Zhang, Y.

Identification of a 24-gene prognostic signature that improves the european LeukemiaNet risk classification of acute myeloid leukemia: An international collaborative study (Article)

2013-03-20T00:00:00Z

Purpose: To identify a robust prognostic gene expression signature as an independent predictor of surviva of patients with acute myeloid leukemia (AML) and use it to improve established risk classification Patients and Methods: Four independent sets totaling 499 patients with AML carrying various cytogenetic and molecular abnormalities were used as training sets. Two independent patient sets composed of 825 patients were used as validation sets. Notably, patients from different sets were treated with different protocols, and their gene expression profiles were derived using different microarray platforms. Cox regression and Kaplan-Meier methods were used for survival analyses. Results: A prognostic signature composed of 24 genes was derived from a meta-analysis of Cox regression values of each gene across the four training sets. In multivariable models, a higher sum value of the 24-gene signature was an independent predictor of shorter overall (OS) and event-free surviva (EFS) in both training and validation sets (P < .01). Moreover, this signature could substantially mprove the European LeukemiaNet (ELN) risk classification of AML, and patients in three new risk groups classified by the integrated risk classification showed significantly (P < .001) distinct OS and EFS Conclusion: Despite different treatment protocols applied to patients and use of different microarray platforms for expression profiling, a common prognostic gene signature was identified as an independent predictor of survival of patients with AML. The integrated risk classification incorporating this gene signature provides a better framework for risk stratification and outcome prediction than the ELN classification.

Solving Weighted Voting Game Design Problems Optimally: Representations, Synthesis, and Enumeration (Research Paper)

2012-05-01T00:00:00Z

We study the inverse power index problem for weighted voting games: the problem of finding a weighted voting game in which the power of the players is as close as possible to a certain target distribution. Our goal is to find algorithms that solve this problem exactly. Thereto, we study various subclasses of simple games, and their associated representation methods. We survey algorithms and impossibility results for the synthesis problem, i.e., converting a representation of a simple game into another representation. We contribute to the synthesis problem by showing that it is impossible to compute in polynomial time the list of ceiling coalitions of a game from its list of roof coalitions, and vice versa. Then, we proceed by studying the problem of enumerating the set of weighted voting games. We present first a naive algorithm for this, running in doubly exponential time. Using our knowledge of the synthesis problem, we then improve on this naive algorithm, and we obtain an enumeration algorithm that runs in quadratic exponential time. Moreover, we show that this algorithm runs in output-polynomial time, making it the best possible enumeration algorithm up to a polynomial factor. Finally, we propose an exact anytime algorithm for the inverse power index problem that runs in exponential time. By the genericity of our approach, our algorithm can be used to find a weighted voting game that optimizes any exponential time computable function. We implement our algorithm for the case of the normalized Banzhaf index, and we perform experiments in order to study performance and error convergence.

The implied cost of capital: A new approach (Article)

2012-01-27T00:00:00Z

We use earnings forecasts from a cross-sectional model to proxy for cash flow expectations and estimate the implied cost of capital (ICC) for a large sample of firms over 1968-2008. The earnings forecasts generated by the cross-sectional model are superior to analysts' forecasts in terms of coverage, forecast bias, and earnings response coefficient. Moreover, the model-based ICC is a more reliable proxy for expected returns than the ICC based on analysts' forecasts. We present evidence on the cross-sectional relation between firm-level characteristics and ex ante expected returns using the model-based ICC.

Revenue Prediction in Budget-constrained Sequential Auctions with Complementarities (Research Paper)

2011-08-24T00:00:00Z

When multiple items are auctioned sequentially, the ordering of auctions plays an important role in the total revenue collected by the auctioneer. This is true especially with budget constrained bidders and the presence of complementarities among items. In such sequential auction settings, it is difficult to develop efficient algorithms for finding an optimal sequence of items that optimizes the revenue of the auctioneer. However, when historical data are available, it is possible to learn a model in order to predict the outcome of a given sequence. In this work, we show how to construct such a model, and provide methods that finds a good sequence for a new set of items given the learned model. We develop an auction simulator and design several experiment settings to test the performance of the proposed methods.

A Local Search Algorithm for Clustering in Software as a Service Networks (Research Paper)

2011-03-02T00:00:00Z

In this paper we present and analyze a model for clustering in networks that offer Software as a Service (SaaS). In this problem, organizations requesting a set of applications have to be assigned to clusters such that the costs of opening clusters and installing the necessary applications in clusters are minimized. We prove that this problem is NP-hard, and model it as an Integer Program with symmetry breaking constraints. We then propose a Tabu search heuristic for situations where good solutions are desired in a short computation time. Extensive computational experiments are conducted for evaluating the quality of the solutions obtained by the IP model and the Tabu Search heuristic. Experimental results indicate that the proposed Tabu Search is promising.

Multiagent task allocation in social networks (Article)

2011-02-28T00:00:00Z

This paper proposes a new variant of the task allocation problem, where the agents are connected in a social network and tasks arrive at the agents distributed over the network. We show that the complexity of this problem remains NP-complete. Moreover, it is not approximable within some factor. In contrast to this, we develop an efficient greedy algorithm for this problem. Our algorithm is completely distributed, and it assumes that agents have only local knowledge about tasks and resources. We conduct a broad set of experiments to evaluate the performance and scalability of the proposed algorithm in terms of solution quality and computation time. Three different types of networks, namely small-world, random and scale-free networks, are used to represent various social relationships among agents in realistic applications. The results demonstrate that our algorithm works well and also that it scales well to large-scale applications. In addition we consider the same problem in a setting where the agents holding the resources are self-interested. For this, we show how the optimal algorithm can be used to incentivize these agents to be truthful. However, the efficient greedy algorithm cannot be used in a truthful mechanism, therefore an alternative, cluster-based algorithm is proposed and evaluated.