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Utility-aware screening with clique-oriented prioritization.

ABSTRACT: Most methods of deciding which hits from a screen to send for confirmatory testing assume that all confirmed actives are equally valuable and aim only to maximize the number of confirmed hits. In contrast, "utility-aware" methods are informed by models of screeners' preferences and can increase the rate at which the useful information is discovered. Clique-oriented prioritization (COP) extends a recently proposed economic framework and aims--by changing which hits are sent for confirmatory testing--to maximize the number of scaffolds with at least two confirmed active examples. In both retrospective and prospective experiments, COP enables accurate predictions of the number of clique discoveries in a batch of confirmatory experiments and improves the rate of clique discovery by more than 3-fold. In contrast, other similarity-based methods like ontology-based pattern identification (OPI) and local hit-rate analysis (LHR) reduce the rate of scaffold discovery by about half. The utility-aware algorithm used to implement COP is general enough to implement several other important models of screener preferences.

SUBMITTER: Swamidass SJ

PROVIDER: S-EPMC3264765 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

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Utility-aware screening with clique-oriented prioritization.

Swamidass S Joshua SJ Calhoun Bradley T BT Bittker Joshua A JA Bodycombe Nicole E NE Clemons Paul A PA

Journal of chemical information and modeling 20111220 1

Most methods of deciding which hits from a screen to send for confirmatory testing assume that all confirmed actives are equally valuable and aim only to maximize the number of confirmed hits. In contrast, "utility-aware" methods are informed by models of screeners' preferences and can increase the rate at which the useful information is discovered. Clique-oriented prioritization (COP) extends a recently proposed economic framework and aims--by changing which hits are sent for confirmatory testi ...[more]

PMID: 22117901

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Project description:OBJECTIVES:Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease in Western countries. No studies have examined the cost-effectiveness of screening its advanced form, nonalcoholic steatohepatitis (NASH). METHODS:We performed a cost-utility analysis of annual noninvasive screening strategies using third-party payer perspective in a general population in comparison to screening a high-risk obese or diabetic population. Screening algorithms involved well-studied techniques, including NAFLD fibrosis score, transient elastography (TE), and acoustic radiation force impulse (ARFI) imaging for detecting advanced fibrosis (? F3); and plasma cytokeratin (CK)-18 for NASH detection. Liver biopsy and magnetic resonance elastography (MRE) were compared as confirmation methods. Canadian dollar (CAD or C$) costs were adjusted for inflation and discounted at 5%. Incremental cost-effectiveness ratio (ICER) of ?C$ 50,000 was considered cost-effective. RESULTS:Compared with no screening, screening with NAFLD fibrosis score/TE/CK-18 algorithm with MRE as confirmation for advanced fibrosis had an ICER of C$ 26,143 per quality-adjusted life year (QALY) gained. Screening in high-risk obese or diabetic populations was more cost-effective, with an ICER of C$ 9,051 and C$ 7,991 per quality-adjusted life-year (QALY) gained, respectively. Liver biopsy confirmation was not found to be cost-effective. CONCLUSIONS:Our model suggests that annual NASH screening in high-risk obese or diabetic populations can be cost-effective. KEY POINTS:• This cost-utility analysis suggests that screening for nonalcoholic steatohepatitis may be cost-effective. • In particular, screening of high-risk obese or diabetic populations is more cost-effective. • Magnetic resonance elastography was more cost-effective to confirm disease compared to biopsy. • More studies are needed to determine quality of life in nonalcoholic steatohepatitis. • More management strategies for nonalcoholic steatohepatitis are also needed.

Project description:Morbidity and mortality impacts of extreme heat amplified by climate change will be unequally distributed among communities given pre-existing differences in socioeconomic, health, and environmental conditions. Many governments are interested in adaptation policies that target those especially vulnerable to the risks, but there are important questions about how to effectively identify and support communities most in need of heat adaptations. Here, we use an equity-oriented adaptation program from the state of California as a case study to evaluate the implications of the currently used environmental justice index (CalEnviroScreen 3.0) for the identification of socially vulnerable communities with climate change adaptation needs. As CalEnviroScreen is geared towards air and water pollution, we assess how community heat risks and adaptation needs would be evaluated differently under two more adaptation-relevant vulnerability indices: the Social Vulnerability Index and the Heat-Health Action Index. Our analysis considers communities at the census tract scale, as well as the patterns emerging at the regional scale. Using the current index, the state designates 25% of its census tracts as "disadvantaged" communities eligible for special adaptation funds. However, an additional 12.6% of the state's communities could be considered vulnerable if the two other indices were considered instead. Only 13.4% of communities are vulnerable across all three vulnerability indices studied. Choice of vulnerability index shapes statewide trends in extreme heat risk and is linked to a community's likelihood of receiving heat-related California Climate Investments (CCI) projects. Tracts that are vulnerable under the current pollution-focused index, but not under the heat-health specific index, received four times the number of heat-related interventions as tracts vulnerable under the reverse scenario. This study demonstrates important nuances relevant to implementing equity-oriented adaptation and explores the challenges, trade-offs, and opportunities in quantifying vulnerability.

Project description:BackgroundThe maximum clique enumeration (MCE) problem asks that we identify all maximum cliques in a finite, simple graph. MCE is closely related to two other well-known and widely-studied problems: the maximum clique optimization problem, which asks us to determine the size of a largest clique, and the maximal clique enumeration problem, which asks that we compile a listing of all maximal cliques. Naturally, these three problems are NP-hard, given that they subsume the classic version of the NP-complete clique decision problem. MCE can be solved in principle with standard enumeration methods due to Bron, Kerbosch, Kose and others. Unfortunately, these techniques are ill-suited to graphs encountered in our applications. We must solve MCE on instances deeply seeded in data mining and computational biology, where high-throughput data capture often creates graphs of extreme size and density. MCE can also be solved in principle using more modern algorithms based in part on vertex cover and the theory of fixed-parameter tractability (FPT). While FPT is an improvement, these algorithms too can fail to scale sufficiently well as the sizes and densities of our datasets grow.ResultsAn extensive testbed of benchmark graphs are created using publicly available transcriptomic datasets from the Gene Expression Omnibus (GEO). Empirical testing reveals crucial but latent features of such high-throughput biological data. In turn, it is shown that these features distinguish real data from random data intended to reproduce salient topological features. In particular, with real data there tends to be an unusually high degree of maximum clique overlap. Armed with this knowledge, novel decomposition strategies are tuned to the data and coupled with the best FPT MCE implementations.ConclusionsSeveral algorithmic improvements to MCE are made which progressively decrease the run time on graphs in the testbed. Frequently the final runtime improvement is several orders of magnitude. As a result, instances which were once prohibitively time-consuming to solve are brought into the domain of realistic feasibility.

Dataset Information

Utility-aware screening with clique-oriented prioritization.

Publications

Utility-aware screening with clique-oriented prioritization.

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