HAPIR: a refined Hallmark gene set-based machine learning approach for predicting immunotherapy response in cancer patients by Mengqin Yuan, Haizhou Liu, Yu-e Huang, Fei Hou, Lihong Wang, Quan Wang, Wei Jiang

Get full text

Maven: a multimodal foundation model for supernova science by Gemma Zhang, Thomas Helfer, Alexander T Gagliano, Siddharth Mishra-Sharma, V Ashley Villar

Get full text

Machine learning-guided evolution of pyrrolysyl-tRNA synthetase for improved incorporation efficiency of diverse noncanonical amino acids by Qunfeng Zhang, Ling Jiang, Yadan Niu, Yujie Li, Wanyi Chen, Jingxi Cheng, Haote Ding, Binbin Chen, Ke Liu, Jiawen Cao, Junli Wang, Shilin Ye, Lirong Yang, Jianping Wu, Gang Xu, Jianping Lin, Haoran Yu

“…This paper presents improved PylRS variants and a machine learning framework for optimizing the enzyme activity.…”
Get full text

A robust machine learning approach to predicting remission and stratifying risk in rheumatoid arthritis patients treated with bDMARDs by Fatemeh Salehi, Emmanuelle Salin, Benjamin Smarr, Sara Bayat, Arnd Kleyer, Georg Schett, Ruth Fritsch-Stork, Bjoern M. Eskofier

“…We evaluated multiple machine learning models, AdaBoost, Random Forest, XGBoost, and Support Vector Machines, using data from Austrian RA patients. …”
Get full text

Identification of matrix stiffness-related molecular subtypes in HCC via integrating multi-omics analysis and machine learning algorithms by Hanqi Li, Jiayi Zhang, Yu Shi, Huanhuan Wang, Ruida Yang, Shaobo Wu, Yue Li, Xue Yang, Qingguang Liu, Liankang Sun

“…A matrix stiffness-related signature comprising 57 genes was constructed by evaluating 101 machine learning algorithm combinations. PPARG, the key gene with the greatest contribution to the model, was selected for validation. …”
Get full text

Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis by Jong-Ho Kim, Hye-Sook Kim, Jong-Hee Sohn, Sung-Mi Hwang, Jae-Jun Lee, Young-Suk Kwon

“…Utilizing statistical methods and machine learning models, these studies aim to analyze and predict these relationships to develop effective approaches for headache management and prevention. …”
Get full text

Machine Learning-Enhanced Discrimination of Gamma-Ray and Hadron Events Using Temporal Features: An ASTRI Mini-Array Analysis by Valentina La Parola, Giancarlo Cusumano, Saverio Lombardi, Antonio Alessio Compagnino, Antonino La Barbera, Antonio Tutone, Antonio Pagliaro

“…The model incorporates feature importance analysis to select the most discriminating temporal parameters from a comprehensive set of time-based features. …”
Get full text

Machine Learning-Based Non-Invasive Prediction of Metabolic Dysfunction-Associated Steatohepatitis in Obese Patients: A Retrospective Study by Jie Chen, Bo Zhang, Yong Cheng, Yuanchen Jia, Biao Zhou

“…<b>Objectives</b>: We aimed to develop and validate machine learning (ML) models that integrate clinical and laboratory data for the non-invasive prediction of metabolic dysfunction-associated steatohepatitis (MASH) in an obese population. …”
Get full text

Physics-enhanced machine learning for predicting strength of high-carbon chromium steel during thermomechanical processing and spheroidizing annealing by Changqing Shu, Shasha Zhang, Peiheng Ding, Yaxin Sun, Xuewei Tao, Xiaolin Zhu, Qiuhao Gu, Liukai Hua, Song Xue, Zhengjun Yao

“…Understanding the microstructural evolution during thermomechanical processing and spheroidizing annealing is critical for optimizing mechanical properties. However, conventional models struggle to capture the complex interactions between process parameters, microstructure, and strength. …”
Get full text

Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting by Vijayalakshmi S, John A, Sunder R, Senthilkumar Mohan, Sweta Bhattacharya, Rajesh Kaluri, Guang Feng, Usman Tariq

“…The selected features design the objective of the problem model. The proposed model is implemented on the WBCD and WDBC breast cancer data sets publicly available from the UCI machine learning data repository. …”
Get full text

Optimizing Performance of AdaBoost Algorithm through Undersampling and Hyperparameter Tuning on CICIoT 2023 Dataset by Sahrul Fahrezi Fahrezi, Adhitya Nugraha, Ardytha Luthfiarta, Nauval Dwi Primadya

“…This optimization underscores the significance of finetuning parameters in machine learning algorithms to enhance the effectiveness of cybersecurity measures for IoT devices. …”
Get full text

Design and evaluation of self-tuning optimal control for vibration suppression of magnetorheological semi-active suspension by Gang Li, Yu Gan, Qianjie Liu, Miaojin Xie, Qingsheng Huang, Liping Zeng

Get full text

CTAB modified SnO₂ PEDOT PSS heterojunction humidity sensor with enhanced sensitivity stability and machine learning evaluation by Poundoss Chellamuthu, Kirubaveni Savarimuthu, M Gulam Nabi Alsath, R. Krishnamoorthy, Yuvaraj T, Feras Alnaimat, Mohammad Shabaz

“…Furthermore, to validate real-time application feasibility, machine learning (ML) algorithms were implemented to model and predict sensor behavior. …”
Get full text

Design of an Improved Method for Task Scheduling Using Proximal Policy Optimization and Graph Neural Networks by Nemilidinea Anantharami Reddy, B.V. Gokulnath

“…We provide an integrated scheduling framework that integrates Proximal Policy Optimization, Graph Neural Networks, hybrid rule-based and machine learning techniques, and synthetic data generation with Generative Adversarial Networks in this paper. …”
Get full text

Explainable Artificial Intelligence (XAI) for Flood Susceptibility Assessment in Seoul: Leveraging Evolutionary and Bayesian AutoML Optimization by Kounghoon Nam, Youngkyu Lee, Sungsu Lee, Sungyoon Kim, Shuai Zhang

“…These models were further fine-tuned using Bayesian optimization via Optuna. …”
Get full text

Optimization of Energy Consumption of a Microwave-assisted Continuous Pilot Plant for the Processing of Shelled Almonds by Antonia Tamborrino, Alessandro Leone, Roberto Romaniello, Claudio Perone, Cosimo Dellisanti, Domenico Tarantino, Biagio Bianchi, Antonio Berardi

“…This article introduces a numerical model for the optimization of electricity consumption of the prototype built for the disinfestation of shelled almonds. …”
Get full text

Optimizing chemotherapeutic targets in non-small cell lung cancer with transfer learning for precision medicine. by Varun Malik, Ruchi Mittal, Deepali Gupta, Sapna Juneja, Khalid Mohiuddin, Swati Kumari

“…Davis, KIBA, and Binding-DB are examples of benchmark datasets that are used to validate the proposed model. Results exhibit that the MRO+DTransL model outflanks existing cutting edge models. …”
Get full text

Integrating Multi-Layer Perceptron Regression with Innovative Optimization for Accurate Building Cooling Load Prediction by Subhiya Zeynalli, Baharak Eslami

“…This study explores the application of a machine learning model called Multi-Layer Perceptron Regression (MLPR) for building cooling demand prediction. …”
Get full text

Modelling and Forecasting Cash Withdrawals in the Bank by Jarosław Bielak, Andrzej Burda, Mieczysław Kowerski, Krzysztof Pancerz

“… The goal of the paper is searching for the optimal forecasting model estimating the amount of cash withdrawn daily by the customers of one of the Polish banks by means of statistical and machine learning methods. …”
Get full text

Process of Solving Multi-Response Optimization Problems Using a Novel Data Envelopment Analysis Variant-Taguchi Method by Narong Wichapa, Narathip Pawaree, Pariwat Nasawat, Prawach Chourwong, Anucha Sriburum, Wanrop Khanthirat

“…Taguchi uses efficiency scores from the DEAV model to enable optimal parameter determination through Taguchi optimization. …”
Get full text