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  • 1
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    Identifying CDKN3 Gene Expression as a Prognostic Biomarker in Lung Adenocarcinoma via Meta-analysis (2015)
    Libertas Academica
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    Publication Date: 2015-05-26
    Description: Lung cancer is among the major causes of cancer deaths, and the survival rate of lung cancer patients is extremely low. Recent studies have demonstrated that the gene CDKN3 is related to neoplasia, but in the literature severe controversy exists over whether it is involved in cancer progression or, conversely, tumor inhibition. In this study, we investigated the expression of CDKN3 and its association with prognosis in lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) using datasets in Lung Cancer Explorer (LCE; http://qbrc.swmed.edu/lce/). We found that CDKN3 was up-regulated in ADC and SCC compared to normal tissues. We also found that CDKN3 was expressed at a higher level in SCC than in ADC, which was further validated through meta-analysis (coefficient = 2.09, 95% CI = 1.50–2.67, P 〈 0.0001). In addition, based on meta-analysis for the prognostic value of CDKN3, we found that higher CDKN3 expression was associated with poorer survival outcomes in ADC (HR = 1.65, 95% CI = 1.39–1.96, P 〈 0.0001), but not in SCC (HR = 1.10, 95% CI = 0.84–1.44, P = 0.494). Our findings indicate that CDKN3 may be a prognostic marker in ADC, though the detailed mechanism is yet to be revealed.
    Electronic ISSN: 1176-9351
    Topics: Computer Science , Medicine
    Published by Libertas Academica
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  • 2
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    Simulation of the Protein-Shedding Kinetics of a Fully Vascularized Tumor (2015)
    Libertas Academica
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    Publication Date: 2015-12-21
    Description: Circulating biomarkers are of significant interest for cancer detection and treatment personalization. However, the biophysical processes that determine how proteins are shed from cancer cells or their microenvironment, diffuse through tissue, enter blood vasculature, and persist in circulation remain poorly understood. Since approaches primarily focused on experimental evaluation are incapable of measuring the shedding and persistence for every possible marker candidate, we propose an interdisciplinary computational/experimental approach that includes computational modeling of tumor tissue heterogeneity. The model implements protein production, transport, and shedding based on tumor vascularization, cell proliferation, hypoxia, and necrosis, thus quantitatively relating the tumor and circulating proteomes. The results highlight the dynamics of shedding as a function of protein diffusivity and production. Linking the simulated tumor parameters to clinical tumor and vascularization measurements could potentially enable this approach to reveal the tumor-specific conditions based on the protein detected in circulation and thus help to more accurately manage cancer diagnosis and treatment.
    Electronic ISSN: 1176-9351
    Topics: Computer Science , Medicine
    Published by Libertas Academica
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  • 3
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    Prognostic Gene Signature Identification Using Causal Structure Learning: Applications in Kidney Cancer (2015)
    Libertas Academica
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    Publication Date: 2015-01-29
    Description: Identification of molecular-based signatures is one of the critical steps toward finding therapeutic targets in cancer. In this paper, we propose methods to discover prognostic gene signatures under a causal structure learning framework across the whole genome. The causal structures are represented by directed acyclic graphs (DAGs), wherein we construct gene-specific network modules that constitute a gene and its corresponding regulators. The modules are then subsequently used to correlate with survival times, thus, allowing for a network-oriented approach to gene selection to adjust for potential confounders, as opposed to univariate (gene-by-gene) approaches. Our methods are motivated by and applied to a clear cell renal cell carcinoma (ccRCC) study from The Cancer Genome Atlas (TCGA) where we find several prognostic genes associated with cancer progression – some of which are novel while others confirm existing findings.
    Electronic ISSN: 1176-9351
    Topics: Computer Science , Medicine
    Published by Libertas Academica
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  • 4
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    Algorithms, Vol. 8, Pages 484-513: Multi-Feedback Interference Cancellation Algorithms for OFDM Systems over Doubly-Selective Channels (2015)
    MDPI Publishing
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    Publication Date: 2015-07-15
    Description: Orthogonal frequency-division multiplexing (OFDM) systems over rapidly time varying channels may suffer from significant inter-carrier interference (ICI), which destroys the orthogonality between subcarriers and degrades the detection performance. Without sufficient ICI suppression, OFDM systems usually experience an error floor. According to the approximate matched filter bound (AMFB), the error floor in a coded OFDM system is not irreducible. In this work, we introduce novel multiple feedback matched filter (MBMF)-based ICI cancellation receivers. Based on the output of a novel MBMF scheme, the approach employs a multiple ICI cancellation strategy with or without signal-to-interference-plus-noise-ratio (SINR) ordering. The developed schemes can significantly improve the performance and remove the error floor with a negligible complexity increase. Given the multiple cancellation approach, we compare the SINR performance of the MBMF outputs with that employing single feedback and show that the SINR performance with multiple cancellation candidates is improved over that with a single one at practical SNR values. Additionally, for time-varying channels, we exploit partial fast Fourier transform (PFFT) by splitting one OFDM symbol into multiple segments; the channel state is separately estimated by least-squares (LS) methods without inserting more pilots. Simulation results demonstrate the superiority of the proposed methods over serial and block equalizers and the robustness to the Doppler effects compared to conventional single-segment method.
    Electronic ISSN: 1999-4893
    Topics: Computer Science
    Published by MDPI Publishing
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  • 5
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    Algorithms, Vol. 7, Pages 15-31: Bio-Inspired Meta-Heuristics for Emergency Transportation Problems (2014)
    MDPI Publishing
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    Publication Date: 2014-02-12
    Description: Emergency transportation plays a vital role in the success of disaster rescue and relief operations, but its planning and scheduling often involve complex objectives and search spaces. In this paper, we conduct a survey of recent advances in bio-inspired meta-heuristics, including genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), etc., for solving emergency transportation problems. We then propose a new hybrid biogeography-based optimization (BBO) algorithm, which outperforms some state-of-the-art heuristics on a typical transportation planning problem.
    Electronic ISSN: 1999-4893
    Topics: Computer Science
    Published by MDPI Publishing
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  • 6
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    In Silico Prediction of Synthetic Lethality by Meta-Analysis of Genetic Interactions, Functions, and Pathways in Yeast and Human Cancer (2014)
    Libertas Academica
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    Publication Date: 2014-11-06
    Description: A major goal in cancer medicine is to find selective drugs with reduced side effect. A pair of genes is called synthetic lethality (SL) if mutations of both genes will kill a cell while mutation of either gene alone will not. Hence, a gene in SL interactions with a cancer-specific mutated gene will be a promising drug target with anti-cancer selectivity. Wet-lab screening approach is still so costly that even for yeast only a small fraction of gene pairs has been covered. Computational methods are therefore important for large-scale discovery of SL interactions. Most existing approaches focus on individual features or machine-learning methods, which are prone to noise or overfitting. In this paper, we propose an approach named MetaSL for predicting yeast SL, which integrates 17 genomic and proteomic features and the outputs of 10 classification methods. MetaSL thus combines the strengths of existing methods and achieves the highest area under the Receiver Operating Characteristics (ROC) curve (AUC) of 87.1% among all competitors on yeast data. Moreover, through orthologous mapping from yeast to human genes, we then predicted several lists of candidate SL pairs in human cancer. Our method and predictions would thus shed light on mechanisms of SL and lead to discovery of novel anti-cancer drugs. In addition, all the experimental results can be downloaded from http://www.ntu.edu.sg/home/zhengjie/data/MetaSL.
    Electronic ISSN: 1176-9351
    Topics: Computer Science , Medicine
    Published by Libertas Academica
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  • 7
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    CARAT-GxG: CUDA-Accelerated Regression Analysis Toolkit for Large-Scale Gene–Gene Interaction with GPU Computing System (2014)
    Libertas Academica
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    Publication Date: 2014-12-10
    Description: In genome-wide association studies (GWAS), regression analysis has been most commonly used to establish an association between a phenotype and genetic variants, such as single nucleotide polymorphism (SNP). However, most applications of regression analysis have been restricted to the investigation of single marker because of the large computational burden. Thus, there have been limited applications of regression analysis to multiple SNPs, including gene–gene interaction (GGI) in large-scale GWAS data. In order to overcome this limitation, we propose CARAT-GxG, a GPU computing system-oriented toolkit, for performing regression analysis with GGI using CUDA (compute unified device architecture). Compared to other methods, CARAT-GxG achieved almost 700-fold execution speed and delivered highly reliable results through our GPU-specific optimization techniques. In addition, it was possible to achieve almost-linear speed acceleration with the application of a GPU computing system, which is implemented by the TORQUE Resource Manager. We expect that CARAT-GxG will enable large-scale regression analysis with GGI for GWAS data.
    Electronic ISSN: 1176-9351
    Topics: Computer Science , Medicine
    Published by Libertas Academica
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  • 8
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    Algorithms, Vol. 11, Pages 85: ILC with Initial State Learning for Fractional Order Linear Distributed Parameter Systems (2018)
    MDPI Publishing
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    Publication Date: 2018-06-15
    Description: Algorithms, Vol. 11, Pages 85: ILC with Initial State Learning for Fractional Order Linear Distributed Parameter Systems Algorithms doi: 10.3390/a11060085 Authors: Yong-Hong Lan Zhe-Min Cui This paper presents a second order P-type iterative learning control (ILC) scheme with initial state learning for a class of fractional order linear distributed parameter systems. First, by analyzing the control and learning processes, a discrete system for P-type ILC is established, and the ILC design problem is then converted to a stability problem for such a discrete system. Next, a sufficient condition for the convergence of the control input and the tracking errors is obtained by introducing a new norm and using the generalized Gronwall inequality, which is less conservative than the existing one. Finally, the validity of the proposed method is verified by a numerical example.
    Electronic ISSN: 1999-4893
    Topics: Computer Science
    Published by MDPI Publishing
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  • 9
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    Future Internet, Vol. 4, Pages 1069-1085: A Web-Based Geovisual Analytical System for Climate Studies (2012)
    MDPI Publishing
    Details
    Publication Date: 2012-12-15
    Description: Climate studies involve petabytes of spatiotemporal datasets that are produced and archived at distributed computing resources. Scientists need an intuitive and convenient tool to explore the distributed spatiotemporal data. Geovisual analytical tools have the potential to provide such an intuitive and convenient method for scientists to access climate data, discover the relationships between various climate parameters, and communicate the results across different research communities. However, implementing a geovisual analytical tool for complex climate data in a distributed environment poses several challenges. This paper reports our research and development of a web-based geovisual analytical system to support the analysis of climate data generated by climate model. Using the ModelE developed by the NASA Goddard Institute for Space Studies (GISS) as an example, we demonstrate that the system is able to (1) manage large volume datasets over the Internet; (2) visualize 2D/3D/4D spatiotemporal data; (3) broker various spatiotemporal statistical analyses for climate research; and (4) support interactive data analysis and knowledge discovery. This research also provides an example for managing, disseminating, and analyzing Big Data in the 21st century.
    Electronic ISSN: 1999-5903
    Topics: Computer Science
    Published by MDPI Publishing
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  • 10
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    Future Internet, Vol. 9, Pages 13: Feature-Based Image Watermarking Algorithm Using SVD and APBT for Copyright Protection (2017)
    MDPI Publishing
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    Publication Date: 2017-04-20
    Description: Watermarking techniques can be applied in digital images to maintain the authenticity and integrity for copyright protection. In this paper, scale-invariant feature transform (SIFT) is combined with local digital watermarking and a digital watermarking algorithm based on SIFT, singular value decomposition (SVD), and all phase biorthogonal transform (APBT) is proposed. It describes the generation process of the SIFT algorithm in detail and obtains a series of scale-invariant feature points. A large amount of candidate feature points are selected to obtain the neighborhood which can be used to embed the watermark. For these selected feature points, block-based APBT is carried out on their neighborhoods. Moreover, a coefficients matrix of certain APBT coefficients is generated for SVD to embed the encrypted watermark. Experimental results demonstrate that the proposed watermarking algorithm has stronger robustness than some previous schemes. In addition, APBT-based digital watermarking algorithm has good imperceptibility and is more robust to different combinations of attacks, which can be applied for the purpose of copyright protection.
    Electronic ISSN: 1999-5903
    Topics: Computer Science
    Published by MDPI Publishing
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