The process of inferring parameter values from experimental data can be a cumbersome task. In addition, the collection of experimental data can be time consuming and costly. This paper covers both these issues by addressing the following question: "Which experimental outputs should be measured to ensure that unique model parameters can be calculated?". Stated formally, we examine the topic of minimal output sets that guarantee a model's structural identifiability. To that end, we introduce an algorithm that guides a researcher as to which model outputs to measure. Our algorithm consists of an iterative structural identifiability analysis and can determine multiple minimal output sets of a model. This choice in different output sets offers researchers flexibility during experimental design. Our method can determine minimal output sets of large differential equation models within short computational times.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6231658 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0207334 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion.
Sensors (Basel)
January 2025
School of Electronic and Information Engineering, Ankang University, Ankang 725000, China.
Convolutional neural networks have achieved excellent results in image denoising; however, there are still some problems: (1) The majority of single-branch models cannot fully exploit the image features and often suffer from the loss of information. (2) Most of the deep CNNs have inadequate edge feature extraction and saturated performance problems. To solve these problems, this paper proposes a two-branch convolutional image denoising network based on nonparametric attention and multiscale feature fusion, aiming to improve the denoising performance while better recovering the image edge and texture information.
View Article and Find Full Text PDFMeStanG-Resource for High-Throughput Sequencing Standard Data Sets Generation for Bioinformatic Methods Evaluation and Validation.
Biology (Basel)
January 2025
Institute for Biosecurity and Microbial Forensics (IBMF), Oklahoma State University, Stillwater, OK 74078, USA.
Metagenomics analysis has enabled the measurement of the microbiome diversity in environmental samples without prior targeted enrichment. Functional and phylogenetic studies based on microbial diversity retrieved using HTS platforms have advanced from detecting known organisms and discovering unknown species to applications in disease diagnostics. Robust validation processes are essential for test reliability, requiring standard samples and databases deriving from real samples and in silico generated artificial controls.
View Article and Find Full Text PDFDeep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson's Disease Stage Prediction.
J Imaging Inform Med
January 2025
Department of Anesthesiology, E-Da Cancer Hospital, I-Shou University, Kaohsiung, Taiwan.
Parkinson's disease (PD), a degenerative disorder of the central nervous system, is commonly diagnosed using functional medical imaging techniques such as single-photon emission computed tomography (SPECT). In this study, we utilized two SPECT data sets (n = 634 and n = 202) from different hospitals to develop a model capable of accurately predicting PD stages, a multiclass classification task. We used the entire three-dimensional (3D) brain images as input and experimented with various model architectures.
View Article and Find Full Text PDF[Prediction of potential geographic distribution of in Yunnan Province using random forest and maximum entropy models].
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi
December 2024
Yunnan Institute of Endemic Diseases Control and Prevention, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Dali, Yunnan 671000, China.
Objective: To predict the potential geographic distribution of in Yunnan Province using random forest (RF) and maximum entropy (MaxEnt) models, so as to provide insights into surveillance and control in Yunnan Province.
Methods: The snail survey data in Yunnan Province from 2015 to 2016 were collected and converted into snail distribution site data. Data of 22 environmental variables in Yunnan Province were collected, including twelve climate variables (annual potential evapotranspiration, annual mean ground surface temperature, annual precipitation, annual mean air pressure, annual mean relative humidity, annual sunshine duration, annual mean air temperature, annual mean wind speed, ≥ 0 ℃ annual accumulated temperature, ≥ 10 ℃ annual accumulated temperature, aridity and index of moisture), eight geographical variables (normalized difference vegetation index, landform type, land use type, altitude, soil type, soil textureclay content, soil texture-sand content and soil texture-silt content) and two population and economic variables (gross domestic product and population).
MythicVision: a deep learning powered mobile application for understanding Indian mythological deities using weight centric decision approach.
Sci Rep
January 2025
School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, 522237, India.
Indian mythology is a treasure trove of divine tales, yet a gap in understanding still exists between foreign tourists and the rich cultural heritage of Indian deities. To address the problem, this paper presents a deep learning-driven mobile application named "MythicVision" designed to help foreign tourists better understand India's rich cultural heritage by recognizing and interpreting images of Indian mythological deities. At first, four state-of-the-art deep models have been trained and evaluated on a custom in-house dataset consists of 10,970 images of various Indian deities sourced from both natural scene and web images.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!