou M, Du X

“…Data were split into training and validation sets. Seven ML algorithms were applied to build and evaluate models. …”
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Classification of Real-World Objects Using Supervised ML-Assisted Polarimetry: Cost/Benefit Analysis by Rui M. S. Pereira, Filipe Oliveira, Nazar Romanyshyn, Irene Estevez, Joel Borges, Stephane Clain, Mikhail I. Vasilevskiy

“…To this end, we look for an algorithm using less input parameters without great loss of the quality of classification. …”
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Micro-Doppler Frequency Extraction and Scatterer Classification for a Smooth-Surfaced Cone-Shaped Precession Target Under Narrowband Radar by Dan Xu, Siyuan Zhao, Kaiming Li, Jiang Qian, Mengdao Xing, Maria Sabrina Greco, Fulvio Gini

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Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning by Mi Liu, Xingxing Gao, Hongfa Wang, Yiping Zhang, Xiaojun Li, Renlai Zhu, Yunru Sheng

“…The machine learning algorithm screened three PCD-related genes, NLRC4, TXN and S100A9, as potential biomarkers for sepsis. …”
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SNet: A novel convolutional neural network architecture for advanced endoscopic image classification of gastrointestinal disorders by Samra Siddiqui, Junaid A. Khan, Tallha Akram, Meshal Alharbi, Jaehyuk Cha, Dina A. AlHammadi

“…To enable the exhaustive evaluation of proposed framework across different datasets, the model has undergone training on a very complex HyperKvasir dataset, and later tested on Kvasir v1 and v2 datasets. …”
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Developing the new diagnostic model by integrating bioinformatics and machine learning for osteoarthritis by Jian Du, Tian Zhou, Wei Zhang, Wei Peng

“…Finally, immune cell infiltration analysis was performed using CIBERSORT algorithm to explore the correlation between feature genes and immune cells. …”
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Screening risk factors for the occurrence of wedge effects in intramedullary nail fixation for intertrochanteric fractures in older people via machine learning and constructing a p... by Zhe Xu, Qiuhan Chen, Zhi Zhou, Jianbo Sun, Guang Tian, Chen Liu, Guangzhi Hou, Ruguo Zhang

“…The variables were screened via 3 machine learning methods, including least absolute shrinkage and selection operator (LASSO) regression, the Boruta algorithm, and recursive feature elimination (RFE). …”
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Studying Alzheimer’s disease through an integrative serum metabolomic and lipoproteomic approach by Alessia Vignoli, Giovanni Bellomo, Federico Paolini Paoletti, Claudio Luchinat, Leonardo Tenori, Lucilla Parnetti

“…Results In the training set, our model classified AD-dem and MCI with an accuracy of 81.7%. …”
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Machine learning derived retinal pigment score from ophthalmic imaging shows ethnicity is not biology by Anand E. Rajesh, Abraham Olvera-Barrios, Alasdair N. Warwick, Yue Wu, Kelsey V. Stuart, Mahantesh I. Biradar, Chuin Ying Ung, Anthony P. Khawaja, Robert Luben, Paul J. Foster, Charles R. Cleland, William U. Makupa, Alastair K. Denniston, Matthew J. Burton, Andrew Bastawrous, Pearse A. Keane, Mark A. Chia, Angus W. Turner, Cecilia S. Lee, Adnan Tufail, Aaron Y. Lee, Catherine Egan, UK Biobank Eye and Vision Consortium

“…RPS may serve as a useful metric to quantify the diversity of the training, validation, and testing datasets used in the development of AI algorithms to ensure adequate inclusion and explainability of the model performance, critical in evaluating all currently deployed AI models. …”
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Human expert grading versus automated quantification of fluid volumes in nAMD, DME and BRVO by Felix Goldbach, Bianca S. Gerendas, Oliver Leingang, Thomas Alten, Alexandros Bampoulidis, Jonas Brugger, Hrvoje Bogunovic, Amir Sadeghipour, Ursula Schmidt-Erfurth

“…OCT scans were analyzed using a validated algorithm (RetInSight, Vienna, Austria) to compute IRF and SRF volumes. …”
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Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson’s disease patients by Yandong lian, Yibin Xu, Linlin Hu, Yuguo Wei, Zhaoge Wang

“…Additionally, multi-factor logistic regression analysis identified clinical predictors associated with PD-RBD, and these clinical features were integrated with the radiomics signatures to develop predictive models using various machine learning algorithms. …”
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