Computational Molecular Biology 2025, Vol.15, No.3, 131-140 http://bioscipublisher.com/index.php/cmb 134 proteome is more like a "construction site", where one can actually see what is happening. Looking at metabolomics, it focuses on the changes of various small molecule products within cells, reflecting the energy and material flow of cells. Once the sugar, amino acid and fat metabolism of tumor cells deviates, it often indicates the signs of drug resistance. By comparing the metabolic profiles of sensitive and drug-resistant samples, researchers can identify the abnormally activated pathways (Shajahan-Haq et al., 2015). Perhaps by intervening in these pathways, the drug efficacy can be "salvaged". By combining the proteome and metabolome, the full picture of drug effects can be depicted at the functional level. Figure 2 Summary of data integration workflow combining proteomics and metabolomics data for a comprehensive understanding of the biochemical alterations of pathogenic drug resistant bacteria (Adopted from Fortuin and Soares, 2022) 3.3 Multi-omics data integration strategy and bioinformatics analysis framework When it comes to studying drug sensitivity, relying on a single type of data is often insufficient. There are various theories at the levels of genes, transcription, proteins, and metabolism, but when taken together, the picture becomes complete. There are several approaches to multi-omics integration. Some people prefer to "get started early", standardizing different data first and then mixing them into a high-dimensional matrix and throwing it all into the model at once (Liu and Mei, 2023). Some people are more cautious. They analyze each one separately first and then piece together the results at the end. No matter which path it is, it cannot do without the support of a complete set of bioinformatics tools. From data cleaning to feature screening, and then to modeling and verification, every step must be meticulous. Old methods like principal component analysis, co-clustering, and network analysis are still effective and can uncover commonalities and complementary information among different omics. There is a more systematic approach - mapping multi-omics data onto the same molecular network, allowing genes, proteins, and metabolites to "match" in pathways. This makes it easier to identify functional modules related to drug responses (Oh et al., 2020). Finally, pathway enrichment is used for verification, which also makes it convenient to explain exactly what these markers mean. 4 Methods and Algorithms for Screening Genomic Markers 4.1 Single-omics feature selection and statistical analysis methods When studying drug sensitivity, many people initially start with single-omics data for the sake of intuitiveness. A common practice is to first separate the drug sensitivity and resistance samples into two groups to see which molecular features are significantly different on both sides - the old methods such as t-test and chi-square test are
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