Barlow Twins deep neural network for advanced 1D drug–target interaction prediction by Maximilian G. Schuh, Davide Boldini, Annkathrin I. Bohne, Stephan A. Sieber

“…By reducing time and cost, machine learning and deep learning can accelerate this laborious discovery process. …”
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FoxA1 knockdown promotes BMSC osteogenesis in part by activating the ERK1/2 signaling pathway and preventing ovariectomy-induced bone loss by Lijun Li, Renjin Lin, Yang Xu, Lingdi Li, Zhijun Pan, Jian Huang

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Multimodal data integration in early-stage breast cancer by Arnau Llinas-Bertran, Maria Butjosa-Espín, Vittoria Barberi, Jose A. Seoane

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AI-driven prediction of drug activity against Toxoplasma gondii: Data augmentation and deep neural networks for limited datasets by Natalia V. Karimova, Ravithree D. Senanayake

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Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data by Jaqueline Alvarenga Silveira, Alexandre Ray da Silva, Mariana Zuliani Theodoro de Lima

“…SVMs are effective with high-dimensional clinical data, while Neural Networks in genetic and molecular analysis. Despite these advancements, limitations such as dataset diversity, sample size, and evaluation standardization persist, emphasizing the need for further research. …”
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AI-enhanced 3D-QSAR screening of fragment-based novel designed molecules targeting Phalaris minor ACCase by Bikash Kumar Rajak, Priyanka Rani, Durg Vijay Singh, Nitesh Singh

“…Leveraging artificial intelligence (AI) and machine learning (ML) in the agritech revolution, researchers are now applying advanced computational techniques to identify and design effective ACCase inhibitors. …”
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Improving drug repositioning with negative data labeling using large language models by Milan Picard, Mickael Leclercq, Antoine Bodein, Marie Pier Scott-Boyer, Olivier Perin, Arnaud Droit

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Protection of liver sinusoidal endothelial cells using different preservation solutions by Julia H E Houtzager, Anne-Marieke van Stalborch, Charlotte Hofstee, Thomas M van Gulik, Jaap D van Buul

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LASSO–MOGAT: a multi-omics graph attention framework for cancer classification by Fadi Alharbi, Aleksandar Vakanski, Murtada K. Elbashir, Mohanad Mohammed

“… The application of machine learning (ML) methods to analyze changes in gene expression patterns has recently emerged as a powerful approach in cancer research, enhancing our understanding of the molecular mechanisms underpinning cancer development and progression. …”
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Ankrd1 as a potential biomarker for the transition from acute kidney injury to chronic kidney disease by Hailin Li, Lemei Hu, Changqing Zheng, Ying Kong, Ming Liang, Quhuan Li

“…In this study, we used single-nucleus RNA sequencing data from GEO database (GSE139107) to identify molecular markers during the transition from AKI to CKD caused by IRI. …”
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A new signature associated with anoikis predicts the outcome and immune infiltration in nasopharyngeal carcinoma by Yonglin Luo, Wenyang Wei, Yaxuan Huang, Jun Li, Weiling Qin, Quanxiang Hao, Jiemei Ye, Zhe Zhang, Yushan Liang, Xue Xiao, Yonglin Cai

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Fast In-House Next-Generation Sequencing in the Diagnosis of Metastatic Non-small Cell Lung Cancer: A Hospital Budget Impact Analysis by Ubong Silas, Maximilian Blüher, Antonia Bosworth Smith, Rhodri Saunders

“…**Background:** Targeted therapy for cancer is becoming more frequent as the understanding of the molecular pathogenesis increases. Molecular testing must be done to use targeted therapy. …”
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QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules by Vivin Vinod, Peter Zaspel

“…Abstract Progress in both Machine Learning (ML) and Quantum Chemistry (QC) methods have resulted in high accuracy ML models for QC properties. …”
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Advancements in the Application of Convolutional Neural Networks in Ultrasound Imaging for Breast Cancer Diagnosis and Treatment by An Zichen, Li Fan

“…It summarizes the use of DL models in breast cancer screening and in preoperative prediction of molecular subtypes, response to neoadjuvant chemotherapy (NAC), and axillary lymph node (ALN) metastasis status. …”
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InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks by Mahmood Kalemati, Mojtaba Zamani Emani, Somayyeh Koohi

“…Predicting drug-target binding affinity via in silico methods is crucial in drug discovery. Traditional machine learning relies on manually engineered features from limited data, leading to suboptimal performance. …”
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Exploring RNA cargo in extracellular vesicles for pleural mesothelioma detection by Agnieszka Kraft, Michaela B. Kirschner, Vanessa Orlowski, Manuel Ronner, Caroline Bodmer, Valentina Boeva, Isabelle Opitz, Mayura Meerang

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Fabrication of Nano-Silver Composite Using Amomum longiligulare Fruit Polysaccharides and Their Biological Activities by Lian Y, Ma N, Cheng Q, Luo M, Xu Z, He F, Zhou X, Zhang Y, Jin D, Kong Y, Wang Y, Wei N

“…The structure of ALP-D was characterized by FT-IR, monosaccharide composition and molecular weight.The optimal conditions for the green synthesis of silver nanoparticles using ALP-D were determined through single-factor experiments. …”
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Harnessing macrophage-drug conjugates for allogeneic cell-based therapy of solid tumors via the TRAIN mechanism by Bartlomiej Taciak, Maciej Bialasek, Malgorzata Kubiak, Ilona Marszalek, Malgorzata Gorczak, Olha Osadchuk, Daria Kurpiel, Damian Strzemecki, Karolina Barwik, Marcin Skorzynski, Julia Nowakowska, Waldemar Lipiński, Łukasz Kiraga, Jan Brancewicz, Robert Klopfleisch, Łukasz Krzemiński, Emilia Gorka, Anna Smolarska, Irena Padzinska-Pruszynska, Małgorzata Siemińska, Jakub Guzek, Jan Kutner, Marlena Kisiala, Krzysztof Wozniak, Giacomo Parisi, Roberta Piacentini, Luca Cassetta, Lesley M. Forrester, Lubomir Bodnar, Tobias Weiss, Alberto Boffi, Paulina Kucharzewska, Tomasz P. Rygiel, Magdalena Krol

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