Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction. by Meng Wang, Jonathan Patsenker, Henry Li, Yuval Kluger, Steven H Kleinstein

“…We perform supervised fine-tuning on four pre-trained antibody language models to predict specificity to these antigens and demonstrate that fine-tuned language model classifiers exhibit enhanced predictive accuracy compared to classifiers trained on pre-trained model embeddings. …”
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MATHEMATICAL MODELS PREDICTING LEUKOPENIA AND NEUTROPENIA IN PATIENTS WITH CHRONIC HEPATITIS C IN THE BACKGROUND INTERFERONCONTAINING SCHEMES by I. G. Bakulin, N. Kh. Dianova, Yu. G. Sandler, M. Yu. Prostov

“…Prognostic criteria were identified, indicating the possible development  of the LP and NP expressed during treatment with interferon: female  gender,  low initial load, TT-genotype of IL-28B, the  initial level of white  blood cells and neutrophils  below 5,7×109/L and 3,4×109/L, respectively. Mathematical  models predicting the onset of LP and NP, formalized in the form of decision trees were also constructed. …”
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Risk factors and clinical prediction models for osteoporosis in pre-dialysis chronic kidney disease patients by Chaoying Kuang, Jingjie Shang, Mingming Ma, Shengling Huang, Bing Yan, Yuzhen Zhong, Baozhang Guan, Jian Gong, Fanna Liu, Liangmei Chen

“…The nomogram clinical prediction models we constructed may aid in the rapid screening of patients at high risk of osteoporosis.…”
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Determinants and risk prediction models for frailty among community-living older adults in eastern China by Lin Qi, Lin Qi, Jianyu Liu, Xuhui Song, Xinle Wang, Mengmeng Yang, Xinyi Cao, Yan He

“…ObjectiveThe purpose of this study is to develop predictive models for frailty risk among community-dwelling older adults in eastern China using machine learning techniques. …”
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Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms. by Huu Nam Nguyen, Quoc Thanh Tran, Canh Tung Ngo, Duc Dam Nguyen, Van Quan Tran

“…The results show that the CatBoost model emerges as the frontrunner in predicting solar energy, with training values of R2 value of 0.608, RMSE of 4.478 W and MAE of 3.367 W and the testing value is R2 of 0.46, RMSE of 4.748 W and MAE of 3.583 W. …”
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Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy by Haofang Zhang, Changbao Xu, Chenge Hu, Yunlai Xue, Daoke Yao, Yifan Hu, Ankang Wu, Miao Dai, Hang Ye

“…Our study aimed to construct a machine learning algorithm predictive model to predict the risk of fungal infection following F-URL. …”
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Prediction of sleep disorders using Novel decision support neutrosophic based machine learning models by Nihar Ranjan Panda, Surapati Paramanik, Prasanta Kumar Raut, Ruchi Bhuyan

“…This study introduces a novel decision support system utilizing a neutrosophic machine learning prediction model to enhance the accuracy and reliability of sleep disorder diagnosis. …”
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Evaluating the accuracy of models using routinely collected herd data for prediction of on-farm lameness prevalence by Conor Barry, Esben Østergaard Eriksen, Kristian Ellingsen-Dalskau, Christoph Winckler, Nicholas J. Bell, Camilla Kielland

“…The accuracy of both models was assessed in terms of their concordance r and prediction errors. …”
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Development of Advanced Machine Learning Models for Predicting CO₂ Solubility in Brine by Xuejia Du, Ganesh C. Thakur

“…This study explores the application of advanced machine learning (ML) models to predict CO₂ solubility in NaCl brine, a critical parameter for effective carbon capture, utilization, and storage (CCUS). …”
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Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. by Caroline Colijn, Aaron Brandes, Jeremy Zucker, Desmond S Lun, Brian Weiner, Maha R Farhat, Tan-Yun Cheng, D Branch Moody, Megan Murray, James E Galagan

“…In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. …”
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Predicting Wind Turbine Blade Tip Deformation With Long Short‐Term Memory (LSTM) Models by Shubham Baisthakur, Breiffni Fitzgerald

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Improving soil pH prediction and mapping using anthropogenic variables and machine learning models by Daocheng Li, Erlong Xiao, Yingxin Xia, Xingyu Liang, Mengxin Guo, Lixin Ning, Jun Yan

“…This study evaluates the impact of anthropogenic activities on soil pH prediction in China's Huang-Huai-Hai Plain using four machine learning models (RF, LightGBM, XGBoost, SVM). …”
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Predicting Crude Oil Prices During a Pandemic: A Comparison of Arima and Garch Models by Mohammad Imdadul Haque, Abdul Rahman Shaik

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Leveraging deep neural network and language models for predicting long-term hospitalization risk in schizophrenia by Yihang Bao, Wanying Wang, Zhe Liu, Weidi Wang, Xue Zhao, Shunying Yu, Guan Ning Lin

“…By utilizing multimodal features, our deep learning model achieved a classification accuracy of 0.81 and an AUC of 0.9. …”
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Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction by Chenxi Wang, Weiwei Zhang, Ming Ni, Qiong Wang, Chang Liu, Linbin Dai, Mengguo Zhang, Yong Shen, Feng Gao

“…Dementia related brain regions, such as the medial temporal lobe, were identified by our model. Finally, amyloid plaque prediction model was trained to predict amyloid plaque, and achieved an AUC about 0.8 for dementia patients. …”
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Predicting police and military violence: evidence from Colombia and Mexico using machine learning models by Juan David Gelvez

“…This article proposes the use of machine learning models to predict armed forces violence at the municipality level. …”
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Learning models for predicting pavement friction based on non-contact texture measurements: Comparative assessment by Xiuquan Lin, You Zhan, Zilong Nie, Joshua Qiang Li, Xinyu Zhu, Allen A. Zhang

“…By assessing the importance of the 38 parameter variables, the most critical 21 variables were selected for model development. Test results demonstrate that the GBDT model exhibits the best predictive performance, with an explanatory capability of 87.4​% for road friction performance. …”
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Early molecular changes predict cancer cachexia in LKB1‐deleted mouse models of NSCLC by Gloriana Ndembe, Andrea David Re Cecconi, Federica Palo, Dorina Belotti, Laura Sala, Selena Foroni, Eugenio Scanziani, Rosanna Piccirillo, Massimo Broggini, Mirko Marabese

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Technology for Improving the Accuracy of Predicting the Position and Speed of Human Movement Based on Machine Learning Models by Artem Obukhov, Denis Dedov, Andrey Volkov, Maksim Rybachok

“…For speed prediction, the linear regression (LR) model showed the best results when the analysed window length was 10 frames. …”
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Random Forest versus Support Vector Machine Models’ Applicability for Predicting Beam Shear Strength by Hayder Riyadh Mohammed Mohammed, Sumarni Ismail

“…Nine input combinations were constructed based on the statistical correlation to be supplied for the proposed predictive model. The prediction accuracy of the RF model was validated against the Support Vector Machine (SVM), and several other empirical formulations have been adopted in the literature. …”
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