Summary: The accuracy of any analytical method is highly dependent on the selection of an appropriate calibration model. Here, we present CCWeights, an R package for automated assessment and selection of weighting factors for accurate quantification using linear calibration curve. Additionally, CCWeights includes a web application that allows users to analyze their data using an interactive graphical user interface, without any programming requirements. The workflow and features of CCWeights are illustrated by the analyses of two datasets acquired by liquid chromatography-mass spectrometry (LC-MS). The resulting quantification table can be directly utilized for further model assessment and subsequent data analysis.
Availability And Implementation: CCWeights is publicly available on CRAN repository (https://cran.r-project.org/web/packages/CCWeights), with source code available on GitHub (https://github.com/YonghuiDong/CCWeights) under a GPL-3 license. The web application can be run locally from R console using a simple command "runGui()". Alternatively, the web application can be freely accessed for direct online use at https://bcdd.shinyapps.io/CCWeights/.
Supplementary Information: Supplementary data are available at online.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710652 | PMC |
| http://dx.doi.org/10.1093/bioadv/vbab029 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Digital Home-Based Self-Monitoring System for People with Heart Failure: Protocol for Development of SmartHeart and Evaluation of Feasibility and Acceptability.
JMIR Res Protoc
January 2025
School of Exercise and Nutrition Sciences, Institute for Physical Activity and Nutrition, Deakin University, Burwood, Australia.
Background: Heart failure (HF) is a chronic, progressive condition where the heart cannot pump enough blood to meet the body's needs. In addition to the daily challenges that HF poses, acute exacerbations can lead to costly hospitalizations and increased mortality. High health care costs and the burden of HF have led to the emerging application of new technologies to support people living with HF to stay well while living in the community.
View Article and Find Full Text PDFRehabilomics Strategies Enabled by Cloud-Based Rehabilitation: Scoping Review.
J Med Internet Res
January 2025
Department of Physical Medicine and Rehabilitation, Korea University Anam Hospital, Seoul, Republic of Korea.
Background: Rehabilomics, or the integration of rehabilitation with genomics, proteomics, metabolomics, and other "-omics" fields, aims to promote personalized approaches to rehabilitation care. Cloud-based rehabilitation offers streamlined patient data management and sharing and could potentially play a significant role in advancing rehabilomics research. This study explored the current status and potential benefits of implementing rehabilomics strategies through cloud-based rehabilitation.
View Article and Find Full Text PDFPredicting rapid progression in knee osteoarthritis: a novel and interpretable automated machine learning approach, with specific focus on young patients and early disease.
Ann Rheum Dis
January 2025
Department of Surgery, University of Cambridge, Cambridge, UK.
Objectives: To facilitate the stratification of patients with osteoarthritis (OA) for new treatment development and clinical trial recruitment, we created an automated machine learning (autoML) tool predicting the rapid progression of knee OA over a 2-year period.
Methods: We developed autoML models integrating clinical, biochemical, X-ray and MRI data. Using two data sets within the OA Initiative-the Foundation for the National Institutes of Health OA Biomarker Consortium for training and hold-out validation, and the Pivotal Osteoarthritis Initiative MRI Analyses study for external validation-we employed two distinct definitions of clinical outcomes: Multiclass (categorising OA progression into pain and/or radiographic) and binary.
The Effectiveness of Nonpharmacological Interventions to Reduce Procedural Pain of Pediatric Oncology Patients: A Systematic Review and Meta-analysis.
Cancer Nurs
January 2025
Author Affiliations: Departments of Pediatric Nursing (Drs Gürcan, Karataş, and Atay Turan) and Internal Disease Nursing (Dr Özer), Faculty of Nursing, Akdeniz University, Antalya, Türkiye.
Background: Relieving procedural pain in pediatric oncology is an important facilitator of the cancer treatment process.
Objective: To synthesize the effect of nonpharmacological interventions on reducing the procedural pain of pediatric oncology patients in randomized controlled trials.
Methods: A meta-analysis and systematic review for randomized controlled trials.
Development of Mobile Application Based System for Improving Population Based Cancer Screening by Community Health Workers.
Asian Pac J Cancer Prev
January 2025
Cachar Cancer Hospital and Research Center, NS Avenue, Meherpur, Silchar, Assam, India.
Objective: Cancer remains a leading cause of morbidity and mortality globally, with India experiencing a significant cancer burden. Effective population-based cancer screening is crucial for early detection and reduction of cancer-related deaths. This study aims to develop a mobile application-based Cancer Screening and Surveillance System (CSMS) to enhance the efficiency and effectiveness of population-based cancer screening by community health workers (CHWs).
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!