Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis by Chao Tan, Hao Liu, Zhen Zhang, Xinyu Liu, Yinquan Ai, Xiumin Wu, Enlin Jian, Yongyan Song, Jin Yang

“…AlphaFold2-related research received the highest citations, reflecting the transformative role of deep learning in protein structure prediction. Thematic clustering revealed key research foci, including deep learning algorithms, protein–protein interaction prediction, and integrative multi-omics analysis. …”
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Multi-level User Interest and Multi-intent Fusion for Next Basket Recommendation by WEI Chuyuan, YUAN Baojie, WANG Changdong

“…Finally, user interests and intents from different levels are fused in the predict layer for the next basket of predictions. …”
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Deep learning radiomics based on MRI for differentiating tongue cancer T - staging by Zhaoyi Lu, Bowen Zhu, Hang Ling, Xi Chen

“…Abstract Objective To develop a deep learning-based MRI model for predicting tongue cancer T-stage. Methods This retrospective study analyzed clinical and MRI data from 579 tongue cancer patients (Xiangya Cancer Hospital and Jiangsu Province Hospital). …”
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Enhancing Yield Estimation and Field Zoning Accuracy in Precision Agriculture Using Solar-Powered Drone-Based Remote Sensing by Abbas Haider Mohammed, Obaid Mohammed Kadhim, Vittalaiah A.

“…The system processes this data using advanced machine learning algorithms to forecast crop yields and generate detailed field zoning maps, enabling optimized resource allocation and improved farm management. …”
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Study on the interaction characteristics between pot seedling and planter based on hanging cup transplanter by Hongbin Bai, Fandi Zeng, Qiang Su, Ji Cui, Xuying Li

“…Comparative analysis reveals that the GA-BP algorithm demonstrates superior performance in ensuring model accuracy and stability, exhibiting better fitting performance with relative error rates between target and predicted values ranging from 2.25 to 10.54%. …”
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Dengue Contingency Planning: From Research to Policy and Practice. by Silvia Runge-Ranzinger, Axel Kroeger, Piero Olliaro, Philip J McCall, Gustavo Sánchez Tejeda, Linda S Lloyd, Lokman Hakim, Leigh R Bowman, Olaf Horstick, Giovanini Coelho

“…Additionally, a computer-assisted early warning system, which enables countries to identify and respond to context-specific variables that predict forthcoming dengue outbreaks, has been developed.…”
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Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma by LI Chaoqun, ZHENG Hongliang, HUANG Ping

“…Results‍　‍Our 9-CARGs prediction model for ESCC prognosis was constructed. …”
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Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review by Dhekre Saber Saleh, Mohd Shahizan Othman

“…By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. …”
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Screening and validation of diagnostic markers for keloids via bioinformatics analysis by Ze Wang, Bo Hu, Wenfei Li, Tengxiao Ma, Lei Li

“…Drug small molecules and compounds were predicted online, and molecular docking was performed. …”
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Machine learning insights into scapular stabilization for alleviating shoulder pain in college students by Omar M. Mabrouk, Doaa A. Abdel Hady, Tarek Abd El-Hafeez

“…This study investigates the prediction of the impact of scapular stability exercises in treating non-specific shoulder pain, leveraging advanced machine learning techniques for comprehensive evaluation and analysis. …”
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Analysis of mid-infrared spectrum characteristics of sandstone with different acidification degrees based on fusion model by Lu Chen, Longfei Chang, Huiqing Lian, En Wang, Bixing Zhang, Jia Kang

“…Subsequently, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) algorithms were compared, and a fusion model of mid-infrared spectral prediction models for red sandstone samples with varying degrees of acidification was established. …”
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ENDOCRINE PANCREATIC FUNCTION IN ACUTE PANCREATITIS by P. V. Novokhatny

“…Serological tests of pancreatic polypeptide promising for early diagnosis and prediction of the outcome of acute pancreatitis.…”
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RSWD-YOLO: A Walnut Detection Method Based on UAV Remote Sensing Images by Yansong Wang, Xuanxi Yang, Haoyu Wang, Huihua Wang, Zaiqing Chen, Lijun Yun

“…Accurate walnut yield prediction is crucial for the development of the walnut industry. …”
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Synthetic Data-Enhanced Classification of Prevalent Osteoporotic Fractures Using Dual-Energy X-Ray Absorptiometry-Based Geometric and Material Parameters by Luca Quagliato, Jiin Seo, Jiheun Hong, Taeyong Lee, Yoon-Sok Chung

“…To model the association of the bone’s current health status with prevalent FXs, three prediction algorithms—extreme gradient boosting (XGB), support vector machine, and multilayer perceptron—were trained using two-dimensional dual-energy X-ray absorptiometry (2D-DXA) analysis results and subsequently benchmarked. …”
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Klasifikasi Multilabel Pada Gaya Belajar Siswa Sekolah Dasar Menggunakan Algoritma Machine Learning by I Kadek Nicko Ananda, Ni Putu Novita Puspa Dewi, Ni Wayan Marti, Luh Joni Erawati Dewi

“…The machine learning algorithms used to build the model are Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). …”
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Integrated network toxicology, machine learning and molecular docking reveal the mechanism of benzopyrene-induced periodontitis by Wen Wenjie, Li Rui, Zhuo Pengpeng, Deng Chao, Zhang Donglin

“…Data from SwissTargetPrediction, CTD databases, and GEO datasets were analyzed to identify potential targets. …”
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Explore potential immune-related targets of leeches in the treatment of type 2 diabetes based on network pharmacology and machine learning by Tairan Hu, Zhaohui Fang

“…Finally, we employed LASSO regression, SVM-RFE, XGBoost, and random forest algorithms to further predict potential targets, followed by validation through molecular docking.ResultsLeeches may influence cellular immunity by modulating immune receptor activity, particularly through the activation of RGS10, CAPS2, and OPA1, thereby impacting the pathology of Type 2 Diabetes Mellitus (T2DM).DiscussionHowever, it is important to note that our results lack experimental validation; therefore, further research is warranted to substantiate these findings.…”
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Deepmol: an automated machine and deep learning framework for computational chemistry by João Correia, João Capela, Miguel Rocha

“…Despite its potential to revolutionize the field, researchers are often encumbered by obstacles, such as the complexity of selecting optimal algorithms, the automation of data pre-processing steps, the necessity for adaptive feature engineering, and the assurance of model performance consistency across different datasets. …”
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Identification of developmental and reproductive toxicity of biocides in consumer products using ToxCast bioassays data and machine learning models by Donghyeon Kim, Siyeol Ahn, Jinhee Choi

“…This study suggested the potential of ToxCast bioassays and machine learning models in predicting DART potential, offering a promising approach to address data-gap in consumer product safety.…”
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A multivariate model incorporating subharmonic measurements for evaluating vocal roughness by Itsuki Kitayama, Kiyohito Hosokawa, Shinobu Iwaki, Misao Yoshida, Akira Miyauchi, Kenji Aruga, Takanari Kawabe, Toshihiro Kishikawa, Hidenori Tanaka, Takeshi Tsuda, Takashi Sato, Yukinori Takenaka, Makoto Ogawa, Hidenori Inohara

“…Furthermore, we introduce the acoustic roughness index (ARI), a predictive acoustic model that integrates these parameters with existing acoustic parameters. …”
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