Circulating miR-4454 as a potential biomarker for the diagnosis of T2DM and the prediction value of comorbidity and complications in T2DM by Song Yang, Mei Wang, Qin Deng

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Is higher resolution always better? A comparison of open-access DEMs for optimized slope unit delineation and regional landslide prediction by M. Ahmed, G. Titti, S. Trevisani, L. Borgatti, M. Francioni

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Evaluating clinician acceptability of the prototype CanRisk tool for predicting risk of breast and ovarian cancer: A multi-methods study. by Stephanie Archer, Chantal Babb de Villiers, Fiona Scheibl, Tim Carver, Simon Hartley, Andrew Lee, Alex P Cunningham, Douglas F Easton, Jennifer G McIntosh, Jon Emery, Marc Tischkowitz, Antonis C Antoniou, Fiona M Walter

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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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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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Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea by Junhyug Noh, Sun Young Park, Wonho Bae, Kangil Kim, Jang-Hee Cho, Jong Soo Lee, Shin-Wook Kang, Yong-Lim Kim, Yon Su Kim, Chun Soo Lim, Jung Pyo Lee, Kyung Don Yoo

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GraftIQ: Hybrid multi-class neural network integrating clinical insight for multi-outcome prediction in liver transplant recipients by Divya Sharma, Neta Gotlieb, Daljeet Chahal, Joseph C. Ahn, Bastian Engel, Richard Taubert, Eunice Tan, Lau Kai Yun, Sara Naimimohasses, Ankit Ray, Yoojin Han, Sara Gehlaut, Maryam Shojaee, Surabie Sivanendran, Maryam Naghibzadeh, Amirhossein Azhie, Sareh Keshavarzi, Kai Duan, Leslie Lilly, Nazia Selzner, Cynthia Tsien, Elmar Jaeckel, Wei Xu, Mamatha Bhat

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