Statistical Machine Learning Models for Biomedical Science

Additional Titles: Statistical learning models have become essential instruments in the field of biomedical science, providing strong approaches to analyzing intricate information, identifying significant patterns, and aiding in predictive modeling. This study explores the various types of biomedical, ranging from clinical and pathological data to medical imaging and electronic health records, highlighting the unique challenges and opportunities associated with each domain. For example, breast cancer prediction research has seen significant development, yet a focused effort is needed to tailor frameworks to patient-specific risk factors. This study fills this gap by analyzing anthropometric data from women's routine blood analyses using multivariate statistical techniques. Based on these results, we propose the advanced statistical learning model: Multiple Directive Feature Selection and Prediction Strategy to find features for the presence of breast cancer in patients. Using a non-linear dimension reduction procedure with an ensemble Random Forest shows better accuracy (77%) compared to other models. To validate the breast cancer risk factors for a specific patient, we develop explainable AI with Shapley values. It shows different risk factors are responsible for specific patients and allows us to investigate a model's decision-making with risk factors at both the local level. In another aspect, we address challenges in network analysis, particularly in graph-based clustering, crucial for various fields such as deep learning, computer vision, and social network analysis. We introduce an innovative algorithm aimed at improving clustering performance by optimizing the choice of parameters, notably the scale parameter σ in the Gaussian kernel. Our approach enhances the scalability and robustness of clustering methods, demonstrating improved performance across diverse datasets through simulations and validation of both synthetic and real-world data. By customizing the scale parameter, ou

Συγγραφείς: Trindade, Adao, Volchenkov, Dimitri, Howle, Victoria E., Allen, Linda J.S., Mostafa, G M Fahad Bin

Όροι ευρετηρίου: Multiple Directive Feature Selection and Prediction Strategy, Explainable AI, Periodic mean-reverting SDEs, Missing Value Imputation, Modified Graph-based Clustering, Novel Graph Convolution Network, Statistical Learning for Classification, Domain of applicability in QSAR modeling, Dissertation

Σύνδεσμος: https://hdl.handle.net/2346/100434

Integrated simulation of deep learning, computer vision and physical layer of UAV and ground vehicle networks

Συγγραφείς: Aldebaro Klautau, Ilan Correa, Felipe Bastos, Ingrid Nascimento, João Borges, Ailton Oliveira, Pedro Batista, Silvia Lins

Πηγή: Deep Learning and Its Applications for Vehicle Networks ISBN: 9781003190691

Integrated Pharmacophore, Docking, Machine Learning, and Molecular Dynamics Approach for the Discovery and Validation of LuxS Quorum-Sensing Inhibitors to Combat Antibiotic Resistance.

Συγγραφείς: Nawaz M; Department of Biosciences, COMSATS University Islamabad, Islamabad, Pakistan., Ahmed M; Department of Chemistry, Division of Science and Technology, University of Education, Lahore, Pakistan., Raheem K; Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, Nebraska, USA., Mughram MHA; Department of Pharmaceutical Chemistry, College of Pharmacy, King Khalid University, Abha, Saudi Arabia., Khan MJ; Department of Biosciences, COMSATS University Islamabad, Islamabad, Pakistan., Alzahrani KJ; Research Center of Basic Sciences, Engineering and High Altitude, Taif University, Taif, Saudi Arabia.; Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia., Muddassar M; Department of Biosciences, COMSATS University Islamabad, Islamabad, Pakistan.

Πηγή: ChemPlusChem [Chempluschem] 2026 Jun; Vol. 91 (6), pp. e70190.

Τύπος έκδοσης: Journal Article

Στοιχεία περιοδικού: Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 101580948 Publication Model: Print Cited Medium: Internet ISSN: 2192-6506 (Electronic) Linking ISSN: 21926506 NLM ISO Abbreviation: Chempluschem Subsets: MEDLINE

Ιατρικοί όροι (MeSH): Quorum Sensing*/drug effects , Carbon-Sulfur Lyases*/antagonists & inhibitors , Carbon-Sulfur Lyases*/metabolism , Bacterial Proteins*/antagonists & inhibitors , Bacterial Proteins*/metabolism , Anti-Bacterial Agents*/pharmacology , Anti-Bacterial Agents*/chemistry , Enzyme Inhibitors*/chemistry , Enzyme Inhibitors*/pharmacology , Molecular Dynamics Simulation* , Machine Learning*, Molecular Docking Simulation ; Pharmacophore ; Drug Discovery

Computer modeling to improve measurement of stroke volume with echocardiography.

Συγγραφείς: Murthi SB; School of Medicine, University of Maryland, Baltimore, USA. smurthi@som.umaryland.edu., Yang S; School of Medicine, University of Maryland, Baltimore, USA., Oliveri PP; School of Medicine, University of Maryland, Baltimore, USA., Safadi S; Departments of Nephrology and Pulmonary Critical Care , University of Minnesota, Minneapolis, USA., Fatima S; School of Medicine, University of Maryland, Baltimore, USA., Teeter W; School of Medicine, University of Maryland, Baltimore, USA.

Πηγή: Cardiovascular ultrasound [Cardiovasc Ultrasound] 2026 Jul 06; Vol. 24 (1). Date of Electronic Publication: 2026 Jul 06.

Τύπος έκδοσης: Journal Article; Observational Study

Στοιχεία περιοδικού: Publisher: BioMed Central Country of Publication: England NLM ID: 101159952 Publication Model: Electronic Cited Medium: Internet ISSN: 1476-7120 (Electronic) Linking ISSN: 14767120 NLM ISO Abbreviation: Cardiovasc Ultrasound Subsets: MEDLINE

Ιατρικοί όροι (MeSH): Echocardiography*/methods , Heart Ventricles*/diagnostic imaging , Heart Ventricles*/physiopathology , Image Interpretation, Computer-Assisted*/methods , Stroke Volume*/physiology , Computer Simulation* , Machine Learning*, Female ; Humans ; Male ; Middle Aged ; Prospective Studies ; Reproducibility of Results ; Aged

Virtual Try-On System

Συγγραφείς: Anagha Kadave, Sakshi Chavan, Komal Karande, Anuja Nalawade, Vishakha Salunkhe, Rachana Shewale

Θεματικοί όροι: Virtual try-on, image inpainting, semantic segmentation, stable diffusion, ipadapter, segformer, deep learning, computer vision, e-commerce, ai fashion technology, photorealistic synthesis, personalized shopping, transformer models, clothing simulation, visual try-on pipeline

Benchmarking Autonomous Driving Systems using a Modular and Scalable Simulation Environment

Συγγραφείς: Szilagyi, David, Benta, Kuderna-Iulian, Sacarea, Christan

Πηγή: 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI) ICTAI Tools with Artificial Intelligence (ICTAI), 2024 IEEE 36th International Conference on. :420-426 Oct, 2024

Relation: 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI)

Evolution of humanoid locomotion control.

Συγγραφείς: Gu, Yan, Shi, Guanya, Shi, Fan, Chang, I-Chia, Wang, Yen-Jen, Cheng, Qilong, Olkin, Zachary, Lopez-Sanchez, Ivan, Feng, Yunchu, Zhang, Jian, Ames, Aaron D., Su, Hao, Sreenath, Koushil

Πηγή: Science Robotics; 8/12/2026, Vol. 11 Issue 117, p1-19, 19p

Θεματικοί όροι: Robotics, Adaptive control systems, Reinforcement learning, Computer simulation, Human-robot interaction, Probabilistic generative models, Human locomotion

An Intelligent Virtual System Relocation Based Prediction Model for Optimal Resource Control in Vehicular Cloud

Συγγραφείς: Das, Sukanta, Ranjan Tripathy, Alok

Πηγή: 2022 International Conference on Machine Learning, Computer Systems and Security (MLCSS) MLCSS Machine Learning, Computer Systems and Security (MLCSS), 2022 International Conference on. :80-85 Aug, 2022

Relation: 2022 International Conference on Machine Learning, Computer Systems and Security (MLCSS)

Practical Simulations for Machine Learning : Using Synthetic Data for AI

Συγγραφείς: Paris Buttfield-Addison, Mars Buttfield-Addison, Tim Nugent, Jon Manning

Resource Type: eBook.

Θέματα: Artificial intelligence--Computer simulation, Machine learning--Computer simulation

Categories: COMPUTERS / Data Science / Machine Learning, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Computer Science, COMPUTERS / Machine Theory

Harnessing the Potential of Carotenoids for Cancer Therapy: An Integrated Machine Learning and MST Based Approach.

Συγγραφείς: Varghese R; School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India., Deb KS; School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India., Pal K; School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India., Jonnalagadda A; School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India., Cherukuri AK; School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India., Dawood M; Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Mainz, Germany., Boulos JC; Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Mainz, Germany., Efferth T; Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Mainz, Germany., Ramamoorthy S; School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Πηγή: Phytotherapy research : PTR [Phytother Res] 2025 Sep; Vol. 39 (9), pp. 4156-4170. Date of Electronic Publication: 2025 Aug 04.

Τύπος έκδοσης: Journal Article

Στοιχεία περιοδικού: Publisher: Wiley Country of Publication: England NLM ID: 8904486 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1099-1573 (Electronic) Linking ISSN: 0951418X NLM ISO Abbreviation: Phytother Res Subsets: MEDLINE

Ιατρικοί όροι (MeSH): Carotenoids*/pharmacology , Carotenoids*/chemistry , Neoplasms*/drug therapy , Receptor Protein-Tyrosine Kinases*/antagonists & inhibitors , Antineoplastic Agents, Phytogenic*/pharmacology , Machine Learning*, Protein Kinase Inhibitors/pharmacology ; Molecular Docking Simulation ; Humans

Data-driven model order reduction with surrogate elements for transient simulations.

Συγγραφείς: Franke, Markus, Krause, Tom Janis, Wagner, Marcus

Πηγή: Engineering Computations; 2026, Vol. 43 Issue 7, p2655-2680, 26p

Θεματικοί όροι: Finite element method, Degrees of freedom, Mathematical optimization, Artificial neural networks, Matrices (Mathematics), Reduced-order models, Dynamic simulation

Virtual patients inspired by multiomics predict the efficacy of an anti-IFNα mAb in cutaneous lupus

Συγγραφείς: Hurez, Vincent ^{1, 5, ∗}, Gauderat, Glenn ², Soret, Perrine ², Myers, Renee ¹, Dasika, Krishnakant ¹, Sheehan, Robert ¹, Friedrich, Christina ¹, Reed, Mike ¹, Laigle, Laurence ², Riquelme, Marta Alarcón ³, Aussy, Audrey ², Chadli, Loubna ², Hubert, Sandra ², Desvaux, Emiko ², Fouliard, Sylvain ², Moingeon, Philippe ^{2, 4, 6, ∗∗}

Πηγή: In iScience 21 February 2025 28(2)

A Review on Machine Learning and Bioinformatics to Study Biofouling in Marine Renewable Energy Devices: Modeling, Performance Prediction, and Maintenance Planning.

Συγγραφείς: Hasil, Shah Dad, Zahid, Zahid, Michailides, Constantine, Shi, Wei, Irshad, Feroz

Πηγή: Journal of Marine Science & Engineering; Mar2026, Vol. 14 Issue 6, p549, 23p

Θεματικοί όροι: Machine learning, Bioinformatics, Fouling, Computer simulation, Computerized maintenance management systems, Nucleic acids, Ocean energy resources, Digital twin

Field Validation of Nonlinear Dynamic Simulation for Plan-Irregular RC Buildings: Insights from the 2024 Hualien Earthquake.

Συγγραφείς: Chen, Ying-Chuan, Chao, Kuo-Hung, Sung, Yu-Chi, Lin, Jin-Sheng

Πηγή: Buildings (2075-5309); Sep2026, Vol. 16 Issue 17, p3421, 33p

Θεματικοί όροι: Earthquake damage, Reinforced concrete buildings, Dynamic simulation, Earthquakes, Structural analysis (Engineering)

Artificial Intelligence in Crime Scene Reconstruction: Using Machine Learning for Predictive Analysis and Scenario Simulation.

Συγγραφείς: Miah, Mohammed Nazmul Islam, Uddin, Joshim, Kakumani, Manohar

Πηγή: Frontiers in Computer Science & Artificial Intelligence; 2026, Vol. 5 Issue 8, p43-57, 15p

Θεματικοί όροι: Machine learning, Ensemble learning, Prediction models, Discrete event simulation, Artificial intelligence, Crime scene searches, Forensic sciences, Deep learning

Autonomous Vehicles: A Survey of Machine Learning, Computer Vision, and IoT Techniques with Cybersecurity Considerations

Συγγραφείς: Dr. Shailesh Kantilal Patel

Πηγή: International Journal for Research in Applied Science and Engineering Technology. 13:393-402

Comprehensive Assessment of Orbital Robotics, Space Application Simulation/Machine Learning, and Methods of Hardware in the Loop Validation

Συγγραφείς: Peterson, Marco, Du, Minzhen, Springle, Bryant, Black, Jonathan

Πηγή: 2022 IEEE Aerospace Conference (AERO) Aerospace Conference (AERO), 2022 IEEE. :1-13 Mar, 2022

Relation: 2022 IEEE Aerospace Conference (AERO)

Direct evidence of non-acoustic collective modes in dynamics of molten Carbon.

Συγγραφείς: Bryk, Taras, Ruocco, Giancarlo, Wax, Jean-François, Jakse, Noël

Πηγή: Communications Physics; 4/1/2026, Vol. 9 Issue 1, p1-8, 8p

Θεματικοί όροι: Molecular dynamics, High temperatures, Quasiparticles, Density functionals, Liquids

Towards resilience in Industry 5.0: application of system dynamics in matrix-structured manufacturing system.

Συγγραφείς: Wang, Miao, Tong, Yifei, Zhuang, Cunbo

Πηγή: International Journal of Production Research; Jul2026, Vol. 64 Issue 13, p5290-5314, 25p

Θεματικοί όροι: System dynamics, Flexible manufacturing systems, Organizational resilience, Manufacturing processes, Industry 4.0, Business continuity planning, Simulation software

Computer Vision for Fashion: A Systematic Review of Design Generation, Simulation, and Personalized Recommendations.

Συγγραφείς: Kachbal, Ilham, El Abdellaoui, Said

Πηγή: Information; Jan2026, Vol. 17 Issue 1, p11, 49p

Θεματικοί όροι: Computer vision, Artificial intelligence, Digital computer simulation, Virtual design, Clothing industry, Recommender systems, Sustainability, Deep learning