Prioritization Algorithm Research Articles

BeReTa: a systematic method for identifying target transcriptional regulators to enhance microbial production of chemicals.

Modulation of regulatory circuits governing the metabolic processes is a crucial step for developing microbial cell factories. Despite the prevalence of in silico strain design algorithms, most of them are not capable of predicting required modifications in regulatory networks. Although a few algorithms may predict relevant targets for transcriptional regulator (TR) manipulations, they have limited reliability and applicability due to their high dependency on the availability of integrated metabolic/regulatory models. We present BeReTa (Beneficial Regulator Targeting), a new algorithm for prioritization of TR manipulation targets, which makes use of unintegrated network models. BeReTa identifies TR manipulation targets by evaluating regulatory strengths of interactions and beneficial effects of reactions, and subsequently assigning beneficial scores for the TRs. We demonstrate that BeReTa can predict both known and novel TR manipulation targets for enhanced production of various chemicals in Escherichia coli Furthermore, through a case study of antibiotics production in Streptomyces coelicolor, we successfully demonstrate its wide applicability to even less-studied organisms. To the best of our knowledge, BeReTa is the first strain design algorithm exclusively designed for predicting TR manipulation targets. MATLAB code is available at https://github.com/kms1041/BeReTa (github). byungkim@snu.ac.krSupplementary information: Supplementary data are available at Bioinformatics online.

A genomics-based systems approach towards drug repositioning for rheumatoid arthritis.

BackgroundRheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation and destruction of synovial joints. RA affects up to 1 % of the population worldwide. Currently, there are no drugs that can cure RA or achieve sustained remission. The unknown cause of the disease represents a significant challenge in the drug development. In this study, we address this challenge by proposing an alternative drug discovery approach that integrates and reasons over genetic interrelationships between RA and other genetic diseases as well as a large amount of higher-level drug treatment data.We first constructed a genetic disease network using disease genetics data from Genome-Wide Association Studies (GWAS). We developed a network-based ranking algorithm to prioritize diseases genetically-related to RA (RA-related diseases). We then developed a drug prioritization algorithm to reposition drugs from RA-related diseases to treat RA.ResultsOur algorithm found 74 of the 80 FDA-approved RA drugs and ranked them highly (recall: 0.925, median ranking: 8.93 %), demonstrating the validity of our strategy. When compared to a study that used GWAS data to directly connect RA-associated genes to drug targets (“direct genetics-based” approach), our algorithm (“indirect genetics-based”) achieved a comparable overall performance, but complementary precision and recall in retrospective validation (precision: 0.22, recall: 0.36; F1: 0.27 vs. precision: 0.74, recall: 0.16; F1: 0.28). Our approach performed significantly better in novel predictions when evaluated using 165 not-yet-FDA-approved RA drugs (precision: 0.46, recall: 0.50; F1: 0.47 vs. precision: 0.40, recall: 0.006; F1: 0.01).ConclusionsIn summary, although the fundamental pathophysiological mechanisms remain uncharacterized, our proposed computation-based drug discovery approach to analyzing genetic and treatment interrelationships among thousands of diseases and drugs can facilitate the discovery of innovative drugs for treating RA.

PoplarGene: poplar gene network and resource for mining functional information for genes from woody plants.

Poplar is not only an important resource for the production of paper, timber and other wood-based products, but it has also emerged as an ideal model system for studying woody plants. To better understand the biological processes underlying various traits in poplar, e.g., wood development, a comprehensive functional gene interaction network is highly needed. Here, we constructed a genome-wide functional gene network for poplar (covering ~70% of the 41,335 poplar genes) and created the network web service PoplarGene, offering comprehensive functional interactions and extensive poplar gene functional annotations. PoplarGene incorporates two network-based gene prioritization algorithms, neighborhood-based prioritization and context-based prioritization, which can be used to perform gene prioritization in a complementary manner. Furthermore, the co-functional information in PoplarGene can be applied to other woody plant proteomes with high efficiency via orthology transfer. In addition to poplar gene sequences, the webserver also accepts Arabidopsis reference gene as input to guide the search for novel candidate functional genes in PoplarGene. We believe that PoplarGene (http://bioinformatics.caf.ac.cn/PoplarGene and http://124.127.201.25/PoplarGene) will greatly benefit the research community, facilitating studies of poplar and other woody plants.

Towards understanding brain-gut-microbiome connections in Alzheimer's disease.

BackgroundAlzheimer’s disease (AD) is complex, with genetic, epigenetic, and environmental factors contributing to disease susceptibility and progression. While significant progress has been made in understanding genetic, molecular, behavioral, and neurological aspects of AD, relatively little is known about which environmental factors are important in AD etiology and how they interact with genetic factors in the development of AD. Here, we propose a data-driven, hypotheses-free computational approach to characterize which and how human gut microbial metabolites, an important modifiable environmental factor, may contribute to various aspects of AD.Materials and methodsWe integrated vast amounts of complex and heterogeneous biomedical data, including disease genetics, chemical genetics, human microbial metabolites, protein-protein interactions, and genetic pathways. We developed a novel network-based approach to model the genetic interactions between all human microbial metabolites and genetic diseases. We identified metabolites that share significant genetic commonality with AD in humans. We developed signal prioritization algorithms to identify the co-regulated genetic pathways underlying the identified AD-metabolite (brain-gut) connections.ResultsWe validated our algorithms using known microbial metabolite-AD associations, namely AD-3,4-dihydroxybenzeneacetic acid, AD-mannitol, and AD-succinic acid. Our study provides supporting evidence that human gut microbial metabolites may be an important mechanistic link between environmental exposure and various aspects of AD. We identified metabolites that are significantly associated with various aspects in AD, including AD susceptibility, cognitive decline, biomarkers, age of onset, and the onset of AD. We identified common genetic pathways underlying AD biomarkers and its top one ranked metabolite trimethylamine N-oxide (TMAO), a gut microbial metabolite of dietary meat and fat. These coregulated pathways between TMAO-AD may provide insights into the mechanisms of how dietary meat and fat contribute to AD.ConclusionsEmploying an integrated computational approach, we provide intriguing and supporting evidence for a role of microbial metabolites, an important modifiable environmental factor, in AD etiology. Our study provides the foundations for subsequent hypothesis-driven biological and clinical studies of brain-gut-environment interactions in AD.

Next-generation personalised medicine for high-risk paediatric cancer patients – The INFORM pilot study

The ‘Individualized Therapy for Relapsed Malignancies in Childhood’ (INFORM) precision medicine study is a nationwide German program for children with high-risk relapsed/refractory malignancies, which aims to identify therapeutic targets on an individualised basis. In a pilot phase, reported here, we developed the logistical and analytical pipelines necessary for rapid and comprehensive molecular profiling in a clinical setting. Fifty-seven patients from 20 centers were prospectively recruited. Malignancies investigated included sarcomas (n = 25), brain tumours (n = 23), and others (n = 9). Whole-exome, low-coverage whole-genome, and RNA sequencing were complemented with methylation and expression microarray analyses. Alterations were assessed for potential targetability according to a customised prioritisation algorithm and subsequently discussed in an interdisciplinary molecular tumour board. Next-generation sequencing data were generated for 52 patients, with the full analysis possible in 46 of 52. Turnaround time from sample receipt until first report averaged 28 d. Twenty-six patients (50%) harbored a potentially druggable alteration with a prioritisation score of ‘intermediate’ or higher (level 4 of 7). Common targets included receptor tyrosine kinases, phosphoinositide 3-kinase–mammalian target of rapamycin pathway, mitogen-activated protein kinase pathway, and cell cycle control. Ten patients received a targeted therapy based on these findings, with responses observed in some previously treatment-refractory tumours. Comparative primary relapse analysis revealed substantial tumour evolution as well as one case of unsuspected secondary malignancy, highlighting the importance of re-biopsy at relapse. This study demonstrates the feasibility of comprehensive, real-time molecular profiling for high-risk paediatric cancer patients. This extended proof-of-concept, with examples of treatment consequences, expands upon previous personalised oncology endeavors, and presents a model with considerable interest and practical relevance in the burgeoning era of personalised medicine.

Evaluation of rain gauge network in arid regions using geostatistical approach: case study in northern Oman

A geostatistical approach based on ordinary kriging is presented for the evaluation and the augmentation of an existing rain gauge network. The evaluation is based on estimating the percentage of the area that achieves a targeted level of acceptable accuracy. The variances of kriging estimation erros at un-gauged locations were assumed to be normally distributed. Kriging estimation erros with a probability that equals to or exceeds a given threshold value of acceptance probability were assumed to have satisfactory accuracies. The percentage of the area that achieved the targeted probability of acceptance is delineated and used to judge the overall performance of the existing rain gauge network. A study area in northern Oman located in Sohar governorate is selected as the pilot case. The area has 34 rain gauges and it is characterized by a terrain surface that varies from coastal plain to mountains. For a threshold value of 0.85, and 0.90 of acceptance probability, the existing network achieved area of acceptable probability of 88.71 and 77.72 %, respectively. For a success criterion of 80 %, the existing rain gauge network indicated acceptable performance for acceptance probability threshold of 0.85 and inadequate performance is noticed in the case of probability threshold of 0.90, which necessitated further network augmentation. A sequential algorithm for ranking and prioritization of the existing rain gauges is used to classify the existing rain gauges into base and non-base rain gauges. The base rain gauge network for mean annual rainfall comprised about 29 of the existing rain gauges. The identified non-base rain gauges were sequentially relocated to achieve higher levels of percentage of area with acceptable accuracy. The percentage of area with acceptable accuracy increased from 88.71 % for the original rain gauge network to about 94.51 % for the augmented network by adding four rain gages at probability acceptance threshold of 0.85. It also increased from 77.72 % for the existing network to 90.50 % for the augmented rain gauge network at acceptance threshold of 0.9.

Test-case prioritization: achievements and challenges

Test-case prioritization, proposed at the end of last century, aims to schedule the execution order of test cases so as to improve test effectiveness. In the past years, test-case prioritization has gained much attention, and has significant achievements in five aspects: prioritization algorithms, coverage criteria, measurement, practical concerns involved, and application scenarios. In this article, we will first review the achievements of test-case prioritization from these five aspects and then give our perspectives on its challenges.

Finding the needles in the haystack: efficient intelligence processing

As a result of communication technologies, the main intelligence challenge has shifted from collecting data to efficiently processing it so that relevant, and only relevant, information is passed on to intelligence analysts. We consider intelligence data intercepted on a social communication network. The social network includes both adversaries (eg terrorists) and benign participants. We propose a methodology for efficiently searching for relevant messages among the intercepted communications. Besides addressing a real and urgent problem that has attracted little attention in the open literature thus far, the main contributions of this paper are two-fold. First, we develop a novel knowledge accumulation model for intelligence processors, which addresses both the nodes of the social network (the participants) and its edges (the communications). Second, we propose efficient prioritization algorithms that utilize the processor’s accumulated knowledge. Our approach is based on methods from graphical models, social networks, random fields, Bayesian learning, and exploration/exploitation algorithms.

First applications of a targeted exome sequencing approach in fetuses with ultrasound abnormalities reveals an important fraction of cases with associated gene defects.

Background. Fetal malformations and other structural abnormalities are relatively frequent findings in the course of routine prenatal ultrasonographic examination. Due to their considerable genetic and clinical heterogeneity, the underlying genetic cause is often elusive and the resulting inability to provide a precise diagnosis precludes proper reproductive and fetal risk assessment. We report the development and first applications of an expanded exome sequencing-based test, coupled to a bioinformatics-driven prioritization algorithm, targeting gene disorders presenting with abnormal prenatal ultrasound findings.Methods. We applied the testing strategy to14 euploid fetuses, from 11 on-going pregnancies and three products of abortion, all with various abnormalities or malformations detected through prenatal ultrasound examination. Whole exome sequencing (WES) was followed by variant prioritization, utilizing a custom analysis pipeline (Fetalis algorithm), targeting 758 genes associated with genetic disorders which may present with abnormal fetal ultrasound findings.Results. A definitive or highly-likely diagnosis was made in 6 of 14 cases (43%), of which 3 were abortuses (Ellis-van Creveld syndrome, Ehlers-Danlos syndrome and Nemaline myopathy 2) and 3 involved on-going pregnancies (Citrullinemia, Noonan syndrome, PROKR2-related Kallmann syndrome). In the remaining eight on-going pregnancy cases (57%), a ZIC1 variant of unknown clinical significance was detected in one case, while in seven cases testing did not reveal any pathogenic variant(s). Pregnancies were followed-up to birth, resulting in one neonate harboring the PROKR2 mutation, presenting with isolated minor structural cardiac abnormalities, and in seven apparently healthy neonates.Discussion. The expanded targeted exome sequencing-based approach described herein (Fetalis), provides strong evidence suggesting a definite and beneficial increase in our diagnostic capabilities in prenatal diagnosis of otherwise chromosomally balanced fetuses with troubling ultrasound abnormalities. Furthermore, the proposed targeted exome sequencing strategy, designed primarily as a diagnostic rather than a research discovery tool, overcomes many of the problems and limitations associated with clinical wide-scale WES testing in a prenatal setting.

Discovering and understanding oncogenic gene fusions through data intensive computational approaches.

Although gene fusions have been recognized as important drivers of cancer for decades, our understanding of the prevalence and function of gene fusions has been revolutionized by the rise of next-generation sequencing, advances in bioinformatics theory and an increasing capacity for large-scale computational biology. The computational work on gene fusions has been vastly diverse, and the present state of the literature is fragmented. It will be fruitful to merge three camps of gene fusion bioinformatics that appear to rarely cross over: (i) data-intensive computational work characterizing the molecular biology of gene fusions; (ii) development research on fusion detection tools, candidate fusion prioritization algorithms and dedicated fusion databases and (iii) clinical research that seeks to either therapeutically target fusion transcripts and proteins or leverages advances in detection tools to perform large-scale surveys of gene fusion landscapes in specific cancer types. In this review, we unify these different—yet highly complementary and symbiotic—approaches with the view that increased synergy will catalyze advancements in gene fusion identification, characterization and significance evaluation.

Empirical Study of the Effects of Different Similarity Measures on Test Case Prioritization

Similarity-based test case prioritization algorithms have been applied to regression testing. The common characteristic of these algorithms is to reschedule the execution order of test cases according to the distances between pair-wise test cases. The distance information can be calculated by different similarity measures. Since the topologies vary with similarity measures, the distances between pair-wise test cases calculated by different similarity measures are different. Similarity measures could significantly influence the effectiveness of test case prioritization. Therefore, we empirically evaluate the effects of six similarity measures on two similarity-based test case prioritization algorithms. The obtained results are statistically analyzed to recommend the best combination of similarity-based prioritization algorithms and similarity measures. The experimental results, confirmed by a statistical analysis, indicate that Euclidean distance is more efficient in finding defects than other similarity measures. The combination of the global similarity-based prioritization algorithm and Euclidean distance could be a better choice. It generates not only higher fault detection effectiveness but also smaller standard deviation. The goal of this study is to provide practical guides for picking the appropriate combination of similarity-based test case prioritization techniques and similarity measures.

Improving the efficiency of functional verification based on test prioritization

Functional verification has become the key bottleneck that delays time-to-market during the embedded system design process. And simulation-based verification is the mainstream practice in functional verification due to its flexibility and scalability. In practice, the success of the simulation-based verification highly depends on the quality of functional tests in use which is usually evaluated by coverage metrics. Since test prioritization can provide a way to simulate the more important tests which can improve the coverage metrics evidently earlier, we propose a test prioritization approach based on the clustering algorithm to obtain a high coverage level earlier in the simulation process. The k-means algorithm, which is one of the most popular clustering algorithms and usually used for the test prioritization, has some shortcomings which have an effect on the effectiveness of test prioritization. Thus we propose three enhanced k-means algorithms to overcome these shortcomings and improve the effectiveness of the test prioritization. Then the functional tests in the simulation environment can be ordered with the test prioritization based on the enhanced k-means algorithms. Finally, the more important tests, which can improve the coverage metrics evidently, can be selected and simulated early within the limited simulation time. Experimental results show that the enhanced k-means algorithms are more accurate and efficient than the standard k-means algorithm for the test prioritization, especially the third enhanced k-means algorithm. In comparison with simulating all the tests randomly, the more important tests, which are selected with the test prioritization based on the third enhanced k-means algorithm, achieve almost the same coverage metrics in a shorter time, which achieves a 90% simulation time saving.

Combined Genetic and Simulated Annealing Approach for Test Case Prioritization

Background: The test case prioritization of regression Testing is described. Methods: A new test case prioritization algorithm is proposed to get better the rate of fault detection and cost reduction. Heuristic Techniques like Genetic Algorithm (GA) and Simulated Annealing (SA) are employed. It prioritizes the test cases depends on fault detection ability and execution time taken. Findings: The implementation of proposed algorithm in JAVA is found to produce optimal or near optimal results. The efficiency of proposed regression testing technique is proved by comparing it with GA and SA methods individually. Applications: A complete automation tool for the complete usage of the algorithm is being developed. It will also be analyzed on larger projects with large number of test cases and faults.

Phrase Prioritization Algorithm and Supporting Data Structure for Retrieval

In the heart of this research work lies the proposed algorithm, which prioritizes the phrases of the search queries. This algorithm suggests the methodology of fetching phrases and then searching all possible phrases, so that recall value can be increased. The most important issue in this regard is the usage of such data structure, which facilitates the efficient search of phrases in documents. For this purpose, Linked Representation of Sparse Matrix has been suggested, which consists of linked lists not only rowwise but also columnwise. Columns correspond to the documents and hence make the search of every possible phrase efficient. Rows correspond to the dictionary of words. Linked Representation maintain the dynamic nature of documents as well as insertion and deletion of words from the documents. Emphasis has also been given to the categorization of dictionary and query words into specific and general words, which will increase the precision of search results. Specific words will be given higher priority as compared to the general words. Synonyms have also been considered for retrieval of documents, hence increasing the understanding between user requirement and search engine

Retraction Note to: Candidate gene prioritization.

Candidate gene identification is typically labour intensive, involving laboratory experiments required to corroborate or disprove any hypothesis for a nominated candidate gene being considered the causative gene. The traditional approach to reduce the number of candidate genes entails fine-mapping studies using markers and pedigrees. Gene prioritization establishes the ranking of candidate genes based on their relevance to the biological process of interest, from which the most promising genes can be selected for further analysis. To date, many computational methods have focused on the prediction of candidate genes by analysis of their inherent sequence characteristics and similarity with respect to known disease genes, as well as their functional annotation. In the last decade, several computational tools for prioritizing candidate genes have been proposed. A large number of them are web-based tools, while others are standalone applications that install and run locally. This review attempts to take a close look at gene prioritization criteria, as well as candidate gene prioritization algorithms, and thus provide a comprehensive synopsis of the subject matter.

Pathway-Dependent Effectiveness of Network Algorithms for Gene Prioritization.

A network-based approach has proven useful for the identification of novel genes associated with complex phenotypes, including human diseases. Because network-based gene prioritization algorithms are based on propagating information of known phenotype-associated genes through networks, the pathway structure of each phenotype might significantly affect the effectiveness of algorithms. We systematically compared two popular network algorithms with distinct mechanisms – direct neighborhood which propagates information to only direct network neighbors, and network diffusion which diffuses information throughout the entire network – in prioritization of genes for worm and human phenotypes. Previous studies reported that network diffusion generally outperforms direct neighborhood for human diseases. Although prioritization power is generally measured for all ranked genes, only the top candidates are significant for subsequent functional analysis. We found that high prioritizing power of a network algorithm for all genes cannot guarantee successful prioritization of top ranked candidates for a given phenotype. Indeed, the majority of the phenotypes that were more efficiently prioritized by network diffusion showed higher prioritizing power for top candidates by direct neighborhood. We also found that connectivity among pathway genes for each phenotype largely determines which network algorithm is more effective, suggesting that the network algorithm used for each phenotype should be chosen with consideration of pathway gene connectivity.

Conservation biogeography of the Cerrado's wild edible plants under climate change: Linking biotic stability with agricultural expansion.

REMISE OF THE STUDY: Wild edible plants (WEPs) have an important cultural and economic role in human population worldwide. Human impacts are quickly converting natural habitats in agricultural, cattle ranch, and urbanized lands, putting native species on peril of risk of extinction, including some WEPs. Moreover, global climate changes also can pose another threat to species persistency. Here, we established conservation priorities for the Cerrado, a neotropical region in South America with high levels of plant endemism and vulnerability, aiming to assure long-term persistency of 16 most important WEPs. We evaluated these conservation priorities using a conservation biogeography framework using ecological patterns and process at a biogeographical scale to deal with species conservation features. We built ecological niche models for 16 WEPs from Cerrado in the neotropics using climate models for preindustrial, past (Last Glacial Maximum) and future (year 2080) time periods to establish climatically stable areas through time, finding refugias for these WEPs. We used a spatial prioritization algorithm based on the spatial pattern of irreplaceability across the neotropics, aiming to ensure the persistence of at least 25% of range size in climatically stable areas for each WEP, using agricultural models as constraints. The Southeast Cerrado was the most biotically stable and irreplaceable region for the WEPs compared with other areas across the neotropics. Our findings strongly suggest that the Southeast Cerrado should be considered a conservation priority, with new protected areas to be sustainably managed and restored, to guarantee the supply of cultural and ecosystem services provided from the Cerrado's WEPs.

Multi-action planning for threat management: a novel approach for the spatial prioritization of conservation actions.

Planning for the remediation of multiple threats is crucial to ensure the long term persistence of biodiversity. Limited conservation budgets require prioritizing which management actions to implement and where. Systematic conservation planning traditionally assumes that all the threats in priority sites are abated (fixed prioritization approach). However, abating only the threats affecting the species of conservation concerns may be more cost-effective. This requires prioritizing individual actions independently within the same site (independent prioritization approach), which has received limited attention so far. We developed an action prioritization algorithm that prioritizes multiple alternative actions within the same site. We used simulated annealing to find the combination of actions that remediate threats to species at the minimum cost. Our algorithm also accounts for the importance of selecting actions in sites connected through the river network (i.e., connectivity). We applied our algorithm to prioritize actions to address threats to freshwater fish species in the Mitchell River catchment, northern Australia. We compared how the efficiency of the independent and fixed prioritization approach varied as the importance of connectivity increased. Our independent prioritization approach delivered more efficient solutions than the fixed prioritization approach, particularly when the importance of achieving connectivity was high. By spatially prioritizing the specific actions necessary to remediate the threats affecting the target species, our approach can aid cost-effective habitat restoration and land-use planning. It is also particularly suited to solving resource allocation problems, where consideration of spatial design is important, such as prioritizing conservation efforts for highly mobile species, species facing climate change-driven range shifts, or minimizing the risk of threats spreading across different realms.

Implementation of Regression Testing of Test Case Prioritization

Background: In this paper, we described the regression testing of test case prioritization. Statistical analysis: To categorize the cruel mistakes and get better the rate of fault detection, test case prioritization algorithm is proposed. Result: It prioritizes the test cases depends on few sets of realistic load aspect such as: changes in requirements, traceability, completeness, developer observed code execution complexity, fault impact and customer allotted priority. Conclusion: The implementation of proposed regression testing method is attained by comparing it with other methods.

Improving effectiveness of systematic conservation planning with density data

Systematic conservation planning aims to design networks of protected areas that meet conservation goals across large landscapes. The optimal design of these conservation networks is most frequently based on the modeled habitat suitability or probability of occurrence of species, despite evidence that model predictions may not be highly correlated with species density. We hypothesized that conservation networks designed using species density distributions more efficiently conserve populations of all species considered than networks designed using probability of occurrence models. To test this hypothesis, we used the Zonation conservation prioritization algorithm to evaluate conservation network designs based on probability of occurrence versus density models for 26 land bird species in the U.S. Pacific Northwest. We assessed the efficacy of each conservation network based on predicted species densities and predicted species diversity. High-density model Zonation rankings protected more individuals per species when networks protected the highest priority 10-40% of the landscape. Compared with density-based models, the occurrence-based models protected more individuals in the lowest 50% priority areas of the landscape. The 2 approaches conserved species diversity in similar ways: predicted diversity was higher in higher priority locations in both conservation networks. We conclude that both density and probability of occurrence models can be useful for setting conservation priorities but that density-based models are best suited for identifying the highest priority areas. Developing methods to aggregate species count data from unrelated monitoring efforts and making these data widely available through ecoinformatics portals such as the Avian Knowledge Network will enable species count data to be more widely incorporated into systematic conservation planning efforts.