Identifying and Diagnosing Lytic Cell Death Genes in Atherosclerosis Using Machine Learning and Bioinformatics by Zhang G, Ma R, Jin H, Zhang Q, Li W, Ding Y

“…Keywords: atherosclerosis, lytic cell death, lytic cell death-related genes, machine learning, bioinformatics, cytochrome B-245β chain, CYBB…”
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Sensitivity to Instruction Strategies in Motor Learning Is Predicted by Anterior–Posterior TMS Motor Thresholds by Michael L. Perrier, Kylee R. Graham, Jessica E. Vander Vaart, W. Richard Staines, Sean K. Meehan

“…<b>Background:</b> The impact of exogenous explicit knowledge on early motor learning is highly variable and may be influenced by excitability within the procedural sensorimotor network. …”
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Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis by Xiao Chun Ling, Henry Shen-Lih Chen, Po-Han Yeh, Yu-Chun Cheng, Chu-Yen Huang, Su-Chin Shen, Yung-Sung Lee

“…<b>Purpose:</b> To evaluate the performance of deep learning (DL) in diagnosing glaucoma and predicting its progression using fundus photography and retinal optical coherence tomography (OCT) images. …”
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Why and how should we simulate platform trials? Learnings from EU-PEARL by Elias Laurin Meyer, Tobias Mielke, Marta Bofill Roig, Michaela Maria Freitag, Peter Jacko, Pavla Krotka, Peter Mesenbrink, Tom Parke, Sonja Zehetmayer, Dario Zocholl, Franz König

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Lung cancer intravasation-on-a-chip: Visualization and machine learning-assisted automatic quantification by Christy Wing Tung Wong, Joyce Zhi Xuen Lee, Anna Jaeschke, Sammi Sze Ying Ng, Kwok Keung Lit, Ho-Ying Wan, Caroline Kniebs, Dai Fei Elmer Ker, Rocky S. Tuan, Anna Blocki

“…When introduced into microvascular networks (MVNs) in microfluidic devices, EMT-IC did not affect MVN stability and physiologically relevant barrier functions.To model lung cancer intravasation on-a-chip, EMT-IC was supplemented into co-cultures of lung tumor micromasses and MVNs. …”
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Novel deep learning for multi-class classification of Alzheimer’s in disability using MRI datasets by Sumaiya Binte Shahid, Maleeha Kaikaus, Md. Hasanul Kabir, Mohammad Abu Yousuf, A. K. M. Azad, A. S. Al-Moisheer, Naif Alotaibi, Salem A. Alyami, Touhid Bhuiyan, Mohammad Ali Moni, Mohammad Ali Moni, Mohammad Ali Moni

“…Although several machine learning (ML) and deep learning (DL) algorithms have been utilized to identify Alzheimer’s disease (AD) from MRI scans, precise classification of AD categories remains challenging as neighbouring categories share common features.MethodsThis study proposes transfer learning-based methods for extracting features from MRI scans for multi-class classification of different AD categories. …”
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autoMEA: machine learning-based burst detection for multi-electrode array datasets by Vinicius Hernandes, Anouk M. Heuvelmans, Anouk M. Heuvelmans, Valentina Gualtieri, Dimphna H. Meijer, Geeske M. van Woerden, Geeske M. van Woerden, Geeske M. van Woerden, Eliska Greplova

“…Neuronal activity in the highly organized networks of the central nervous system is the vital basis for various functional processes, such as perception, motor control, and cognition. …”
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Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in trauma-induced coagulopathy by Qingsong Chen, Tao Li, Tao Zhang, Yue Zhou, Weifeng Huang, Hui Li, Li Shi, Jianxiao Li, Qi Zhang, Man Ma, Pan Wang, Hui Hu, Gongbin Wei, Jiangxia Xiang, Yuan Cheng, Jun Yang, Guangbin Huang, Yongming Li, Dingyuan Du

“…Functional analysis of genes and immunoinfiltration analysis were also conducted. …”
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Identification of ZNF652 as a Diagnostic and Therapeutic Target in Osteoarthritis Using Machine Learning by Chen Y, Liang R, Zheng X, Fang D, Lu WW, Chen Y

“…The underlying mechanism is that ZNF652 was related to nitric oxide anabolism, macrophage proliferation, various signaling pathways, and immune cells and their functions in OA. Nevertheless, the findings need to be confirmed in clinical trials and the molecular mechanism requires further study.Keywords: osteoarthritis, zinc finger protein 652, machine learning algorithms, immune cell…”
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Evaluation Methods, Indicators, and Outcomes in Learning Health Systems: Protocol for a Jurisdictional Scan by Shelley Vanderhout, Marissa Bird, Antonia Giannarakos, Balpreet Panesar, Carly Whitmore

“…We will describe evaluation approaches used both at the LHS learning cycle and system levels. To gain a comprehensive understanding of each LHS, including details specific to evaluation, self-identified LHSs will be included if they are described according to at least 4 of 11 prespecified criteria (core functionalities, analytics, use of evidence, co-design or implementation, evaluation, change management or governance structures, data sharing, knowledge sharing, training or capacity building, equity, and sustainability). …”
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Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features by Ameya Harmalkar, Roshan Rao, Yuxuan Richard Xie, Jonas Honer, Wibke Deisting, Jonas Anlahr, Anja Hoenig, Julia Czwikla, Eva Sienz-Widmann, Doris Rau, Austin J. Rice, Timothy P. Riley, Danqing Li, Hannah B. Catterall, Christine E. Tinberg, Jeffrey J. Gray, Kathy Y. Wei

“…In this work, we show two machine learning approaches – one with pre-trained language models (PTLM) capturing functional effects of sequence variation, and second, a supervised convolutional neural network (CNN) trained with Rosetta energetic features – to better classify thermostable scFv variants from sequence. …”
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Identification and Evaluation of Lipocalin-2 in Sepsis-Associated Encephalopathy via Machine Learning Approaches by Hu J, Chen Z, Wang J, Xu A, Sun J, Xiao W, Yang M

“…Enrichment analysis revealed the biological functions of these genes. Subsequently, neuroinflammation-related genes were obtained to construct a neuroinflammation-related signature. …”
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AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP by Dr. Bharti Khemani, Dr. Sachin Malave, Samyukta Shinde, Mandvi Shukla, Razzaq Shikalgar, Harshita Talwar

“…By leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, including Random Forests, Gradient Boosting Machines, and Convolutional Neural Networks (CNNs), our model aims to identify potential ADRs across different patient subgroups. …”
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Development and Optimization of a Novel Deep Learning Model for Diagnosis of Quince Leaf Diseases by A. Naderi Beni, H. Bagherpour, J. Amiri Parian

“…DCNNs improve detection or classification accuracy by developing machine-learning models with many hidden layers to extract optimal features. …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…In another study, Akbari and Yazdanian (2023) applied machine learning algorithms to determine optimal thresholds for operational loss severity data, classifying the data and estimating the capital required to cover operational risk by integrating severity and frequency distribution functions with Monte Carlo simulation. …”
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A novel general kernel-based non-negative matrix factorisation approach for face recognition by Wen-Sheng Chen, Xiya Ge, Binbin Pan

“…It not only avoids pre-image learning but also is suitable for any kernel functions as well. …”
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Neuroplasticity and the microbiome: how microorganisms influence brain change by Abdullah Al Noman, Abdulrahman Mohammed Alhudhaibi, Moushumi Afroza, Susmita Deb Tonni, Habibul Mohsin Shehab, Nusrat Jahan Iba, Tarek H. Taha, Emad M. Abdallah

“…This review examines the mechanisms by which intestinal microorganisms influence brain function, including microbial metabolite production, immune system modulation, neurotransmitter synthesis, and hormonal regulation. …”
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Assessing CO2 separation performances of IL/ZIF-8 composites using molecular features of ILs by Hasan Can Gulbalkan, Alper Uzun, Seda Keskin

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Row-Column Decoupled Loss: A Probability-Based Geometric Similarity Framework for Aerial Micro-Target Detection by Xiaohui Chen, Yunzhi Ling, Lingjun Chen, Li Liu, Xuechen Cui, Ziqiang Liu, Zhenyu Wang

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Association of systolic blood pressure variability with cognitive decline in type 2 diabetes: A post hoc analysis of a randomized clinical trial by Junmin Chen, Xuan Zhao, Huidan Liu, Kan Wang, Xiaoli Xu, Siyu Wang, Mian Li, Ruizhi Zheng, Libin Zhou, Yufang Bi, Yu Xu

“…Abstract Background We aimed to explore the association between visit‐to‐visit systolic blood pressure variability (BPV) and cognitive function in individuals with type 2 diabetes. Methods We performed a post hoc analysis of the Action to Control Cardiovascular Risk in Diabetes Memory in Diabetes (ACCORD‐MIND) substudy. …”
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