AMCL: supervised contrastive learning with hard sample mining for multi-functional therapeutic peptide prediction by Jiwei Fang, Henghui Fan, Jintao Zhao, Jianping Zhao, Junfeng Xia

Subjects: “…Multi-functional therapeutic peptides…”
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Functional Brain Network Disruptions in Parkinson’s Disease: Insights from Information Theory and Machine Learning by Ömer Akgüller, Mehmet Ali Balcı, Gabriela Cioca

“…<b>Objectives:</b> This study investigates disruptions in functional brain networks in Parkinson’s Disease (PD), using advanced modeling and machine learning. …”
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Machine learning-based pseudo-continuous pedotransfer function for predicting soil freezing characteristic curve by Sangyeong Park, Yongjoon Choe, Hangseok Choi, Khanh Pham

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A deep learning framework for predicting disease-gene associations with functional modules and graph augmentation by Xianghu Jia, Weiwen Luo, Jiaqi Li, Jieqi Xing, Hongjie Sun, Shunyao Wu, Xiaoquan Su

“…While graph representation learning has recently introduced groundbreaking approaches for predicting novel associations, existing studies always overlooked the cumulative impact of functional modules such as protein complexes and the incompletion of some important data such as protein interactions, which limits the detection performance. …”
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A comprehensive machine learning for high throughput Tuberculosis sequence analysis, functional annotation, and visualization by Md. Saddam Hossain, Md. Parvez Khandocar, Farzana Akter Riti, Md. Yeakub Ali, Prithbey Raj Dey, S M Jahurul Haque, Amira Metouekel, Atrsaw Asrat Mengistie, Mohammed Bourhia, Farid Khallouki, Khalid S. Almaary

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Machine learning orbital-free density functional theory resolves shell effects in deformed nuclei by X. H. Wu, Z. X. Ren, P. W. Zhao

“…Here, we develop a machine-learning-based orbital-free density functional theory, enabling the description of ground-state properties and potential energy curves for both spherical 16O and deformed 20Ne nuclei. …”
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Reinforcement Learning-Based Formulations With Hamiltonian-Inspired Loss Functions for Combinatorial Optimization Over Graphs by Redwan Ahmed Rizvee, Raheeb Hassan, Md. Mosaddek Khan

Subjects: “…Deep reinforcement learning…”
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A Novel Multimodal Deep Learning Approach With Loss Function for Detection of Sleep Apnea Events by Alireza Fakhim Babaei, Jafar Tanha, Mohammad Ali Balafar, Seyedehsan Roshan

“…To promote model diversity and robustness, we further introduce a novel loss function that encourages the network&#x2019;s key modules to learn disparate feature representations. …”
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Overcoming Data Scarcity: Guiding Citation Function Classification With Prompt-Based Few-Shot Learning by Krittin Chatrinan, Thanapon Noraset, Suppawong Tuarob

Subjects: “…Citation function analysis…”
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Learning Spatial Density Functions of Random Waypoint Mobility over Irregular Triangles and Convex Quadrilaterals by Yiming Feng, Wanxin Gao, Lefeng Zhang, Minfeng Qi, Qi Zhong, Ningran Li

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Knowledge Transfer in Deep Reinforcement Learning via an RL-Specific GAN-Based Correspondence Function by Marko Ruman, Tatiana V. Guy

“…Deep reinforcement learning has demonstrated superhuman performance in complex decision-making tasks, but it struggles with generalization and knowledge reuse&#x2014;key aspects of true intelligence. …”
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Using graph machine learning to identify functioning in patients with low back pain in terms of ICF by Linda Nieminen, Harri Ketamo, Jari Vuori, Markku Kankaanpää

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Research on Functional Mathematical Literacy of Pupils with Moderate Special Educational Needs Learning in Mainstream Schools by Laima Tomėnienė

“… The article deals with the research on functional mathematical literacy of pupils with moderate special educational needs (SEN) learning in eighth forms of mainstream schools. …”
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Prediction of potential occurrence of historical objects with defensive function in Slovakia using machine learning approach by Jana Vojteková, Saeid Janizadeh, Matej Vojtek, Anna Tirpáková, Matej Ruttkay, František Petrovič

Subjects: “…Historical object with defensive function…”
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Machine Learning Accurate Exchange–Correlation Potentials for Reducing Delocalization Error in Density Functional Theory by Yuan Zhuang, Yonghao Gu, Beini Zhang, Jiang Wu, Guanhua Chen

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Functional gastrointestinal disorders predictors in neonates and toddlers: A machine learning approach to risk assessment by Flavia Indrio, Elio Masciari, Flavia Marchese, Matteo Rinaldi, Gianfranco Maffei, Ilaria Gangai, Assunta Grillo, Roberta De Benedetto, Enea Vincenzo Napolitano, Isadora Beghetti, Luigi Corvaglia, Antonio Di Mauro, Arianna Aceti

“…Background: Functional Gastrointestinal Disorders (FGIDs) can pose a great burden on affected children, their families, and the healthcare system. …”
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Using Machine Learning for Analysis of Wideband Acoustic Immittance and Assessment of Middle Ear Function in Infants by Shan Peng, Yukun Zhao, Xinyi Yao, Huilin Yin, Bei Ma, Ke Liu, Gang Li, Yang Cao

Subjects: “…machine learning…”
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Development of Deep Learning Simulation and Density Functional Theory Framework for Electrocatalyst Layers for PEM Electrolyzers by Jaydev Zaveri, Shankar Raman Dhanushkodi, Michael W. Fowler, Brant A. Peppley, Dawid Taler, Tomasz Sobota, Jan Taler

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Higher-order and distributed synergistic functional interactions encode information gain in goal-directed learning by Etienne Combrisson, Ruggero Basanisi, Matteo Neri, Guillaume Auzias, Giovanni Petri, Daniele Marinazzo, Stefano Panzeri, Andrea Brovelli

“…However, the role of cortico-cortical functional interactions remains unclear. To address this question, we integrated information dynamics analysis with magnetoencephalography to investigate the encoding of learning signals through neural interactions. …”
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SuperEdgeGO: Edge-supervised graph representation learning for enhanced protein function prediction. by Shugang Zhang, Yuntong Li, Wenjian Ma, Qing Cai, Jing Qin, Xiangpeng Bi, Huasen Jiang, Xiaoyu Huang, Zhiqiang Wei

“…To date, there have been over 200 million known proteins, but only 0.2% of them have well-annotated functional terms. By measuring the contacts among residues, proteins can be described as graphs so that the graph leaning approaches can be applied to learn protein representations. …”
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