Urban Parking Demand Forecasting Using xLSTM-Informer Model by Jimei Ma, Zhong Wang

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deepTFBS: Improving within‐ and Cross‐Species Prediction of Transcription Factor Binding Using Deep Multi‐Task and Transfer Learning by Jingjing Zhai, Yuzhou Zhang, Chujun Zhang, Xiaotong Yin, Minggui Song, Chenglong Tang, Pengjun Ding, Zenglin Li, Chuang Ma

“…In this study, deepTFBS, a comprehensive deep learning (DL) framework that builds a robust DNA language model of TF binding grammar for accurately predicting TFBSs within and across plant species is presented. …”
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Burden of neurological diseases in Asia, from 1990 to 2021 and its predicted level to 2045: a Global Burden of Disease study by Rujing Yang, Xinyu Liu, Zhexuan Zhao, Yan Zhao, Xiaoqing Jin

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A nomogram based on quantitative MR signal intensity predicts early response to combined systemic treatment in patients with hepatocellular carcinoma by Ran Tao, Haohao Lu, Xiangjun Dong, Qian Qian Ren, Hongjie Fan, Zhaoming Tang, Xiangwen Xia

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Peripheral blood markers predict prognosis and irAEs of stage IV driver gene-negative lung adenocarcinoma treated with ICIs by Yu Li, Lei Cao, Lei Liu, Yawen Ding, Feng Cao

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Preoperative MRI and CA19-9 for predicting occult lymph node metastasis in small pancreatic ductal adenocarcinoma (≤ 2 cm) by Qiying Tang, Lei Li, Zhiwei Pan, Jianbo Li, Xiaolan Huang, Mengsu Zeng, Haitao Sun, Jianjun Zhou

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LRMAHpan: a novel tool for multi-allelic HLA presentation prediction using Resnet-based and LSTM-based neural networks by Xue Mi, Shaohao Li, Zheng Ye, Zhu Dai, Bo Ding, Bo Sun, Yang Shen, Yang Shen, Zhongdang Xiao, Zhongdang Xiao

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Predicting ROS1 and ALK fusions in NSCLC from H&E slides with a two-step vision transformer approach by Eghbal Amidi, Mohammadreza Ramzanpour, Ming Chen, Tommy Boucher, Mukund Varma, Timothy Samec, Brian Lamon, Nicolas Stransky, Mark R. Miglarese, Matthew Oberley, David Spetzler, George W. Sledge

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A predictive nomogram based on triglyceride glucose index to body mass index ratio for low appendicular skeletal muscle mass by Jingfeng Zou, Nianli Zhou, Shaotian Li, Liping Wang, Jiajia Ran, Xin Yang, Meng Zhang, Wen Peng

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Artificial intelligence generated 3D body composition predicts dose modifications in patients undergoing neoadjuvant chemotherapy for rectal cancer by Alex Besson, Ke Cao, Ahmed Mardinli, Lara Wirth, Josephine Yeung, Rory Kokelaar, Peter Gibbs, Fiona Reid, Justin M. Yeung

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Prediction of the composition of prolonged release tablets based on 4,4'-(propandiamido) sodium dibenzoate using the SeDeM method by Ju. M. Kotsur, Ju. M. Ladytko, I. A. Narkevich, E. V. Flisyuk

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Machine learning algorithms predict breast cancer incidence risk: a data-driven retrospective study based on biochemical biomarkers by Qianqian Guo, Peng Wu, Junhao He, Ge Zhang, Wu Zhou, Qianjun Chen

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Early reduction in total cholesterol to high-density lipoprotein cholesterol ratio predicts hydroxychloroquine efficacy in treating IgA nephropathy by Yaotong Shi, Ting Yang, Yuan Feng, Nan Li, Qiuyuan Shao, Chunming Jiang, Jing Liu

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12-Month trajectories of physical and mental symptom scores after COVID-19 hospitalization and their role in predicting “very long” COVID by Oleksii Honchar, Tetiana Ashcheulova, Alla Bobeiko, Viktor Blazhko, Eduard Khodosh, Nataliia Matiash, Vladyslav Syrota

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AI may help to predict thyroid nodule malignancy based on radiomics features from [18F]FDG PET/CT by Krystian Ślusarz, Mikolaj Buchwald, Adrian Szczeszek, Szymon Kupinski, Anna Gramek-Jedwabna, Wojciech Andrzejewski, Juliusz Pukacki, Robert Pękal, Marek Ruchała, Rafał Czepczyński, Cezary Mazurek

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SACrA score to predict the initiation of renal replacement therapy in critically ill patients: a single-center retrospective study by Ginga Suzuki, Saria Nishioka, Toshimitsu Kobori, Yuka Masuyama, Saki Yamamoto, Hibiki Serizawa, Yoshimi Nakamichi, Mitsuru Honda

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DeepCCDS: Interpretable Deep Learning Framework for Predicting Cancer Cell Drug Sensitivity through Characterizing Cancer Driver Signals by Jiashuo Wu, Jiyin Lai, Xilong Zhao, Ziyi Wang, Yongbao Zhang, Liqiang Wang, Yinchun Su, Yalan He, Siyuan Li, Ying Jiang, Junwei Han

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Dynamic Modeling of Aeroengine Rotor Speed Based on Data Fusion Method by Jun Hong, Hongxin Wang, Ziqiao Chen, Jiawei Lu, Gang Xiao

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Field-crop transcriptome models are enhanced by measurements in systematically controlled environments by Yoichi Hashida, Daisuke Kyogoku, Suguru E. Tanaka, Naoya Mori, Takanari Tanabata, Hiroyuki Watanabe, Atsushi J. Nagano

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Defect modeling in semiconductors: the role of first principles simulations and machine learning by Md Habibur Rahman, Arun Mannodi-Kanakkithodi

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