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The Five-Gene-Network data analysis with Local Causal Discovery Algorithm Using Causal Bayesian Networks

CHALLENGES OF SYSTEMS BIOLOGY: COMMUNITY EFFORTS TO HARNESS BIOLOGICAL COMPLEXITY 2009年第1期1158卷 93-101页

作者： Yoo, Changwon Brilz, Erik M. Univ Montana Missoula MT 59812 USA

Using microarray experiments, we can model causal relationships of genes measured through mRNA expression levels. To this end, it is desirable to compare experiments of the system under complete interventions of some genes, such as by knock out of some genes, with experiments of the system under no interventions. However, it. is expensive and difficult to conduct wet lab experiments of complete interventions of genes in a biological system. Thus, it will be helpful if we can discover promising causal relationships among genes with no interventions or incomplete interventions, such as by applying a treatment. that has unknown effects to modeled genes, in order to identify promising genes to perturb in the system dial can later be verified in wet laboratories. In this paper we use causal Bayesian networks to implement, a causal discovery algorithm-the equivalence local implicit. latent variable scoring method (EquLIM)-that identifies promising causal relationships even with a small dataset generated from no or incomplete interventions. We then apply EquLIM to analyze the five-gene-network data and compare EquLIM's predictions with true causal pairwise relationships between the genes.

关键词： causal Bayesian networks causal discovery gene networks high throughput data analysis microarray data analysis

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Sparse discriminant analysis for breast cancer biomarker identification and classification

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Progress in Natural Science:Materials International 2009年第11期19卷 1635-1641页

作者： Yu Shi~(a,b),Daoqing Dai~(a,*),Chaochun Liu~(a,b),Hong Yan~(b,c) a Center for Computer Vision and Department of Mathematics,Sun Yat-Sen(Zhongshan) University,Guangzhou 510275,China b Department of Electric Engineering,City University of Hong Kong,83 Tat Chee Avenue,Kowloon,Hong Kong,China c School of Electrical and Information Engineering,University of Sydney,NSW 2006,Australia [a]Center for Computer Vision and Department of Mathematics Sun Yat-Sen (Zhongshan) University Guangzhou 510275 China [b]Department of Electric Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong China [c]School of Electrical and Information Engineering University of Sydney NSW 2006 Australia

Biomarker identification and cancer classification are two important procedures in microarray data *** propose a novel unified method to carry out both *** first preselect biomarker candidates by eliminating unrelated genes through the BSS/WSS ratio filter to reduce computational cost,and then use a sparse discriminant analysis method for simultaneous biomarker identification and cancer ***,we give a mathematical justification about automatic biomarker *** results show that the proposed method can identify key genes that have been verified in biochemical or biomedical research and classify the breast cancer type correctly.

关键词： Biomarker identification Cancer classification Discriminant analysis Maximum penalized likelihood microarray data analysis

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Survival genes expression analysis following ionizing radiation to LiCl treated KG1a cells

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INTERNATIONAL JOURNAL OF RADIATION BIOLOGY 2020年第5期96卷 671-688页

作者： Verma, Yogesh Kumar Singh, Ajay Kumar Gurudutta, Gangenahalli Ugraiah DRDO Div Stem Cell & Gene Therapy Res INMAS Delhi 110054 India

Purpose: Lithium chloride (LiCl) is clinically used for manic disorders. Its role has been shown in improving cell survival by decreasing Bax and p53 expression and increasing Bcl-2 concentration in the cell. This potential of LiCl is responsible for reducing irradiated cell death. In this study, we have explored the role of LiCl as a radioprotectant affecting survival genes. Materials and methods: To find out the cellular response upon LiCl pretreatment to radiation-exposed KG1a cells;viability, clonogenic assay and microarray studies were performed. This was followed by the detection of transcription factor binding motif in coregulated genes. These results were confirmed by reverse transcription-polymerase chain reaction (RT-PCR) and chromatin immunoprecipitation (CHIP). Results: LiCl improved irradiated KG1a cell survival and its clonogenicity at 2 mM concentration (clinically used). microarray data analysis showed differential expression of cell-protecting genes playing an important role in apoptosis, cell cycle, adhesion and inflammation, etc. The coregulation analysis revealed genes involved in bile acid biosynthesis were also affected by LiCl treatment, these genes are likely to be responsible for radiation-induced gastrointestinal (GI) syndrome through bile production. Conclusions: This is the first study with respect to global genetic expression upon LiCl treatment to radiation-exposed cells. Our results suggest considering repurposing of LiCl as a protective agent for radiation injury.

关键词： microarray data analysis cell survival clustering coregulated expression network analysis bile acid synthesis

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Learning relational descriptions of differentially expressed gene groups

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS 2008年第1期38卷 16-25页

作者： Trajkovski, Igor Zelezny, Filip Lavrac, Nada Tolar, Jakub Jozef Stefan Inst Dept Knowledge Technol Ljubljana 1000 Slovenia Czech Tech Univ Gerstner Lab Intelligent Data Anal Res Grp CR-16635 Prague 6 Czech Republic Univ Nova Gorica SL-5000 Nova Gorica Slovenia Univ Minnesota Div Hematol Oncol & Blood & Marrow Transplantat Minneapolis MN 55455 USA

This paper presents a method that uses gene ontologies (GOs), together with the paradigm of relational subgroup discovery, to find compactly described groups of genes differentially expressed in specific cancers. The groups are described by means of relational logic features, extracted from publicly available GO information, and are straightforwardly interpretable by medical experts. We applied the proposed method to three gene expression data sets with the following respective sets of sample classes: 1) acute lymphoblastic leukemia (ALL) versus acute myeloid leukemia (AML);2) seven subtypes of ALL;and 3) 14 different types of cancers. Significant number of discovered groups of genes had a description that highlighted the underlying biological process responsible for distinguishing one class from the other classes. The quality of the discovered descriptions was also verified by cross validation. We believe that the. presented approach will significantly contribute to the application of relational machine learning to gene expression analysis, given the expected increase in both the quality and quantity of gene/protein annotations in the, near future.

关键词： inductive logic programming learning from structured data learning in bioinformatics microarray data analysis relational learning scientific discovery

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ESVM: Evolutionary support vector machine for automatic feature selection and classification of microarray data

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BIOSYSTEMS 2007年第2期90卷 516-528页

作者： Huang, Hui-Ling Chang, Fang-Lin Jin Wen Inst Technol Dept Informat Management Taipei Taiwan Tri Serv Gen Hosp Dept Anesthesiol Taipei Taiwan

An optimal design of support vector machine (SVM)-based classifiers for prediction aims to optimize the combination of feature selection, parameter setting of SVM, and cross-validation methods. However, SVMs do not offer the mechanism of automatic internal relevant feature detection. The appropriate setting of their control parameters is often treated as another independent problem. This paper proposes an evolutionary approach to designing an SVM-based classifier (named ESVM) by simultaneous optimization of automatic feature selection and parameter tuning using an intelligent genetic algorithm, combined with k-fold cross-validation regarded as an estimator of generalization ability. To illustrate and evaluate the efficiency of ESVM, a typical application to microarray classification using 11 multi-class datasets is adopted. By considering model uncertainty, a frequency-based technique by voting on multiple sets of potentially informative features is used to identify the most effective subset of genes. It is shown that ESVM can obtain a high accuracy of 96.88% with a small number 10.0 of selected genes using 10-fold cross-validation for the 11 datasets averagely. The merits of ESVM are three-fold: (1) automatic feature selection and parameter setting embedded into ESVM can advance prediction abilities, compared to traditional SVMs;(2) ESVM can serve not only as an accurate classifier but also as an adaptive feature extractor;(3) ESVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of ESVM for bioinformatics problems. (c) 2006 Elsevier Ireland Ltd. All rights reserved.

关键词： classification gene expression intelligent genetic algorithm microarray data analysis support vector machine

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Genetic network modeling

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PHARMACOGENOMICS 2002年第4期3卷 507-525页

作者： van Someren, EP Wessels, LFA Backer, E Reinders, MJT Delft Univ Technol Fac Informat Technol & Syst Dept Mediamet Informat & Commun Theory Grp Delft Netherlands

The inference of genetic interactions from measured expression data is one of the most challenging tasks of modern functional genomics. When successful, the learned network of regulatory interactions yields a wealth of useful information. An inferred genetic network contains information about the pathway to which a gene belongs and which genes it interacts with. Furthermore, it explains the function of the gene in terms of how it influences other genes and indicates which genes are pathway initiators and therefore potential drug targets. Obviously, such wealth comes at a price and that of genetic network modeling is that it is an extremely complex task. Therefore, it is necessary to develop sophisticated computational tools that are able to extract relevant information from a limited set of microarray measurements and integrate this with different information sources, to come up with reliable hypotheses of a genetic regulatory network. Thus far, a multitude of modeling approaches have been proposed for discovering genetic networks. However, it is unclear what the advantages and disadvantages of each of the different approaches are and how their results can be compared. In this review, genetic network models are put in a historical perspective that explains why certain models were introduced. Various modeling assumptions and their consequences are also highlighted. In addition, an overview of the principal differences and similarities between the approaches is given by considering the qualitative properties of the chosen models and their learning strategies.

关键词： bioinformatics genetic regulatory networks microarray data analysis

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How many differentially expressed genes: A perspective from the comparison of genotypic and phenotypic distances

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GENOMICS 2018年第1期110卷 67-73页

作者： Zhao, Bi Erwin, Aqeela Xue, Bin Univ S Florida Coll Arts & Sci Sch Nat Sci & Math Dept Cell Biol Microbiol & Mol Biol 4202 East Fowler AveISA2015 Tampa FL 33620 USA

Identifying differentially expressed genes is critical in microarray data analysis. Many methods have been developed by combining p-value, fold-change, and various statistical models to determine these genes. When using these methods, it is necessary to set up various pre-determined cutoff values. However, many of these cutoff values are somewhat arbitrary and may not have clear connections to biology. In this study, a genetic distance method based on gene expression level was developed to analyze eight sets of microarray data extracted from the GEO database. Since the genes used in distance calculation have been ranked by fold-change, the genetic distance becomes more stable when adding more genes in the calculation, indicating there is an optimal set of genes which are sufficient to characterize the stable difference between samples. This set of genes is differentially expressed genes representing both the genotypic and phenotypic differences between samples.

关键词： microarray data analysis Differentially expressed genes Genetic distance Genotypically and phenotypically significant DEGs gpsDEGs

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Dealing with large diagonals in kernel matrices

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ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 2003年第2期55卷 391-408页

作者： Weston, J Schölkopf, B Eskin, E Leslie, C Noble, WS Max Planck Inst Biol Cybernet D-72076 Tubingen Germany Columbia Univ Dept Comp Sci New York NY 10027 USA Columbia Univ Ctr Computat Biol New York NY 10032 USA Univ Washington Hlth Sci Ctr Dept Genome Sci Seattle WA 98195 USA

In kernel methods, all the information about the training data is contained in the Gram matrix. If this matrix has large diagonal values, which arises for many types of kernels, then kernel methods do not perform well: We propose and test several methods for dealing with this problem by reducing the dynamic range of the matrix while preserving the positive definiteness of the Hessian of the quadratic programming problem that one has to solve when training a Support Vector Machine, which is a common kernel approach for pattern recognition.

关键词： Kernel methods Support Vector Machines pattern recognition bioinformatics microarray data analysis transduction regularization

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Identification of potential biomarkers for melanoma cancer (black tumor) using bioinformatics strategy: a study based on GEO and SRA datasets

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JOURNAL OF APPLIED GENETICS 2024年第1期65卷 83-93页

作者： Almatrafi, Tahani Ahmad Mohammedsaleh, Zuhair M. Moawadh, Mamdoh S. Bassfar, Zaid Jalal, Mohammed M. Badahdah, Fatima Ahmed Alghamdi, Youssef S. Almasoudi, Hassan Hussain Hakami, Mohammed Ageeli Binshaya, Abdulkarim S. Almohaimeed, Hailah M. Soliman, Mona H. King Saud Univ Coll Med Anat Dept Riyadh Saudi Arabia Univ Tabuk Fac Appl Med Sci Dept Med Lab Technol Tabuk 71491 Saudi Arabia Univ Tabuk Fac Comp & Informat Technol Tabuk Saudi Arabia Prince Sultan Mil Med City Surg Dept PSMMC Riyadh Saudi Arabia Taif Univ Turabah Univ Coll Dept Biol Taif 21995 Saudi Arabia Najran Univ Coll Appl Med Sci Dept Clin Lab Sci Najran Saudi Arabia Shaqra Univ Coll Appl Med Sci Dept Clin Lab Sci Riyadh Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Appl Med Sci Dept Med Lab Sci AlKharj 11942 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Med Dept Basic Sci POB 84428 Riyadh 11671 Saudi Arabia Cairo Univ Fac Sci Bot & Microbiol Dept Giza 12613 Egypt Taibah Univ Fac Sci Biol Dept Yanbu El Bahr 46429 Yanbu Saudi Arabia

Melanoma, a highly invasive type of skin cancer that penetrates the entire dermis layer, is associated with increased mortality rates. Excessive exposure of the skin to sunlight, specifically ultraviolet radiation, is the underlying cause of this malignant condition. The appearance of unique skin moles represents a visible clue, referred to as the "ugly duckling" sign, indicating the presence of melanoma and its association with cellular DNA damage. This research aims to explore potential biomarkers derived from microarray data, employing bioinformatics techniques and methodologies, for a thorough investigation of melanoma skin cancer. The microarray dataset for melanoma skin cancer was obtained from the GEO database, and thorough data analysis and quality control measures were performed to identify differentially expressed genes (DEGs). The top 14 highly expressed DEGs were identified, and their gene information and protein sequences were retrieved from the NCBI gene and protein database. These proteins were further analyzed for domain identification and network analysis. Gene expression analysis was conducted to visualize the upregulated and downregulated genes. Additionally, gene metabolite network analysis was carried out to understand the interactions between highly interconnected genes and regulatory transcripts. Molecular docking was employed to investigate the ligand-binding sites and visualize the three-dimensional structure of proteins. Our research unveiled a collection of genes with varying expression levels, some elevated and others reduced, which could function as promising biomarkers closely linked to the development and advancement of melanoma skin cancer. Through molecular docking analysis of the GINS2 protein, we identified two natural compounds (PubChem-156021169 and PubChem-60700) with potential as inhibitors against melanoma. This research has implications for early detection, treatment, and understanding the molecular basis of melanoma.

关键词： Melanoma cancer Molecular docking microarray data analysis SRA data analysis

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A Bayesian decision theoretic approach to directional multiple hypotheses problems

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JOURNAL OF MULTIVARIATE analysis 2013年 120卷 205-215页

作者： Bansal, Naveen K. Miescke, Klaus J. Marquette Univ Dept Math Stat & Comp Sci Milwaukee WI 53201 USA Univ Illinois Dept Math Stat & Comp Sci Chicago IL 60680 USA

A multiple hypothesis problem with directional alternatives is considered in a decision theoretic framework. Skewness in the alternatives is considered, and it is shown that this skewness permits the Bayes rules to possess certain advantages when one direction of the alternatives is more important or more probable than the other direction. Bayes rules subject to constraints on certain directional false discovery rates are obtained, and their performances are compared with a traditional FDR rule through simulation. We also analyzed a gene expression data using our methodology, and compare the results to that of a FDR method. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Directional alternatives False discovery rate microarray data analysis MicroRNA Multiple hypotheses

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