Integration of multi-omics data to mine cancer-related gene modules

3PNMF-MKL: A non-negative matrix factorization-based multiple kernel learning method for multi-modal data integration and its application to gene signature detection

In this current era, biomedical big data handling is a challenging task. Interestingly, the integration of multi-modal data, followed by significant feature mining (gene signature detection), becomes a daunting task. Remembering this, here, we proposed a novel framework, namely, three-factor penalized, non-negative matrix factorization-based multiple kernel learning with soft margin hinge loss (3PNMF-MKL) for multi-modal data integration, followed by gene signature detection. In brief, limma, employing the empirical Bayes statistics, was initially applied to each individual molecular profile, and the statistically significant features were extracted, which was followed by the three-factor penalized non-negative matrix factorization method used for data/matrix fusion using the reduced feature sets. Multiple kernel learning models with soft margin hinge loss had been deployed to estimate average accuracy scores and the area under the curve (AUC). Gene modules had been identified by the consecutive analysis of average linkage clustering and dynamic tree cut. The best module containing the highest correlation was considered the potential gene signature. We utilized an acute myeloid leukemia cancer dataset from The Cancer Genome Atlas (TCGA) repository containing five molecular profiles. Our algorithm generated a 50-gene signature that achieved a high classification AUC score (viz., 0.827). We explored the functions of signature genes using pathway and Gene Ontology (GO) databases. Our method outperformed the state-of-the-art methods in terms of computing AUC. Furthermore, we included some comparative studies with other related methods to enhance the acceptability of our method. Finally, it can be notified that our algorithm can be applied to any multi-modal dataset for data integration, followed by gene module discovery.

Exploration of hanshi zufei prescription for treatment of COVID-19 based on network pharmacology

Objective

Network pharmacology combines drug and disease targets with biological information networks based on the integrity and systematicness of the interactions between drugs and disease targets. This study aims to explore the molecular basis of Hanshi Zufei formula for treatment of COVID-19 based on network pharmacology and molecular docking techniques.

Methods

Using TCMSP, the chemical constituents and molecular targets of Atractylodis Rhizoma, Citri Reticulatae Pericarpium, Magnoliae Officinalis Cortex, Pogostemonis Herba, Tsaoko Fructus, Ephedrae Herba, Notopterygii Rhizoma et Radix, Zingiberis Rhizoma Recens, and Arecae Semen were investigated. The predicted targets of novel coronavirus were screened using the NCBI and GeneCards databases. To further screen the drug-disease core targets network, the corresponding target proteins were queried using multiple databases (Biogrid, DIP, and HPRD), a protein interaction network graph was constructed, and the network topology was analyzed. The molecular docking studies were also performed between the network’s top 15 compounds and the coronavirus (SARS-CoV-2) 3CL hydrolytic enzyme and angiotensin conversion enzyme II (ACE2).

Results

The herb-active ingredient-target network contained nine drugs, 86 compounds, and 49 drug-disease targets. Gene ontology (GO) enrichment analysis resulted in 1566 GO items (P < 0.05), among which 1438 were biological process items, 35 were cell composition items, and 93 were molecular function items. Fourteen signal pathways were obtained by enrichment screening of the KEGG pathway database (P < 0.05). The molecular docking results showed that the affinity of the core active compounds with the SARS-CoV-2 3CL hydrolase was better than for the other compounds.

Conclusion

Several core compounds can regulate multiple signaling pathways by binding with 3CL hydrolase and ACE2, which might contribute to the treatment of COVID-19.