ArticleCommunications biology2018
The HTPmod Shiny application enables modeling and visualization of large-scale biological data.
Article in Communications biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- ChIP-Hub provides an integrative platform for exploring plant regulome.Nature communications · 2022Pooled it
- ChemGenXplore: an interactive tool for exploring and analysing chemical genomic data.Bioinformatics (Oxford, England) · 2026Article
- High-Throughput Photocatalysis for Generating Reliable Datasets Analyzed by Machine Learning.Chemphyschem : a European journal of chemical physics and physical chemistry · 2025Article
- A Novel Web-Based Approach for Monitoring Biodiversity.Ecology and evolution · 2024Article
- PerSEveML: A Web-Based Tool to Identify Persistent Biomarker Structure for Rare Events Using Integrative Machine Learning Approach.bioRxiv : the preprint server for biology · 2023Article
- scPlant: A versatile framework for single-cell transcriptomic data analysis in plants.Plant communications · 2023Article
- Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches.Frontiers in plant science · 2023Article
- High-throughput phenotyping of physiological traits for wheat resilience to high temperature and drought stress.Journal of experimental botany · 2022Article
- MVApp-Multivariate Analysis Application for Streamlined Data Analysis and Curation.Plant physiology · 2019Article
- Architecture of gene regulatory networks controlling flower development in Arabidopsis thaliana.Nature communications · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The wave of high-throughput technologies in genomics and phenomics are enabling data to be generated on an unprecedented scale and at a reasonable cost. Exploring the large-scale data sets generated by these technologies to derive biological insights requires efficient bioinformatic tools. Here we introduce an interactive, open-source web application (HTPmod) for high-throughput biological data modeling and visualization. HTPmod is implemented with the Shiny framework by integrating the computational power and professional visualization of R and including various machine-learning approaches. We demonstrate that HTPmod can be used for modeling and visualizing large-scale, high-dimensional data sets (such as multiple omics data) under a broad context. By reinvestigating example data sets from recent studies, we find not only that HTPmod can reproduce results from the original studies in a straightforward fashion and within a reasonable time, but also that novel insights may be gained from fast reinvestigation of existing data by HTPmod.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.