Noncoding variants and sulcal patterns in congenital heart disease: Machine learning to predict functional impact by Enrique Mondragon-Estrada, Jane W. Newburger, Steven R. DePalma, Martina Brueckner, John Cleveland, Wendy K. Chung, Bruce D. Gelb, Elizabeth Goldmuntz, Donald J. Hagler, Jr., Hao Huang, Patrick McQuillen, Thomas A. Miller, Ashok Panigrahy, George A. Porter, Jr., Amy E. Roberts, Caitlin K. Rollins, Mark W. Russell, Martin Tristani-Firouzi, P. Ellen Grant, Kiho Im, Sarah U. Morton

“…Genes predicted to be regulated by these ncDNVs were enriched for functions related to neuronal development. This highlights the potential of deep learning models to generate hypotheses about the role of noncoding variants in brain development.…”
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The Impact of Functional Asymmetry of the Cerebral Hemispheres in Students of a Physics and Mathematics Lyceum on the Learning Outcomes by Oksana Ikkert, Tetiana Korol, Kateryna Hlazunova, Iryna Tsinkevych

“…This indicates that the type of interhemispheric asymmetry is not a factor that causes learning difficulties or vice versa. The article summarizes the necessity of considering the individual psychophysiological characteristics of students in the educational process, particularly their functional asymmetry of the cerebral hemispheres. …”
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DPFunc: accurately predicting protein function via deep learning with domain-guided structure information by Wenkang Wang, Yunyan Shuai, Min Zeng, Wei Fan, Min Li

“…In this study, we propose a deep learning-based solution, named DPFunc, for accurate protein function prediction with domain-guided structure information. …”
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Ramp Metering for a Distant Downstream Bottleneck Using Reinforcement Learning with Value Function Approximation by Yue Zhou, Kaan Ozbay, Pushkin Kachroo, Fan Zuo

“…The mechanism and development of the approximate value function and how learning of its parameters is integrated into the Q-learning process are well explained. …”
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The Effectiveness of Self-instruction training on Executive Functions and Cognitive Inhibition of Students with Special Learning Disorder by Mahmood Jabarzadeh Chaharbrod, Mansoor Beyrami, Asghar Nokhestin Goldost, Tooraj HAshemi

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Functional polymorphism of the mu-opioid receptor gene (OPRM1) influences reinforcement learning in humans. by Mary R Lee, Courtney L Gallen, Xiaochu Zhang, Colin A Hodgkinson, David Goldman, Elliot A Stein, Christina S Barr

“…Previous reports on the functional effects (i.e., gain or loss of function), and phenotypic outcomes (e.g., changes in addiction vulnerability and stress response) of a commonly occurring functional single nucleotide polymorphism (SNP) of the mu-opioid receptor (OPRM1 A118G) have been inconsistent. …”
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Rectified Tangent Activation (RTA): A Novel Activation Function for Enhanced Deep Learning Performance by Gaurav Kumar Pandey, Sumit Srivastava

Subjects: “…Deep learning…”
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Application of Machine Learning for Identifying Factors Associated with Renal Function Impairment in Gouty Arthritis Patients by Osman Cüre, Fatih Bal

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Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application by Chao Li, Ji Chen, Mengshi Dong, Hao Yan, Feng Chen, Ning Mao, Shuai Wang, Xiaozhu Liu, Yanqing Tang, Fei Wang, Jie Qin

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Predictions of Spartina alterniflora leaf functional traits based on hyperspectral data and machine learning models by Wei Li, Xueyan Zuo, Zhijun Liu, Leichao Nie, Huazhe Li, Junjie Wang, Zhiguo Dou, Yang Cai, Xiajie Zhai, Lijuan Cui

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Hyperspectral Image Joint Super-Resolution via Local Implicit Spatial-Spectral Function Learning by Yanan Zhang, Jizhou Zhang, Sijia Han

“…In this paper, we propose the Local Implicit Spatial-spectral Function (LISSF), which learns a local continuous representation of high spatial resolution hyperspectral images (HR-HSI) from the discrete inputs. …”
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Learning control system of lifting machine motors by V. I. Zimovets, A. S. Chirva, O. I. Marishchenko

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A machine learning model for automated contact tracing during disease outbreaks by Zeyad Aklah, Amean Al-Safi, Marwa H. Abdali, Khalid Al-jabery

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Skew Logistic Distribution Applied as Activation Function in Artificial Neural Networks by Eder Silva Dos Santos, Altemir da Silva Braga, Ana Beatriz Alvarez, Thuanne Paixao

Subjects: “…Activation function…”
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Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness by Junhui Song, Zhangqi Zheng, Afei Li, Zhixin Xia, Yongshan Liu

Subjects: “…federated learning…”
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Shifted Hexpo activation function: An improved vanishing gradient mitigation activation function for disease classification by Joseph Otoo, Suleman Nasiru, Irene Dekomwine Angbing

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Synergistic application of molecular docking and machine learning for improved binding pose by Li Yaqi, Lin Hongrui, Yang He, Yuan Yannan, Zou Rongfeng, Zhou Gengmo, Zheng Linfeng, Zheng Hang

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Learning High-Dimensional Chaos Based on an Echo State Network with Homotopy Transformation by Shikun Wang, Fengjie Geng, Yuting Li, Hongjie Liu

“…Based on an echo state network (ESN), we introduce homotopy transformation in topological theory to learn high-dimensional chaos. On the premise of maintaining the basic topological properties, our model can obtain the key features of chaos for learning through the continuous transformation between different activation functions, achieving an optimal balance between nonlinearity and linearity to enhance the generalization capability of the model. …”
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Prediction of Corroded Pipeline Failure Pressure Based on Empirical Knowledge and Machine Learning by Hongbo Liu, Xiangzhao Meng

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Ensemble learning techniques reveals multidimensional EEG feature alterations in pediatric schizophrenia by Ying Mao, Ying Mao, Fang Wang, Shan Wang, Zhaowei Wang, Gang Li, Xuchen Qi, Xuchen Qi, Yu Sun, Yu Sun

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