Gold nanobiosensors and Machine Learning: Pioneering breakthroughs in precision breast cancer detection by Soheil Sadr, Ashkan Hajjafari, Abbas Rahdar, Sadanand Pandey, Parian Poorjafari Jafroodi, Narges Lotfalizadeh, Mahdi Soroushianfar, Shahla Salimpour Kavasebi, Zelal Kharaba, Sonia Fathi-karkan, Hassan Borji

“…Gold nanobiosensors have been significantly developed through innovations like signal amplification and surface functionalization, integrated with the use of advanced imaging techniques. …”
Get full text

Adjoint‐Based Online Learning of Two‐Layer Quasi‐Geostrophic Baroclinic Turbulence by F. E. Yan, H. Frezat, J. Le Sommer, J. Mak, K. Otness

“…Other details relating to online training, such as window size, machine learning model set up and designs of the loss functions are detailed to aid in further explorations of the online training methodology for Earth System Modeling.…”
Get full text

Triple-effect correction for Cell Painting data with contrastive and domain-adversarial learning by Chengwei Yan, Yu Zhang, Jiuxin Feng, Heyang Hua, Zhihan Ruan, Zhen Li, Siyu Li, Chaoyang Yan, Pingjing Li, Jian Liu, Shengquan Chen

“…Moreover, cpDistiller effectively captures system-level phenotypic responses to genetic perturbations and reliably infers gene functions and interactions both when combined with scRNA-seq data and independently. cpDistiller also demonstrates promising capability for identifying gene and compound targets, highlighting its potential utility in drug discovery and broader biological research.…”
Get full text

Discriminative learning of receptive fields from responses to non-Gaussian stimulus ensembles. by Arne F Meyer, Jan-Philipp Diepenbrock, Max F K Happel, Frank W Ohl, Jörn Anemüller

“…Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. …”
Get full text

The nursing process and total health cost variability: an analysis using machine learning by Maria Consuelo Company-Sancho, Victor M. González-Chordá, Maria Isabel Orts-Cortés

“…Abstract Aims To find out whether the information that the nursing process provides (functional patterns and the NANDA-NIC-NOC taxonomy), presented through clinical histories, influences predictions of total healthcare costs. …”
Get full text

Data-related risks for the use of machine learning in retail customer demand forecasting by Lee-Ann Pietersen, Riaan J. Rudman

“…These risks link to each stage and component of the machine learning system development life cycle. Practical implications: The risks can be used by internal and external auditors, as well as those charged with governance and other management functions within an organisation, to identify and evaluate risks arising from the use of machine learning within their organisation. …”
Get full text

SynergyBug: A deep learning approach to autonomous debugging and code remediation by Hong Chen

Get full text

Ion channel classification through machine learning and protein language model embeddings by Ghazikhani Hamed, Butler Gregory

“…Ion channels are critical membrane proteins that regulate ion flux across cellular membranes, influencing numerous biological functions. The resource-intensive nature of traditional wet lab experiments for ion channel identification has led to an increasing emphasis on computational techniques. …”
Get full text

Comparing statistical learning methods for complex trait prediction from gene expression. by Noah Klimkowski Arango, Fabio Morgante

“…Here, we used data from the Drosophila Genetic Reference Panel (DGRP) to compare the ability of several existing statistical learning methods to predict starvation resistance and startle response from gene expression in the two sexes separately. …”
Get full text

LEARNING ENGLISH THROUGH THE INTEGRATION OF STUDENTS’ EDUCATIONAL-COGNITIVE AND SELF-EDUCATIONAL ACTIVITIES by Liliya A. Dmytruk

Get full text

Learning optimal image representations through noise injection for fine-grained search by Vidit Kumar, Vikas Tripathi, Bhaskar Pant, Manoj Diwakar, Prabhishek Singh, Anchit Bijalwan

“…This embedding is usually learned by defining loss functions based on local structure like triplet loss. …”
Get full text

Deep learning-based research on fault warning for marine dual fuel engines by Lingkai Meng, Huibing Gan, Haisheng Liu, Daoyi Lu

“…The model integrated convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM) networks, and Kolmogorov-Arnold networks (KAN) to perform feature extraction from multi-dimensional time series data, autonomously identify temporal patterns within the data, and directly learn parameterized nonlinear activation functions, respectively. …”
Get full text

Development of Machine Learning Models to Categorize Life Satisfaction in Older Adults in Korea by Suyeong Bae, Mi Jung Lee, Ickpyo Hong

“…Objectives This study aimed to identify factors associated with life satisfaction by developing machine learning (ML) models to predict life satisfaction in older adults living alone. …”
Get full text

Graph-based machine learning model for weight prediction in protein–protein networks by Hajer Akid, Kirsley Chennen, Gabriel Frey, Julie Thompson, Mounir Ben Ayed, Nicolas Lachiche

Get full text

Field inversion and machine learning based on the Rubber–Band Spalart–Allmaras Model by Chenyu Wu, Yufei Zhang

“…Machine learning (ML) techniques have emerged as powerful tools for improving the predictive capabilities of Reynolds-averaged Navier–Stokes (RANS) turbulence models in separated flows. …”
Get full text

Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides by Heqian Zhang, Yihan Wang, Yanran Zhu, Pengtao Huang, Qiandi Gao, Xiaojie Li, Zhaoying Chen, Yu Liu, Jiakun Jiang, Yuan Gao, Jiaquan Huang, Zhiwei Qin

“…Objectives: In this study, the lipopolysaccharide-binding domain (LBD) was identified through machine learning-guided directed evolution, which acts as a functional domain of the anti-lipopolysaccharide factor family of AMPs identified from Marsupenaeus japonicus. …”
Get full text

Computational modelling of immunological mechanisms: From statistical approaches to interpretable machine learning by María Rodríguez Martínez, Matteo Barberis, Anna Niarakis

“…This large amount of data has facilitated the emergence of statistical and machine-learning models focused on unravelling the intricate complexities of the immune system. …”
Get full text

Exploring pesticide risk in autism via integrative machine learning and network toxicology by Ling Qi, Jingran Yang, Qiao Niu, Jianan Li

“…This study aims to investigate the pathogenic mechanisms of ASD and identify potential causative pesticides by integrating bioinformatics, machine learning, network toxicology, and molecular docking approaches. …”
Get full text

A Comparative Study of Machine Learning Models for Accurate E-Waste Prediction by Mohammed Algafri, Mohammed Sayad, Mohammad A.M. Abdel-Aal, Ahmed M. Attia

“…This study evaluates six Machine Learning (ML) models, Linear Regression, Regression Tree, Support Vector Regression, Ensemble Regression, Gaussian Process Regression (GPR), and Artificial Neural Networks, for e-waste forecasting. …”
Get full text

Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency by Bruce Pyenson, Maggie Alston, Jeffrey Gomberg, Feng Han, Nikhil Khandelwal, Motoharu Dei, Monica Son, Jaime Vora

“… # Methods A machine learning algorithm was developed in Scikit-learn, a Python module. …”
Get full text