Enhanced Relative Comparison of Traditional Sorting Approaches towards Optimization of New Hybrid Two-in-One (OHTO) Novel Sorting Technique

In the world of computer technology, sorting is an operation on a data set that involves ordering it in an increasing or decreasing fashion according to some linear relationship among the data items. With the rise in the generation of big data, the concept of big numbers has come into existence. When the number of records to be sorted is limited to thousands, traditional sorting approaches can be used; in such cases, complexities in their execution time can be ignored. However, in the case of big data, where processing times for billions or trillions of records are very long, time complexity is very significant. Therefore, an optimized sorting technique with efficient time complexity is very much required. Hence, in this paper an optimized sorting technique is proposed, named Optimized Hybrid Two-in-One Novel Sorting Technique (OHTO, a mixed approach of the Insertion Sort technique and the Bubble Sort technique. The proposed sorting technique uses the procedure of both Bubble Sort and Insertion Sort, resulting in fewer comparisons, fewer data movements, fewer data insertions, and less time complexity for any given input data set compared to existing sorting techniques

Enhanced Relative Comparison of Traditional Sorting Approaches towards Optimization of New Hybrid Two-in-One (OHTO) Novel Sorting Technique

In the world of computer technology, sorting is an operation on a data set that involves ordering it in an increasing or decreasing fashion according to some linear relationship among the data items. With the rise in the generation of big data, the concept of big numbers has come into existence. When the number of records to be sorted is limited to thousands, traditional sorting approaches can be used; in such cases, complexities in their execution time can be ignored. However, in the case of big data, where processing times for billions or trillions of records are very long, time complexity is very significant. Therefore, an optimized sorting technique with efficient time complexity is very much required. Hence, in this paper an optimized sorting technique is proposed, named Optimized Hybrid Two-in-One Novel Sorting Technique (OHTO, a mixed approach of the Insertion Sort technique and the Bubble Sort technique. The proposed sorting technique uses the procedure of both Bubble Sort and Insertion Sort, resulting in fewer comparisons, fewer data movements, fewer data insertions, and less time complexity for any given input data set compared to existing sorting techniques

An integrative sufficient dimension reduction method for multi-omics data analysis

Ding, ShanshanAdvancement in next-generation sequencing, transcriptomics, proteomics andother high-throughput technologies has enabled to simultaneously measure multipletypes of genomic data for cancer samples. These data may reveal new biological in-sights as compared to analyzing one single genome type. This study proposes a newintegrative supervised dimension reduction method, called integrative sliced inverseregression (integrative SIR), for simultaneous analysis of multiple omics data types ofcancer samples, including MiRNA, MRNA and proteomics, to improve prediction andinterpretation. The proposed method can reduce the dimensions of multiple omics datasimultaneously while sharing common latent structures without losing any informationin prediction. By capturing common information across data types, the new methoddemonstrates advantages over conventional methods. In this work, we classify differenttumor types like CNS, leukemia and melanoma using dimension reduction methods.M.S.University of Delaware, Program in Bioinformatics and Computational Biology