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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Guidelines for releasing a variant effect predictor by Benjamin J. Livesey, Mihaly Badonyi, Mafalda Dias, Jonathan Frazer, Sushant Kumar, Kresten Lindorff-Larsen, David M. McCandlish, Rose Orenbuch, Courtney A. Shearer, Lara Muffley, Julia Foreman, Andrew M. Glazer, Ben Lehner, Debora S. Marks, Frederick P. Roth, Alan F. Rubin, Lea M. Starita, Joseph A. Marsh

“…Many different VEPs have been released, and there is tremendous variability in their underlying algorithms, outputs, and the ways in which the methodologies and predictions are shared. …”
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Assessing the association between ADHD and brain maturation in late childhood and emotion regulation in early adolescence by Kristóf Ágrez, Pál Vakli, Béla Weiss, Zoltán Vidnyánszky, Nóra Bunford

“…Whether the difference between an individual’s brain age predicted by machine-learning algorithms trained on neuroimaging data and that individual’s chronological age, i.e. brain-predicted age difference (brain-PAD) predicts differences in emotion regulation, and whether ADHD problems add to this prediction is unknown. …”
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Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation by Guiling Wu, Guiling Wu, Sihui Wu, Sihui Wu, Tian Xiong, Tian Xiong, Tian Xiong, You Yao, You Yao, Yu Qiu, Yu Qiu, Yu Qiu, Liheng Meng, Cuihong Chen, Xi Yang, Xi Yang, Xi Yang, Xinghuan Liang, Yingfen Qin

“…Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. …”
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Machine-Learning-Assisted Identification of Steam Channeling after Cyclic Steam Stimulation in Heavy-Oil Reservoirs by Yu Li, Huiqing Liu, Peng Jiao, Qing Wang, Dong Liu, Liangyu Ma, Zhipeng Wang, Hao Peng

“…In this work, a machine-learning-assisted identification model, based on a random-forest ensemble algorithm, is developed to predict the occurrence of steam channeling during steam huff-and-puff processes. …”
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Machine Learning G-Code Optimization by Héctor Lasluisa-Naranjo, David Rivas-Lalaleo, Joaquín Vaquero-López, Christian Cruz-Moposita

“…This research applies the K-means machine learning clustering algorithm to optimize G-code parameters such as extruder and heated bed temperature, deposition speed, and flow control. …”
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Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta by Vaisali Chandrasekar, Mohammed Yusuf Ansari, Ajay Vikram Singh, Shahab Uddin, Kirthi S. Prabhu, Sagnika Dash, Souhaila Al Khodor, Annalisa Terranegra, Matteo Avella, Sarada Prasad Dakua

“…Owing to limited drug testing possibilities in pregnant population, the development of computational algorithms is crucial to predict the fate of drugs in the placental barrier; it could serve as an alternative to animal testing. …”
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Global Methane Retrieval, Monitoring, and Quantification in Hotspot Regions Based on AHSI/ZY-1 Satellite by Tong Lu, Zhengqiang Li, Cheng Fan, Zhuo He, Xinran Jiang, Ying Zhang, Yuanyuan Gao, Yundong Xuan, Gerrit de Leeuw

“…The results show that the improved MF algorithm effectively suppresses noise in retrieval results over both land and ocean surfaces, enhancing algorithm robustness. …”
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