Factor analysis for gene regulatory networks and transcription factor activity profiles

Pournara, Iosifina; Wernisch, Lorenz

doi:10.1186/1471-2105-8-61

Cited by 82 publications

(76 citation statements)

References 16 publications

(47 reference statements)

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“…Principal component analysis (PCA) is used to select the representative factors of performance indicators from the data set basically, and not to uncover the hidden relationships. Pournara and Wernisch (2007) emphasise that PCA can be used for data reduction by evaluating for the smallest possible set of principal components that can explain most of the variances in the data set. …”

Section: Discussionmentioning

confidence: 99%

Modeling for incentive payoffs in the Nigerian construction industry

Ogwueleka

Maritz

2016

JEDT

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Incentives are mechanisms used to create genuine opportunity for contracting parties to work together to achieve good results, rational returns and bear appropriate risks. The question of how to motivate the construction workforce rightly so as to achieve best performance has remained paramount to project owners. This paper investigates on how to model for incentive payoffs in the Nigerian construction industry in order to effectively utilise the benefits of incentive mechanisms. The collected data are analysed using descriptive and inferential statistics, such as frequency counts, charts and principal component analysis. The findings reveal the metrics for measuring organisational incentive payoff and the scaling factor for each metric. The study further develops the employee incentive payoff models for both operational workers and management staff in the construction sector. This study provides a practical solution to the application of incentive mechanisms in construction projects. The paper recommends the need for restructuring of incentive mechanisms to significantly impact on other performance criteria therefore contributing to best performance in project delivery.Keywords: incentive payoffs, performance improvement, partnering, alliance contracting and Nigerian construction industry IntroductionContracting has become an important avenue for service delivery. Researchers and project practitioners have sought for various ways to outsource goods and services in order to improve project performance. The use of performance-based contracting systems has been overwhelming whereby contractors are rewarded for good performance and penalised for failing to achieve project outcomes (Smith and Grinker, 2003;Martin, 2007). Regardless of this practice, clients are often concerned with obtaining best performance from contractors within reasonable prices by stating the objectives and specifying the scope and metrics for achieving the project goals with less emphasis on how they are to be achieved. Most limitations associated with project delivery are centered on improper alignment of contractors' objectives with clients' expectations. Partnering was introduced in the late 1980's to facilitate an environment of trust where contracting parties can share a lot of objectives (Tang et al., 2006). The priorities of contracting parties are different; therefore the use of partnering alone cannot promote good performance but might create a platform that is prone to recurring disputes (Hosie, 2001).Researchers have advocated the need of a formal agreement which can promote mutual understanding, effective knowledge sharing and good working relationships amongst contracting parties (Popp and Zenger, 2002;Vlaar et al., 2006). The use of incentives in contracts has allowed for effective sharing of resources, risks and profits/losses measured against the performance indicators amongst contracting parties based on the contract agreement. Incentives encourage contracting parties to work together in a mutual agreement in order t...

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Section: Discussionmentioning

confidence: 99%

Modeling for incentive payoffs in the Nigerian construction industry

Ogwueleka

Maritz

2016

JEDT

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“…The process of finding the unmixing matrix can be performed by different algorithms, based on different metrics of statistical independence. Pournara and Wernisch provided a thorough review of ICA and other factor analysis approaches in TRN estimation [28]. …”

Section: Review Of Matrix Factorization Methodsmentioning

confidence: 99%

Matrix factorization for transcriptional regulatory network inference

Ochs

Fertig

2012

2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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Inference of Transcriptional Regulatory Networks (TRNs) provides insight into the mechanisms driving biological systems, especially mammalian development and disease. Many techniques have been developed for TRN estimation from indirect biochemical measurements. Although successful when initially tested in model organisms, these regulatory models often fail when applied to data from multicellular organisms where multiple regulation and gene reuse increase dramatically. Non-negative matrix factorization techniques were initially introduced to find non-orthogonal patterns in data, making them ideal techniques for inference in cases of multiple regulation. We review these techniques and their application to TRN analysis.

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“…Despite different motivations, these methods all share the same general modeling form. Pournara and Wernisch [134] compared some methods in this context.…”

Section: Learning Transcriptional Network: Past Studies and Future Pmentioning

confidence: 98%

Codimensional matrix pairing perspective of BYY harmony learning: hierarchy of bilinear systems, joint decomposition of data-covariance, and applications of network biology

2011

Front. Electr. Electron. Eng. China

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One paper in a preceding issue of this journal has introduced the Bayesian Ying-Yang (BYY) harmony learning from a perspective of problem solving, parameter learning, and model selection. In a complementary role, the paper provides further insights from another perspective that a co-dimensional matrix pair (shortly co-dim matrix pair) forms a building unit and a hierarchy of such building units sets up the BYY system. The BYY harmony learning is re-examined via exploring the nature of a co-dim matrix pair, which leads to improved learning performance with refined model selection criteria and a modified mechanism that coordinates automatic model selection and sparse learning. Besides updating typical algorithms of factor analysis (FA), binary FA (BFA), binary matrix factorization (BMF), and nonnegative matrix factorization (NMF) to share such a mechanism, we are also led to (a) a new parametrization that embeds a de-noise nature to Gaussian mixture and local FA (LFA); (b) an alternative formulation of graph Laplacian based linear manifold learning; (c) a codecomposition of data and covariance for learning regularization and data integration; and (d) a co-dim matrix pair based generalization of temporal FA and state space model. Moreover, with help of a co-dim matrix pair in Hadamard product, we are led to a semi-supervised formation for regression analysis and a semi-blind learning formation for temporal FA and state space model. Furthermore, we address that these advances provide with new tools for network biology studies, including learning transcriptional regulatory, Protein-Protein Interaction network alignment, and network integration.

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Factor analysis for gene regulatory networks and transcription factor activity profiles

Cited by 82 publications

References 16 publications

Modeling for incentive payoffs in the Nigerian construction industry

Modeling for incentive payoffs in the Nigerian construction industry

Matrix factorization for transcriptional regulatory network inference

Codimensional matrix pairing perspective of BYY harmony learning: hierarchy of bilinear systems, joint decomposition of data-covariance, and applications of network biology

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