Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach. Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis

Neoadjuvant in situ gene-mediated cytotoxic immunotherapy improves postoperative outcomes in novel syngeneic esophageal carcinoma models

Esophageal carcinoma is the most rapidly increasing tumor in the United States and has a dismal 15% 5-year survival. Immunotherapy has been proposed to improve patient outcomes; however, no immunocompetent esophageal carcinoma model exists to date to test this approach. We developed two mouse models of esophageal cancer by inoculating immunocompetent mice with syngeneic esophageal cell lines transformed by cyclin-D1 or mutant HRASG12V and loss of p53. Similar to humans, surgery and adjuvant chemotherapy (cisplatin and 5-fluorouracil) demonstrated limited efficacy. Gene-mediated cyototoxic immunotherapy (adenoviral vector carrying the herpes simplex virus thymidine kinase gene in combination with the prodrug ganciclovir; AdV-tk/GCV) demonstrated high levels of in vitro transduction and efficacy. Using in vivo syngeneic esophageal carcinoma models, combining surgery, chemotherapy and AdV-tk/GCV improved survival (P=0.007) and decreased disease recurrence (P<0.001). Mechanistic studies suggested that AdV-tk/GCV mediated a direct cytotoxic effect and an increased intra-tumoral trafficking of CD8 T cells (8.15% vs 14.89%, P=0.02). These data provide the first preclinical evidence that augmenting standard of care with immunotherapy may improve outcomes in the management of esophageal carcinoma