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作者： Luo, Weijun University of Michigan

学位级别：Ph.D.

Two basic motivating questions in biomedical research are: What genes regulate what other genes? What genes or groups of genes regulate a specific phenotype? Gene regulatory network (GRN) reconstruction and pathway inference are the two computational strategies addressing these two questions respectively. GRN reconstruction is to infer the components and topology of an unknown pathway, while pathway inference is to infer association between known pathways and a phenotype. This thesis focuses on gene regulatory network reconstruction and pathway inference from high throughput biological data. In the first part of this work, I developed a novel method, MI3, for de novo GRN reconstruction using continuous three-way mutual information. MI3 addresses three major issues in previous probabilistic methods simultaneously: (1) to handle continuous variables, (2) to detect high order relationships, (3) to differentiate causal vs. confounding relationships. MI3 consistently and significantly outperformed frequently used control methods and faithfully capture mechanistic relationships from gene expression data. In the second part of this work, I proposed another novel method, GAGE, Generally Applicable Gene Set Enrichment for pathway inference. I successfully apply GAGE to multiple microarray data sets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better performance when compared to two other commonly used GSA methods of GSEA and PAGE. GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. In the third part of this work, we conducted a microarray study on transcriptional programs during BMP6 induced osteoblast differentiation and mineralization, and applied GAGE to recover the regulatory pathways and transcriptional signaling networks in the process. I not only showed which pathways or gene sets are significant, but also when and how they are involved in the o

关键词： Gene Regulatory Network Pathway Inference microarray data BMP Induced Osteoblast Differentiation Systems Biology Thesis

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A Selection of an Optimal Framework Identifying the Prominent Autism Risk Gene Biomarkers from Gene Expression data Using Neural Network

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SN Computer Science 2021年第4期2卷 241页

作者： Roopa, B.S. Manjunatha Prasad, R. Department of Electronics and Communication Enginerring D.B.I.T Affiliated to Visvesvaraya Technological University Karnataka Bengaluru 74 India Department of Electronics and Communication Engineering D.S.A.T.M Affiliated to Visvesvaraya Technological University Karnataka Bengaluru 82 India

Autism is not just the consequence of mutations in single genes but across many genes. The dynamic changes in genes are well expressed by gene expression microarray data. The potential role of researchers still persists in finding the most prominent genes that influence autism risk. In this work, microarray data collected has 146 samples of autism and a control group with 54,613 gene expression features. The features and samples suffer from dimensionality issues to train the machine learning model. To reduce the dimension of features without loss of generality, four feature selection methods are used to select 100 prominent genes each with statistical significance. The feature selection model is concatenated to the neural network classifier model for classification. The neural network model is configured with proper adjustments of hyper parameters. Each feature selection method is combined with a neural network model for the analysis of prominent genes to better the classification of autism. The metrics used to access the classification are accuracy and loss. The neural network model is trained for ten folds and tested for untrained data. The optimized framework shows an accuracy of 78.6% with a minimum loss of 0.694 for the untrained data. The optimized model is identified with a trade-off between accuracy and loss. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Autism Gene expression microarray data Neural network (NN)

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A NON-PARAMETRIC BAYESIAN CLUSTERING FOR GENE EXPRESSION data

A NON-PARAMETRIC BAYESIAN CLUSTERING FOR GENE EXPRESSION DAT...

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IEEE Statistical Signal Processing Workshop (SSP)

作者： Wang, Liming Wang, Xiaodong Columbia Univ Dept Elect Engn New York NY 10027 USA

ISBN: (纸本)9781467301831

Clustering is an important data processing tool for interpreting microarray data and genomic network inference. In this paper, we propose a non-parametric Bayesian clustering algorithm based on the hierarchical Dirichlet processes (HDP). The proposed clustering algorithm captures the hierarchical features prevalent in biological data such as the gene express data by introducing a hierarchical structure in the model. We develop a Gibbs sampling algorithm based on the Chinese restaurant metaphor. We conduct experiments on the yeast galactose datasets and yeast cell cycle datasets by comparing our clustering results to the standard results. The proposed clustering algorithm is shown to outperform several popular clustering algorithms by revealing the underlying hierarchical structure of the data. The experiments also show that the proposed clustering algorithm provides more information and reduces the unnecessary clustering fragments than the clustering algorithm based on Dirichlet mixture model.

关键词： Hierarchical Dirichlet processes Dirichlet processes clustering microarray data

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Prognostic genomic predictive biomarkers for early-stage lung cancer patients

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Open Biomarkers Journal 2021年第1期11卷 69-78页

作者： Moon, Hojin Nguyen, Alex Lee, Evan Department of Mathematics and Statistics California State University Long Beach United States Yale University New Haven 06520 CT United States

Aims: Our goal is to find predictive genomic biomarkers in order to identify subgroups of early-stage lung cancer patients that are most likely to benefit from adjuvant chemotherapy with surgery (ACT). Background: Receiving ACT appears to have a better prognosis for more severe early-stage non-small cell lung cancer patients than surgical resection only. However, not all patients benefit from chemotherapy. Objective: Preliminary studies suggest that the application of ACT is associated with a better prognosis for more severe NSCLC patients compared to those who only underwent surgical resection. Given the immense personal and financial costs associated with ACT, finding the patients who are most likely to benefit from ACT is paramount. Thus, the purpose of this research is to utilize gene expression and clinical data from lung cancer patients to find treatment-associated genomic biomarkers. Methods: To investigate the treatment effect, a modified-covariate regularized Cox regression model with lasso penalty is implemented using National Cancer Institute gene expression data to find genomic biomarkers. Results: This research utilized an independent validation dataset involving 318 lung cancer patients to validate the models. In the validation set with 318 patients, the modified covariate Cox model with lasso penalty were able to show patients who followed their predicted recommendation (either ACT for low-risk group or OBS for the high-risk group, n = 171) have higher survival benefits than 147 patients who did not follow the recommendations (p < .0001). Conclusion: Based on validation data, patients who follow our predicted recommendation by genomic biomarkers selected from the proposed model will likely benefit from ACT. © 2021 Moon et al.

关键词： Genome microarray data Personalized medicine Regression Subgroup analysis Survival analysis

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Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks

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Journal of Bioinformatics and Computational Biology 2004年第1期2卷 77-98页

作者： Imoto, Seiya Higuchi, Tomoyuki Goto, Takao Tashiro, Kousuke Kuhara, Satoru Miyano, Satoru Human Genome Center Institute of Medical Science University of Tokyo Minato-ku Tokyo 108-8639 4-6-1 Shirokanedai Japan The Inst. of Statistical Mathematics Minato-ku Tokyo 106-8569 4-6-7 Minami-Azabu Japan Grad. Sch. of Genet. Rsrc. Technol. Kyushu University Higashi-ku Fukuoka 812-8581 6-10-1 Hakozaki Japan

We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application. © Imperial College Press.

关键词： Bayesian network Biological knowledge Gene network microarray data

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Information Gain with Chaotic Genetic Algorithm for Gene Selection and Classification Problem

Information Gain with Chaotic Genetic Algorithm for Gene Sel...

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IEEE International Conference on System, Man, and Cybernetic

作者： Yang, Cheng-San Chuang, Li-Yeh Li, Jung-Chike Yang, Cheng-Hong Natl Cheng Kung Univ Inst Biomed Engn Tainan 70101 Taiwan I Shou Univ Dept Chem Engn Kaohsiung Taiwan Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung Taiwan

ISBN: (纸本)9781424423835

For microarray data classification problem, selecting relevant genes from microarray data pose a formidable challenge to researchers due to the high-dimensionality of features, multi-class categories being involved and the usually small sample size. In order to correctly analyze microarray data, the goal of feature (gene) selection is to select those subsets of differentially expressed genes that are potentially relevant for distinguishing the sample classes. In this paper, information gain and chaotic genetic algorithm are proposed to select the relevant genes, and a K-nearest neighbor with the leave-one-out cross-validation method serves as a classifier. Chaotic genetic algorithm is modified by using the chaotic mutation operator to increase the population diversity. The experimental results show that the proposed method not only effectively reduced the number of gene expression levels, but also achieved lower classification error rates.

关键词： microarray data feature selection information gain chaotic genetic algorithm K-nearest neighbor

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Feature Selection and Classification for Gene Expression data using Evolutionary Computation

Feature Selection and Classification for Gene Expression Dat...

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23rd International Conference on database and Expert Systems Applications (DEXA)

作者： Banka, Haider Dara, Suresh Indian Sch Mines Dept Comp Sci & Engn Dhanbad 826004 Bihar India

ISBN: (纸本)9780769548012

An evolutionary rough feature selection algorithm is proposed for classifying gene expression patterns. Since the data typically consist of a large number of redundant features, an initial redundancy reduction of the attributes is done to enable faster convergence. Rough set theory is employed to generate the distinction table that enable PSO to find reducts, which represent the minimal sets of non-redundant features capable of discerning between all objects. The effectiveness of the algorithm is demonstrated on three benchmark cancer datasets viz. Colon, Lymphoma and Leukemia using MOGA.

关键词： Soft computing bioinformatics microarray data feature selection reduct generation classification

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Proportionate Feature Selection - A Pre-Processing Step for Clustering

Proportionate Feature Selection - A Pre-Processing Step for ...

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IEEE International Conference on System, Man, and Cybernetic

作者： Sekhon, Jashanjot Singh Gopalkrishnan, Vivekanand Keong, Ng Wee Nanyang Technol Univ Singapore 639798 Singapore

ISBN: (纸本)9781424423835

Accuracy and efficiency of clustering algorithms depend greatly on the input data. Thus, removing unimportant features from the dataset can help us form better dusters in lesser time. These unimportant features may be those that are redundant, or affected by noise, etc. We also need to consider the fact that the features we finally choose, should represent the original dataset in the best possible way. In other words, the underlying structure of the original dataset should be the same as that of the dataset that contains only the selected features. In this paper we propose a technique that selects a subset of features that best represent the entire dataset. This technique is based on two measures - Distance Measure and Similarity Measure. We first group the similar features and then select a proportionate number of features from each group. We perform experiments on a gate expression microarray dataset, and our experimental results show that using our technique as a pre-processing step significantly increases the quality of dusters generated by the underlying K-means algorithm. We also demonstrate that our approach is better than other contemporary pre-processing filters.

关键词： Feature Selection Clustering Distance Measure Similarity Measure Proportionate Number of Genes microarray data

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Identification of Genetic Pathway for Cervical Cancer Development using Rough and Bayesian Theory 4

Identification of Genetic Pathway for Cervical Cancer Develo...

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Fourth International Conference on Emerging Applications of Information Technology

作者： Mandal, Sudip Saha, Goutam Pal, Rajat Kumar GIMT ECE Dept Krishna Nagar India NEHU IT Dept Shillong Meghalaya India Univ Calcutta CSE Dept Kolkata India

ISBN: (纸本)9781479942725

Suitable analysis of microarray dataset can unlock the mystery of the origin of many dreaded disease like cancer which can subsequently be investigated for its rectification, resulting into search for drug design. A critical challenge of the post-genomic era is to find out the cancer causing genes that induce changes in gene expression profiles in the microarray dataset. Various algorithms based on SVM, data Mining Techniques, Information theory based investigations, Clustering Techniques etc. were used by previous researchers. In this paper, Rough Set Theory and Bayesian Network based techniques have been applied for the same purpose. Rough Set has been used to isolate genes from microarray dataset responsible for cervical cancer. Bayesian approach has been used for extracting the Gene Regulating Network using the isolated genes. The same has been repeated for a normal healthy person. By superimposing these two networks, it is possible to find out the distinct cellular pathway for development of cancer from the departure of directed edges of the two networks. The results obtained in this work are quite satisfactory.

关键词： Cancer diagnosis microarray data Rough set Rule reduction Gene regulatory network Bayesian network

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Inference of Gene Regulatory Networks with Neural-Cuckoo Hybrid 2nd

Inference of Gene Regulatory Networks with Neural-Cuckoo Hyb...

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2nd International Doctoral Symposium on Applied Computation and Security Systems (ACSS)

作者： Mandal, Sudip Saha, Goutam Pal, Rajat K. GIMT ECE Dept Krishna Nagar India NEHU IT Dept Shillong Meghalaya India Univ Calcutta CSE Dept Kolkata India

ISBN: (纸本)9788132226505;9788132226482

Current progress in cellular biology and bioinformatics allow researchers to get a distinct picture of the complex biochemical processes those occur within a cell of the human body and remain as the cause for many diseases. Therefore, this technology opened up a new door to the researchers of computer science as well as to biologists to work together to investigate the causes of a disease. One of the greatest challenges of the post-genomic era is the investigation and inference of the regulatory interactions or dependencies between genes from the microarray data. Here, a new methodology has been devised for investigating the genetic interactions among genes from temporal gene expression data by combining the features of Neural Network and Cuckoo Search optimization. The developed technique has been applied on the real-world microarray dataset of Lung Adenocarcinoma for detection of genes which may be directly responsible for the cause of Lung Adenocarcinoma.

关键词： Gene regulatory network microarray data Neural network Cuckoo search optimization

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