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Book ; Online: Selection Combining over Log-Logistic Fading Channels with Applications to Underwater Optical Wireless Communications

Al-Badarneh, Yazan H. / Alshawaqfeh, Mustafa K. / Badarneh, Osamah S.

2023

Abstract: We study the performance of a selection combining (SC) receiver operating over independent but non-identically distributed log-logistic ($\mathcal{LL})$ fading channels. We first characterize the statistics of the output instantaneous signal-to-noise ... ...

Abstract	We study the performance of a selection combining (SC) receiver operating over independent but non-identically distributed log-logistic ($\mathcal{LL})$ fading channels. We first characterize the statistics of the output instantaneous signal-to-noise ratio (SNR) of the SC receiver. Based on the SNR statistics, we derive exact analytical expressions, in terms of multivariate Fox H-functions, for the outage probability, the average bit error rate, and the ergodic capacity. We also derive exact expressions for such performance measures when all channels are independent and identically distributed, as a special case. Furthermore, we deduce simplified asymptotic expressions for these performance metrics assuming high values of average transmit SNR. To demonstrate the applicability of our theoretical analysis, we study the performance of an SC receiver in underwater optical wireless communication systems. Finally, we confirm the correctness of the derived analytical results using Monte Carlo Simulations. Comment: Accepted for publication in IEEE Wireless Communications Letters
Keywords	Computer Science - Information Theory ; Electrical Engineering and Systems Science - Signal Processing
Subject code	003
Publishing date	2023-06-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Inferring microbial interaction networks from metagenomic data using SgLV-EKF algorithm.

Alshawaqfeh, Mustafa / Serpedin, Erchin / Younes, Ahmad Bani

BMC genomics

2017 Volume 18, Issue Suppl 3, Page(s) 228

Abstract: Background: Inferring the microbial interaction networks (MINs) and modeling their dynamics are critical in understanding the mechanisms of the bacterial ecosystem and designing antibiotic and/or probiotic therapies. Recently, several approaches were ... ...

Abstract	Background: Inferring the microbial interaction networks (MINs) and modeling their dynamics are critical in understanding the mechanisms of the bacterial ecosystem and designing antibiotic and/or probiotic therapies. Recently, several approaches were proposed to infer MINs using the generalized Lotka-Volterra (gLV) model. Main drawbacks of these models include the fact that these models only consider the measurement noise without taking into consideration the uncertainties in the underlying dynamics. Furthermore, inferring the MIN is characterized by the limited number of observations and nonlinearity in the regulatory mechanisms. Therefore, novel estimation techniques are needed to address these challenges. Results: This work proposes SgLV-EKF: a stochastic gLV model that adopts the extended Kalman filter (EKF) algorithm to model the MIN dynamics. In particular, SgLV-EKF employs a stochastic modeling of the MIN by adding a noise term to the dynamical model to compensate for modeling uncertainties. This stochastic modeling is more realistic than the conventional gLV model which assumes that the MIN dynamics are perfectly governed by the gLV equations. After specifying the stochastic model structure, we propose the EKF to estimate the MIN. SgLV-EKF was compared with two similarity-based algorithms, one algorithm from the integral-based family and two regression-based algorithms, in terms of the achieved performance on two synthetic data-sets and two real data-sets. The first data-set models the randomness in measurement data, whereas, the second data-set incorporates uncertainties in the underlying dynamics. The real data-sets are provided by a recent study pertaining to an antibiotic-mediated Clostridium difficile infection. The experimental results demonstrate that SgLV-EKF outperforms the alternative methods in terms of robustness to measurement noise, modeling errors, and tracking the dynamics of the MIN. Conclusions: Performance analysis demonstrates that the proposed SgLV-EKF algorithm represents a powerful and reliable tool to infer MINs and track their dynamics.
MeSH term(s)	Algorithms ; Metagenomics/methods ; Microbial Interactions ; Models, Theoretical
Language	English
Publishing date	2017--27
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1471-2164
ISSN (online)	1471-2164
DOI	10.1186/s12864-017-3605-x
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Article ; Online: Reliable Biomarker discovery from Metagenomic data via RegLRSD algorithm.

Alshawaqfeh, Mustafa / Bashaireh, Ahmad / Serpedin, Erchin / Suchodolski, Jan

BMC bioinformatics

2017 Volume 18, Issue 1, Page(s) 328

Abstract: Background: Biomarker detection presents itself as a major means of translating biological data into clinical applications. Due to the recent advances in high throughput sequencing technologies, an increased number of metagenomics studies have suggested ...

Abstract	Background: Biomarker detection presents itself as a major means of translating biological data into clinical applications. Due to the recent advances in high throughput sequencing technologies, an increased number of metagenomics studies have suggested the dysbiosis in microbial communities as potential biomarker for certain diseases. The reproducibility of the results drawn from metagenomic data is crucial for clinical applications and to prevent incorrect biological conclusions. The variability in the sample size and the subjects participating in the experiments induce diversity, which may drastically change the outcome of biomarker detection algorithms. Therefore, a robust biomarker detection algorithm that ensures the consistency of the results irrespective of the natural diversity present in the samples is needed. Results: Toward this end, this paper proposes a novel Regularized Low Rank-Sparse Decomposition (RegLRSD) algorithm. RegLRSD models the bacterial abundance data as a superposition between a sparse matrix and a low-rank matrix, which account for the differentially and non-differentially abundant microbes, respectively. Hence, the biomarker detection problem is cast as a matrix decomposition problem. In order to yield more consistent and solid biological conclusions, RegLRSD incorporates the prior knowledge that the irrelevant microbes do not exhibit significant variation between samples belonging to different phenotypes. Moreover, an efficient algorithm to extract the sparse matrix is proposed. Comprehensive comparisons of RegLRSD with the state-of-the-art algorithms on three realistic datasets are presented. The obtained results demonstrate that RegLRSD consistently outperforms the other algorithms in terms of reproducibility performance and provides a marker list with high classification accuracy. Conclusions: The proposed RegLRSD algorithm for biomarker detection provides high reproducibility and classification accuracy performance regardless of the dataset complexity and the number of selected biomarkers. This renders RegLRSD as a reliable and powerful tool for identifying potential metagenomic biomarkers.
MeSH term(s)	Algorithms ; Animals ; Biomarkers/analysis ; Biomarkers/metabolism ; Colitis, Ulcerative/diagnosis ; Colitis, Ulcerative/metabolism ; Dogs ; Exocrine Pancreatic Insufficiency/diagnosis ; Exocrine Pancreatic Insufficiency/metabolism ; High-Throughput Nucleotide Sequencing ; Inflammatory Bowel Diseases/diagnosis ; Inflammatory Bowel Diseases/metabolism ; Metagenomics/methods ; Mice ; Reproducibility of Results
Chemical Substances	Biomarkers
Language	English
Publishing date	2017-07-10
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-017-1738-1
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Article ; Online: Consistent metagenomic biomarker detection via robust PCA.

Alshawaqfeh, Mustafa / Bashaireh, Ahmad / Serpedin, Erchin / Suchodolski, Jan

Biology direct

2017 Volume 12, Issue 1, Page(s) 4

Abstract: Background: Recent developments of high throughput sequencing technologies allow the characterization of the microbial communities inhabiting our world. Various metagenomic studies have suggested using microbial taxa as potential biomarkers for certain ... ...

Abstract	Background: Recent developments of high throughput sequencing technologies allow the characterization of the microbial communities inhabiting our world. Various metagenomic studies have suggested using microbial taxa as potential biomarkers for certain diseases. In practice, the number of available samples varies from experiment to experiment. Therefore, a robust biomarker detection algorithm is needed to provide a set of potential markers irrespective of the number of available samples. Consistent performance is essential to derive solid biological conclusions and to transfer these findings into clinical applications. Surprisingly, the consistency of a metagenomic biomarker detection algorithm with respect to the variation in the experiment size has not been addressed by the current state-of-art algorithms. Results: We propose a consistency-classification framework that enables the assessment of consistency and classification performance of a biomarker discovery algorithm. This evaluation protocol is based on random resampling to mimic the variation in the experiment size. Moreover, we model the metagenomic data matrix as a superposition of two matrices. The first matrix is a low-rank matrix that models the abundance levels of the irrelevant bacteria. The second matrix is a sparse matrix that captures the abundance levels of the bacteria that are differentially abundant between different phenotypes. Then, we propose a novel Robust Principal Component Analysis (RPCA) based biomarker discovery algorithm to recover the sparse matrix. RPCA belongs to the class of multivariate feature selection methods which treat the features collectively rather than individually. This provides the proposed algorithm with an inherent ability to handle the complex microbial interactions. Comprehensive comparisons of RPCA with the state-of-the-art algorithms on two realistic datasets are conducted. Results show that RPCA consistently outperforms the other algorithms in terms of classification accuracy and reproducibility performance. Conclusions: The RPCA-based biomarker detection algorithm provides a high reproducibility performance irrespective of the complexity of the dataset or the number of selected biomarkers. Also, RPCA selects biomarkers with quite high discriminative accuracy. Thus, RPCA is a consistent and accurate tool for selecting taxanomical biomarkers for different microbial populations. Reviewers: This article was reviewed by Masanori Arita and Zoltan Gaspari.
MeSH term(s)	Algorithms ; Animals ; Bacteria/classification ; Bacteria/genetics ; Colitis, Ulcerative/diagnosis ; Colitis, Ulcerative/microbiology ; Dog Diseases/diagnosis ; Dog Diseases/microbiology ; Dogs ; Genetic Markers ; Inflammatory Bowel Diseases/diagnosis ; Inflammatory Bowel Diseases/microbiology ; Inflammatory Bowel Diseases/veterinary ; Metagenomics/methods ; Mice ; Microbiota/genetics ; Models, Genetic ; Principal Component Analysis/methods ; Reproducibility of Results
Chemical Substances	Genetic Markers
Language	English
Publishing date	2017-01-31
Publishing country	England
Document type	Journal Article
ISSN	1745-6150
ISSN (online)	1745-6150
DOI	10.1186/s13062-017-0175-4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Robust Recurrent CNV Detection in the Presence of Inter-Subject Variability.

Alshawaqfeh, Mustafa / Al Kawam, Ahmad / Serpedin, Erchin / Datta, Aniruddha

IEEE/ACM transactions on computational biology and bioinformatics

2018 Volume 17, Issue 3, Page(s) 1056–1067

Abstract: The study of recurrent copy number variations (CNVs) plays an important role in understanding the onset and evolution of complex diseases such as cancer. Array-based comparative genomic hybridization (aCGH) is a widely used microarray based technology ... ...

Abstract	The study of recurrent copy number variations (CNVs) plays an important role in understanding the onset and evolution of complex diseases such as cancer. Array-based comparative genomic hybridization (aCGH) is a widely used microarray based technology for identifying CNVs. However, due to high noise levels and inter-sample variability, detecting recurrent CNVs from aCGH data remains a challenging topic. This paper proposes a novel method for identification of the recurrent CNVs. In the proposed method, the noisy aCGH data is modeled as the superposition of three matrices: a full-rank matrix of weighted piece-wise generating signals accounting for the clean aCGH data, a Gaussian noise matrix to model the inherent experimentation errors and other sources of error, and a sparse matrix to capture the sparse inter-sample (sample-specific) variations. We demonstrated the ability of our method to separate accurately recurrent CNVs from sample-specific variations and noise in both simulated (artificial) data and real data. The proposed method produced more accurate results than current state-of-the-art methods used in recurrent CNV detection and exhibited robustness to noise and sample-specific variations.
MeSH term(s)	Comparative Genomic Hybridization ; Computational Biology/methods ; DNA Copy Number Variations/genetics ; Databases, Genetic ; Humans ; Models, Genetic
Language	English
Publishing date	2018-10-30
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1557-9964
ISSN (online)	1557-9964
DOI	10.1109/TCBB.2018.2878560
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Article ; Online: Correction to: Music of metagenomics-a review of its applications, analysis pipeline, and associated tools.

Wajid, Bilal / Anwar, Faria / Wajid, Imran / Nisar, Haseeb / Meraj, Sharoze / Zafar, Ali / Al-Shawaqfeh, Mustafa Kamal / Ekti, Ali Riza / Khatoon, Asia / Suchodolski, Jan S

Functional & integrative genomics

2021 Volume 22, Issue 1, Page(s) 137

Language	English
Publishing date	2021-11-01
Publishing country	Germany
Document type	Published Erratum
ZDB-ID	2014670-X
ISSN	1438-7948 ; 1438-793X
ISSN (online)	1438-7948
ISSN	1438-793X
DOI	10.1007/s10142-021-00820-w
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Article: Music of metagenomics—a review of its applications, analysis pipeline, and associated tools [Erratum: February 2022, v.22(1); p.137]

Wajid, Bilal / Anwar, Faria / Wajid, Imran / Nisar, Haseeb / Meraj, Sharoze / Zafar, Ali / Al-Shawaqfeh, Mustafa Kamal / Ekti, Ali Riza / Khatoon, Asia / Suchodolski, Jan S.

Functional & integrative genomics. 2022 Feb., v. 22, no. 1

2022

Abstract: This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of ... ...

Abstract	This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the “metagenome.”
Keywords	ecosystems ; genome ; metagenomics ; music ; taxonomy
Language	English
Dates of publication	2022-02
Size	p. 3-26.
Publishing place	Springer Berlin Heidelberg
Document type	Article
Note	Review
ZDB-ID	2014670-X
ISSN	1438-7948 ; 1438-793X
ISSN (online)	1438-7948
ISSN	1438-793X
DOI	10.1007/s10142-021-00810-y
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Music of metagenomics-a review of its applications, analysis pipeline, and associated tools.

Wajid, Bilal / Anwar, Faria / Wajid, Imran / Nisar, Haseeb / Meraj, Sharoze / Zafar, Ali / Al-Shawaqfeh, Mustafa Kamal / Ekti, Ali Riza / Khatoon, Asia / Suchodolski, Jan S

Functional & integrative genomics

2021 Volume 22, Issue 1, Page(s) 3–26

Abstract	This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."
MeSH term(s)	High-Throughput Nucleotide Sequencing ; Metagenome ; Metagenomics/methods ; Software
Language	English
Publishing date	2021-10-18
Publishing country	Germany
Document type	Journal Article ; Review
ZDB-ID	2014670-X
ISSN	1438-7948 ; 1438-793X
ISSN (online)	1438-7948
ISSN	1438-793X
DOI	10.1007/s10142-021-00810-y
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Article ; Online: Simulating variance heterogeneity in quantitative genome wide association studies.

Al Kawam, Ahmad / Alshawaqfeh, Mustafa / Cai, James J / Serpedin, Erchin / Datta, Aniruddha

BMC bioinformatics

2018 Volume 19, Issue Suppl 3, Page(s) 72

Abstract: Background: Analyzing Variance heterogeneity in genome wide association studies (vGWAS) is an emerging approach for detecting genetic loci involved in gene-gene and gene-environment interactions. vGWAS analysis detects variability in phenotype values ... ...

Abstract	Background: Analyzing Variance heterogeneity in genome wide association studies (vGWAS) is an emerging approach for detecting genetic loci involved in gene-gene and gene-environment interactions. vGWAS analysis detects variability in phenotype values across genotypes, as opposed to typical GWAS analysis, which detects variations in the mean phenotype value. Results: A handful of vGWAS analysis methods have been recently introduced in the literature. However, very little work has been done for evaluating these methods. To enable the development of better vGWAS analysis methods, this work presents the first quantitative vGWAS simulation procedure. To that end, we describe the mathematical framework and algorithm for generating quantitative vGWAS phenotype data from genotype profiles. Our simulation model accounts for both haploid and diploid genotypes under different modes of dominance. Our model is also able to simulate any number of genetic loci causing mean and variance heterogeneity. Conclusions: We demonstrate the utility of our simulation procedure through generating a variety of genetic loci types to evaluate common GWAS and vGWAS analysis methods. The results of this evaluation highlight the challenges current tools face in detecting GWAS and vGWAS loci.
MeSH term(s)	Algorithms ; Computer Simulation ; Diploidy ; Genetic Loci ; Genome-Wide Association Study ; Genotype ; Humans ; Linkage Disequilibrium/genetics ; Phenotype ; Polymorphism, Single Nucleotide/genetics
Language	English
Publishing date	2018-03-21
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-018-2061-1
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Article ; Online: Simulating variance heterogeneity in quantitative genome wide association studies

Ahmad Al Kawam / Mustafa Alshawaqfeh / James J. Cai / Erchin Serpedin / Aniruddha Datta

BMC Bioinformatics, Vol 19, Iss S3, Pp 35-

2018 Volume 44

Abstract: Abstract Background Analyzing Variance heterogeneity in genome wide association studies (vGWAS) is an emerging approach for detecting genetic loci involved in gene-gene and gene-environment interactions. vGWAS analysis detects variability in phenotype ... ...

Abstract	Abstract Background Analyzing Variance heterogeneity in genome wide association studies (vGWAS) is an emerging approach for detecting genetic loci involved in gene-gene and gene-environment interactions. vGWAS analysis detects variability in phenotype values across genotypes, as opposed to typical GWAS analysis, which detects variations in the mean phenotype value. Results A handful of vGWAS analysis methods have been recently introduced in the literature. However, very little work has been done for evaluating these methods. To enable the development of better vGWAS analysis methods, this work presents the first quantitative vGWAS simulation procedure. To that end, we describe the mathematical framework and algorithm for generating quantitative vGWAS phenotype data from genotype profiles. Our simulation model accounts for both haploid and diploid genotypes under different modes of dominance. Our model is also able to simulate any number of genetic loci causing mean and variance heterogeneity. Conclusions We demonstrate the utility of our simulation procedure through generating a variety of genetic loci types to evaluate common GWAS and vGWAS analysis methods. The results of this evaluation highlight the challenges current tools face in detecting GWAS and vGWAS loci.
Keywords	Variance heterogeneity ; Genome wide association studies ; GWAS simulation ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2018-03-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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