Data Mining Applications in Cancer Drug Discovery Essay
Developing a new drug is a complex, risky, expensive and time-consuming undertaking. It is estimated that the conventional drug discovery process, which ends with a new drug ready for market, can. Furthermore, in the genomic era when the number of available genomes is increasing, genome mining combined with synthetic biology significantly aids drug discovery. In the current review, we discuss the importance of genome mining and synthetic biology approaches to identify novel natural products, also highlighting that there are widespread applications of ANNs and deep learning DL algorithms in biomedical and drug discovery. Gene expression data and microarray data are used for the development of anticancer drugs and machine learning models for biomarker prediction. Fig. 2. Representation of genome data for machine learning approaches. Synthetic biology in the discovery of new drugs. The availability of well-characterized and standardized biological components such as repressor-DNA interactions, transactivation domains, minimal promoters, and reporter systems allows the assembly of designer circuits that mimic pathways essential for or involved in human pathogens, for the rapid discovery and development of new anticancer agents. drugs. Clinical trials, electronic health records, high-resolution medical imaging, and genomic assessments are just some of the tools. The increasing understanding of the genetics and molecular biology of cancer has resulted in the identification of an increasing number of potential molecular targets for anticancer drug discovery. In the last part of the study, we assessed the applications of artificial intelligence biological analysis for cancer therapy from four perspectives: Identification of novel anticancer targets. These web-based data mining tools enable robust analysis of the correlation between the in vitro anticancer activity of the drugs in the NCI anticancer database, protein levels, and mRNA levels.