Experimental And Computational Methods In The Development Of Diagnostics And Therapeutics For Colon Cancer

Experimental and Computational Methods in the Development of Diagnostics and Therapeutics for Colon Cancer

Cancer continues to be one of the major causes of illness and death worldwide. Cancer is growing at a shocking speed and touches every geographic region of the world. It is predicted that by 2030 there will be 21.7 million new cases and 13 million deaths. To overcome this problem efficiently and to make significant progress in cancer research and therapy, both the scientific and healthcare sectors must work together. Recent advancements in the development of AI-based methods i.e. ensemble or stacking algorithms to discover novel biomarkers using gene expression and other data provide greater opportunity for complete data analysis to decipher the mechanism of colon cancer initiation, progression, and metastasis. In cancer therapy and precision medicine, drug discovery is critical. The surge of omics data over the previous decade has allowed for experimental and computational prediction of anti-cancer therapies and enhanced drug discovery. The goal is therefore to both experimentally and computationally investigate the novel biomarkers in the development of colon cancer and drug resistance. Then, novel drug targets (biomarkers) may help to overcome the problem of drug resistance in cancer. The most advanced experimental and computational techniques, particularly using artificial intelligence and machine learning methods, can be implemented to predict the structural implications of mutations. This will be beneficial in understanding mechanisms of drug resistance and the discovery of novel biomarkers and drugs.

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Statistical Diagnostics for Cancer

This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities.