Predicting molecular mechanisms of hereditary diseases by using their tissue‐selective manifestation by Eyal Simonovsky, Moran Sharon, Maya Ziv, Omry Mauer, Idan Hekselman, Juman Jubran, Ekaterina Vinogradov, Chanan M Argov, Omer Basha, Lior Kerber, Yuval Yogev, Ayellet V Segrè, Hae Kyung Im, GTEx Consortium, Ohad Birk, Lior Rokach, Esti Yeger‐Lotem

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Content-Based Brain Magnetic Resonance Image Retrieval and Classification With the Proposed Deep Learning and Tissue-Based System by Bedriye Dogan, Hursit Burak Mutlu, Muhammed Yildirim, Sercan Yalcin, Serpil Aslan, Niranjana Sampathila, Ozal Yildirim, Edward J. Ciaccio, Ru-San Tan, U. Rajendra Acharya

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OncoTrace‐TOO: Interpretable Machine Learning Framework for Cancer Tissue‐of‐Origin Identification Using Transcriptomic Signatures by Yang Hao, Haochun Huang, Daiyun Huang, Jianwen Ruan, Xin Liu, Jianquan Zhang

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Identify the potential target of efferocytosis in knee osteoarthritis synovial tissue: a bioinformatics and machine learning-based study by Shangbo Niu, Shangbo Niu, Shangbo Niu, Mengmeng Li, Mengmeng Li, Mengmeng Li, Jinling Wang, Jinling Wang, Jinling Wang, Peirui Zhong, Peirui Zhong, Peirui Zhong, Xing Wen, Xing Wen, Xing Wen, Fujin Huang, Fujin Huang, Fujin Huang, Linwei Yin, Linwei Yin, Linwei Yin, Yang Liao, Yang Liao, Yang Liao, Jun Zhou, Jun Zhou, Jun Zhou

“…A critical mechanism in the pathogenesis of KOA is impaired efferocytosis in synovial tissue. The present study aimed to identify and validate key efferocytosis-related genes (EFRGs) in KOA synovial tissue by using comprehensive bioinformatics and machine learning approaches.MethodsWe integrated three datasets (GSE55235, GSE55457, and GSE12021) from the Gene Expression Omnibus database to screen differentially expressed genes (DEGs) associated with efferocytosis and performed weighted gene co-expression network analysis. …”
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Proteomics and machine learning: Leveraging domain knowledge for feature selection in a skeletal muscle tissue meta-analysis by Alireza Shahin-Shamsabadi, John Cappuccitti

Subjects: “…Machine learning…”
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Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review by Chun-Liang Lai, Riya Karmakar, Arvind Mukundan, Ragul Kumar Natarajan, Song-Cun Lu, Cheng-Yi Wang, Hsiang-Chen Wang

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Histopathological Image Analysis Using Machine Learning to Evaluate Cisplatin and Exosome Effects on Ovarian Tissue in Cancer Patients by Tuğba Şentürk, Fatma Latifoğlu, Çiğdem Gülüzar Altıntop, Arzu Yay, Zeynep Burçin Gönen, Gözde Özge Önder, Özge Cengiz Mat, Yusuf Özkul

“…This study aims to evaluate the reparative effects of cisplatin and exosome treatments on ovarian tissue damage through the analysis of histopathological images and machine learning (ML)-based classification techniques. …”
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Optical Coherence Tomography-Based Fingerprint Presentation Attack Detection via Multi-Tissue Features Contribution Analysis by Zhanqing Li, Jie Ji, Jiajin Qi, Haonan Fang, Yipeng Liu

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Robotic Integration of Massage and Real-Time Tissue Assessment: A Technological Framework by Yizhong Zhang, Hui Zhang, Chen Qiu, Shaosong Xie, Min Tang, Yue-Guang Fan

“…Compared with traditional soft tissue assessment devices, the massage robot equipped with a stiffness assessment system that leverages integrated sensors, enables real-time monitoring of soft tissue conditions during massage, thus integrates detection and treatment seamlessly. …”
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Cross study transcriptomic investigation of Alzheimer’s brain tissue discoveries and limitations by Fernando Koiti Tsurukawa, Yixiang Mao, Cesar Sanchez-Villalobos, Nishtha Khanna, Chiquito J. Crasto, J. Josh Lawrence, Ranadip Pal

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Deep Learning Technique for Automatic Segmentation of Proximal Hip Musculoskeletal Tissues From CT Scan Images: A MrOS Study by Mahdi Imani, Jared Buratto, Thang Dao, Erik Meijering, Sara Vogrin, Timothy C. Y. Kwok, Eric S. Orwoll, Peggy M. Cawthon, Gustavo Duque

“…This study aims to develop a deep learning technique for segmentation and quantification of musculoskeletal tissues in CT scans of proximal hip. …”
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Different effects of scopolamine on learning, memory, and nitric oxide metabolite levels in hippocampal tissues of ovariectomized and Sham-operated rats by Hamid Azizi-Malekabadi, Mahmoud Hosseini, Mohammad Soukhtanloo, Reihaneh Sadeghian, Masoud Fereidoni, Fatimeh Khodabandehloo

“…Different effects of scopolamine on learning, memory, and nitric oxide (NO) metabolites in hippocampal tissues of ovariectomized (OVX) and sham-operated rats were investigated. …”
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In-depth and high-throughput spatial proteomics for whole-tissue slice profiling by deep learning-facilitated sparse sampling strategy by Ritian Qin, Jiacheng Ma, Fuchu He, Weijie Qin

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Empowering minds: An e-book blending contextual teaching learning with plant tissue exploration to boost argumentation and cognitive skills by Alfiya Damayanti, Paramita. C. Kuswandi

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Development of ultrasound-based clinical, radiomics and deep learning fusion models for the diagnosis of benign and malignant soft tissue tumors by Xinpeng Dai, Haiyong Lu, Xinying Wang, Bingxin Zhao, Zongjie Liu, Tao Sun, Feng Gao, Peng Xie, Hong Yu, Xin Sui

Subjects: “…deep learning…”
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Fully automatic HER2 tissue segmentation for interpretable HER2 scoring by Mathias Öttl, Jana Steenpass, Frauke Wilm, Jingna Qiu, Matthias Rübner, Corinna Lang-Schwarz, Cecilia Taverna, Francesca Tava, Arndt Hartmann, Hanna Huebner, Matthias W. Beckmann, Peter A. Fasching, Andreas Maier, Ramona Erber, Katharina Breininger

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Utility of artificial intelligence in a binary classification of soft tissue tumors by Jing Di, Caylin Hickey, Cody Bumgardner, Mustafa Yousif, Mauricio Zapata, Therese Bocklage, Bonnie Balzer, Marilyn M. Bui, Jerad M. Gardner, Liron Pantanowitz, Shadi A. Qasem

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Exploring novel molecular mechanisms underlying recurrent pregnancy loss in decidual tissues by Hui Ding, Yajie Gao, Yuan Gao, Yulu Chen, Ruimin Liu, Caili Wang, Yuqing Gao

“…Machine learning (LASSO, SVM-RFE, RF) identified optimal feature genes, which were validated via real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC). …”
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An in vivo systemic massively parallel platform for deciphering animal tissue-specific regulatory function by Ashley R. Brown, Ashley R. Brown, Grant A. Fox, Grant A. Fox, Irene M. Kaplow, Irene M. Kaplow, Irene M. Kaplow, Alyssa J. Lawler, Alyssa J. Lawler, BaDoi N. Phan, BaDoi N. Phan, BaDoi N. Phan, Lahari Gadey, Lahari Gadey, Morgan E. Wirthlin, Morgan E. Wirthlin, Easwaran Ramamurthy, Easwaran Ramamurthy, Gemma E. May, Ziheng Chen, Qiao Su, Qiao Su, C. Joel McManus, Robert van de Weerd, Robert van de Weerd, Andreas R. Pfenning, Andreas R. Pfenning, Andreas R. Pfenning

“…Here, we introduce a systemic MPRA (sysMPRA) to address these challenges through systemic intravenous AAV viral delivery.Results: We demonstrate successful transduction of the MPRA library into diverse mouse tissues, efficiently identifying tissue specificity in candidate enhancers and aligning well with predictions from machine learning models. …”
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CMImpute: cross-species and tissue imputation of species-level DNA methylation samples across mammalian species by Emily Maciejewski, Steve Horvath, Jason Ernst

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