# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 2 Methods
### Platforms for Clinical Decision-Making and Personalized Medicine
#### 2.4.4 Summary and Comparative Insights
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Platform/Paper** & **Programming Languages/Tools** & **Processes/Analysis Performed** \\ \hline AlzGPS [14] & Django, HTML, CSS, JavaScript, Cytoscape.js, NetworkX & Network-based drug repurposing, AD-related gene and protein network analysis, Mechanism of action (MOA) analysis, Network proximity analysis \\ \hline PALMO [15] & R, Python, ArchR, Seurat, variancePartition & Variance Decomposition Analysis (VDA), Coefficient of Variation Profiling (CVP), Stability Pattern Evaluation (SPECT), Outlier Detection Analysis (ODA), Time Course Analysis (TCA) \\ \hline Aging Bank [16] & Web-based platform using tools like Differential Expression Analyzer, Co-Expression Network, Functional Annotation Analyzer & Multi-omics data integration, differential expression analysis, co-expression network analysis, functional annotation, cancer-aging associations \\ \hline iODA [17] & Java, R, MACS, PeakAnalyzer, Perl & Differential gene/mRNA expression analysis, pathway enrichment analysis, ChIP-Seq data analysis, cross-level omics consistency analysis \\ \hline multiSLIDE [18] & Java (backend), Python (computation), Angular (frontend), MongoDB, SVG (visualization) & Heatmap visualization of multi-omics data, pathway and GO term-based queries, hierarchical clustering, network visualization of inter-omics relationships, differential expression analysis, FDR control \\ \hline MVP [19] & HTML, JavaScript, CSS, SQLite, IGVjs, Lorikeet & Proteogenomics data integration, visualization of peptides, MS/MS spectra visualization, protein-genome alignment, variant annotation analysis \\ \hline VDJView [20] & R, Shiny, dplyr, Seurat, tcR, Rcircos & Gene expression and clonotype analysis, clustering, dimensionality reduction (PCA, t-SNE, UMAP), pseudo-time analysis, immune receptor gene analysis \\ \hline ProteomicsDB [21] & SAP HANA (database), JavaScript, R, Docker, Prosit (deep learning), Python & Mass spectrometry-based proteomics, transcriptomics-proteomics integration, drug sensitivity prediction, protein-protein interaction analysis, missing value imputation, biochemical assay analysis \\ \hline PaintOmics 3 [22] & Python, R, MongoDB, JavaScript, HTML5 & Pathway enrichment, multi-layer feature matching, network analysis, interactive visualization, KEGG-based pathway mapping, heatmaps, trend analysis, integration of regulatory data \\ \hline Argonaut [23] & Docker, HTML, JavaScript (D3.js), MySQL, PHP, Bootstrap, Angular & Real-time statistical analysis, interactive data visualization (e.g., volcano plots, PCA, GO enrichment), batch control, missing value imputation, outlier analysis, secure data sharing \\ \hline SM-Omics [24] & Python, Image registration tools (SpoTteR) & High-throughput spatial transcriptomics, multiplex protein detection, image registration \\ \hline OmicsSuite [25] & Java, R, Shiny, JavaScript, Bioconductor & Multi-omics integration, differential expression analysis, PPI network construction, enrichment analysis, single-cell RNA-Seq, spatial transcriptomics workflows \\ \hline Single-Platform Multi-Omic Profiling system [26] & LC-MS, nLC-MS, computational workflows for proteomics and metabolomics integration & Simultaneous proteomics and metabolomics data acquisition, network-based integrative framework, sample preparation techniques, high-sensitivity analysis, pathway and network analysis, mass spectrometry optimization \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of Multi-Omics Integration Platforms (Part 1).