# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 3 Discussion  
The rapid development of multi-omics platforms has introduced a new era of integrative biology, enabling deeper insights into complex biological systems and diseases [6]. The platforms discussed in this review have been instrumental in advancing research across  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Platform/Paper** & **Genome** & **Transcriptome** & **Proteome** & **Metabolome** & **Epigenome** \\ \hline AlzGPS [14] & Yes & Yes & Yes & Yes & No \\ PALMO [15] & Yes & Yes & Yes & No & Yes \\ Aging Bank [16] & Yes & Yes & No & No & Yes \\ iODA [17] & Yes & Yes & No & No & Yes \\ multiSLIDE [18] & Yes & Yes & Yes & Yes & No \\ MVP [19] & Yes & Yes & Yes & No & No \\ VDJView [20] & No & Yes & No & No & No \\ ProteomicsDB [21] & No & Yes & Yes & No & No \\ PaintOmics 3 [22] & No & Yes & Yes & Yes & Yes \\ Argonaut [23] & No & No & Yes & Yes & No \\ SM-Omics [24] & No & Yes & No & No & No \\ OmicsSuite [25] & Yes & Yes & Yes & Yes & No \\ Single-Platform & & & & & \\ Multi-Omic Profiling & No & No & Yes & Yes & No \\ system [26] & & & & & \\ CHOmics [27] & No & Yes & Yes & No & No \\ Majorbio Cloud [28] & Yes & Yes & Yes & Yes & No \\ GraphOmics [29] & No & Yes & Yes & Yes & No \\ OmicsAnalyst [30] & Yes & Yes & Yes & Yes & No \\ OmicsNet 2.0 [31] & Yes & Yes & Yes & Yes & No \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of Multi-Omics Integration Platforms (Part 2/Omics Intagated).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Platform/Paper** & **Programming Languages/Tools** & **Processes/Analysis Performed** \\ \hline CHOmics [27] & PHP, R, JavaScript, Limma, Subread, Voom, KEGG pathways & RNA-Seq processing, Differential expression (DE) analysis, PCA, pathway enrichment, meta-analysis, visualization of gene expression and pathway diagrams across multiple omics \\ \hline \hline \multirow{7}{*}{Majorbio Cloud [28]} & \multirow{7}{*}{Django, HTML, CSS, JavaScript, MongoDB, Ceph, MySQL, D3} & One-stop multi-omics data integration, gene expression analysis, gene set and structure analysis, metagenomics, differential expression analysis, taxonomic and functional analysis, correlation, model predictive analysis, visualization through Sanger-Charts, interactive analysis reports, and cloud storage.  
\begin{tabular}{l c c} \hline \hline \multirow{3}{*}{GraphOmics [29]} & \multirow{3}{*}{Python, JavaScript, Django, Plotly, D3, SQLite, Neo4j} & Horizontal omics integration using Reactome pathways, differential expression analysis, PCA, pathway enrichment analysis, interactive clustering, heatmaps, gene ontology analysis, multi-omics integration, linked data exploration across tables \\ \hline \multirow{3}{*}{OmicsAnalyst [30]} & \multirow{3}{*}{Java, R, JavaScript Faces (JSF), PrimeFaces, sigma.js, three.js} &
\begin{tabular}{l} Correlation network analysis, cluster heatmap analysis, dimension reduction analysis, multi-view clustering, interactive 2D/3D visual analytics, PCA, DIABLO, feature correlation, enrichment analysis \\ \end{tabular} \\ \hline \multirow{3}{*}{OmicsNet 2.0 [31]} & \multirow{3}{*}{Python, R, JavaScript, Kcpp, Cytoscape, Rgraphviz} & Multi-omics network creation, SNP and peak annotation, microbiome interaction network analysis, 2D and 3D network visualization, random walk with restart algorithm for disease gene prediction, network refinement using graph algorithms (PCSF), enrichment analysis \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._diverse fields, from Alzheimer's drug discovery to plant-specific trait development. By integrating data from genomics, transcriptomics, proteomics, metabolomics, and other omics layers, these platforms allow for a more systemic view of biological processes, thereby facilitating the identification of biomarkers, drug targets, and pathways that would otherwise remain hidden in single-omics analysis [32].