Bing Bing Zhou

TL;DR: This article provides a review of the most widely used ensemble learning methods and their application in various bioinformatics problems, including the main topics of gene expression, mass spectrometry-based proteomics, gene-gene interaction identification from genome-wide association studies, and prediction of regulatory elements from DNA and protein sequences.

TL;DR: This paper considers a three-tier cloud structure, which consists of infrastructure vendors, service providers and consumers, the latter two parties are particular interest to us and contributes to the development of a pricing model—using processor-sharing—for clouds and two sets of profit-driven scheduling algorithms.

TL;DR: This paper uses utility theory leveraged from economics and develops a new utility model for measuring customer satisfaction in the cloud based on the utility model, and designs a mechanism to support utility-based SLAs in order to balance the performance of applications and the cost of running them.

TL;DR: This paper addresses the reconciliation of these conflicting objectives by scheduling service requests with the dynamic creation of service instances by developing a pricing model using processor-sharing for clouds and developing a prioritization policy for data service aiming to maximize the profit of data service.

TL;DR: The experimental results demonstrate that unlike many currently available methods which often perform unevenly with different datasets the proposed hybrid system has a better generalization property which alleviates the method-data dependency problem.