Predicting Oncological and Functional Outcomes by Nephrectomy Type for T1 Renal Tumors Using Machine Learning Models by Dongrul Shin, Maisy Song, Jungyo Suh, Cheryn Song

“…Purpose Determining the optimal surgical approach for patients with T1 renal tumors requires balancing long-term oncological and renal functional outcomes. Using machine learning algorithms, we aimed to develop a model to predict both outcomes simultaneously, according to each radical (RN) and partial nephrectomy (PN). …”
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A hybrid machine learning framework for functional annotation of mitochondrial glutathione transport and metabolism proteins in cancers by Luke Kennedy, Jagdeep K. Sandhu, Mary-Ellen Harper, Miroslava Cuperlovic-Culf

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Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data by Ísak Valsson, Matthew T. Warren, Charlotte M. Deane, Aniket Magarkar, Garrett M. Morris, Philip C. Biggin

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Frequency Response Function-Based Finite Element Model Updating Using Extreme Learning Machine Model by Yu Zhao, Zhenrui Peng

“…A frequency response function- (FRF-) based surrogate model for finite element model updating (FEMU) is presented in this paper. …”
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MMFuncPhos: A Multi‐Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types by Juan Xie, Ruihan Dong, Jintao Zhu, Haoyu Lin, Shiwei Wang, Luhua Lai

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Modeling and forecasting of the high-dimensional time series data with functional data analysis and machine learning approaches by Yousuf Alkhezi, Hajar M. Alkhezi, Ahmad Shafee

“…The system is built on the latest functional autoregressive model of order one [FAR(1)]. …”
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Functionally characterizing obesity-susceptibility genes using CRISPR/Cas9, in vivo imaging and deep learning by Eugenia Mazzaferro, Endrina Mujica, Hanqing Zhang, Anastasia Emmanouilidou, Anne Jenseit, Bade Evcimen, Christoph Metzendorf, Olga Dethlefsen, Ruth JF Loos, Sara Gry Vienberg, Anders Larsson, Amin Allalou, Marcel den Hoed

“…Abstract Hundreds of loci have been robustly associated with obesity-related traits, but functional characterization of candidate genes remains a bottleneck. …”
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Cued Reactivation of Motor Learning during Sleep Leads to Overnight Changes in Functional Brain Activity and Connectivity. by James N Cousins, Wael El-Deredy, Laura M Parkes, Nora Hennies, Penelope A Lewis

“…Participants learned two serial reaction time task (SRTT) sequences associated with different auditory tones (high or low pitch). …”
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Editorial: Invertebrate brains as model systems for learning, memory, and recall: development, anatomy and function of memory systems by André Fiala, Hagar Meltzer, Hagar Meltzer, Michael Schleyer, Oriane Turrel, Annekathrin Widmann

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Machine learning predicts improvement of functional outcomes in spinal cord injury patients after inpatient rehabilitation by Mohammad Rasoolinejad, Irene Say, Peter B. Wu, Xinran Liu, Yan Zhou, Yan Zhou, Nathan Zhang, Emily R. Rosario, Daniel C. Lu, Daniel C. Lu, Daniel C. Lu

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Discovering the key symptoms for identifying patterns in functional dyspepsia patients: A doctor's decision and machine learning by Da-Eun Yoon, Heeyoung Moon, In-Seon Lee, Younbyoung Chae

Subjects: “…Functional dyspepsia…”
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Cutting-edge tech for basketball: Virtual reality and machine learning in executive function and decision-making training by Ahmad Rahmadani, Oki Candra, Feby Elra Perdima, Novri Gazali, Mohd Sofian Omar-Fauzee

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Research on monthly runoff prediction model considering secondary decomposition of multiple fitness functions and deep learning by Zhongfeng Zhao, Xueni Wang, Hua Jin, Jie Zheng

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Functional role of cell classes in monkey prefrontal cortex after learning a working memory task by Amirreza Asadi, Christos Constantinidis, Mohammad Reza Daliri

“…To investigate this issue, we analyzed single-unit recordings from the PFC of monkeys during the passive viewing phase before they learned the task rules and after learning, during the execution of active working memory tasks (spatial and feature). …”
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Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data by Quan Zhou, Lihua Zhang, Nan Xiang, Lele Zhang, Fuqiang Ma, Fengchang Yu, Shenhui Lv, Zhilin Lu, He-Rong Mao

“…Objectives To develop and validate a machine learning (ML) model to differentiate malignant from benign thyroid nodules (TNs) based on the routine data and provide diagnostic assistance for medical professionals.Setting A qualified panel of 1649 patients with TNs from one hospital were stratified by gender, age, free triiodothyronine (FT3), free thyroxine (FT4) and thyroid peroxidase antibody (TPOAB).Participants Thyroid function (TF) data of 1649 patients with TNs were collected in a single centre from January 2018 to June 2022, with a total of 273 males and 1376 females, respectively.Measures Seven popular ML models (Random Forest, Decision Tree, Logistic Regression (LR), K-Neighbours, Gaussian Naive Bayes, Multilayer Perception and Gradient Boosting) were developed to predict malignant and benign TNs, whose performance indicators included area under the curve (AUC), accuracy, recall, precision and F1 score.Results A total of 1649 patients were enrolled in this study, with the median age of 45.15±13.41 years, and the male to female ratio was 1:5.055. …”
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Predicting acupuncture efficacy for neck pain based on functional connectivity features: a machine learning study by Zhen Gao, Mengjie Cui, Cheng Xu, Haijun Wang, Yanlin Zhang, Laixi Ji

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A hierarchical reinforcement learning approach for energy‐aware service function chain dynamic deployment in IoT by Shuyi Wang, Haotong Cao, Longxiang Yang

“…In this regard, a convolutional neural network‐based hierarchical reinforcement learning approach is provided to lower total energy consumption and carbon emissions in the dynamic service function chaining situations. …”
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Innovative machine learning-based prediction of early airway hyperresponsiveness using baseline pulmonary function parameters by Hua Yang, Xingru Zhao, Zhuochang Chen, Lihong Yang, Guihua Zhao, Chenxiao Xu, Jinyi Xu

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Machine-Learning Approach to Identify Organic Functional Groups from FT-IR and NMR Spectral Data by Gwanho Lee, Hyekyoung Shim, Juhyun Cho, Sang-Il Choi

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Synergistic detection of E. coli using ultrathin film of functionalized graphene with impedance spectroscopy and machine learning by Amrit Kumar, Shweta Mishra, R. K. Gupta, V. Manjuladevi

“…This study presents a novel, label-free approach for E. coli detection using ultrathin Langmuir-Blodgett films of octadecylamine functionalized (ODA)-functionalized graphene on gold electrodes, with a detection range spanning $$10^{1}-10^{6}$$ colony-forming units/mL (CFU/mL). …”
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