Functionally Classifying Genes from Microarray Data Using Linear and Non-linear Data Projection

SYSTEMATIC VARIATION NORMALIZATION IN MICROARRAY DATA TO GET GENE EXPRESSION COMPARISON UNBIASED

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720005001028 ◽

2005 ◽

Vol 03 (02) ◽

pp. 225-241 ◽

Cited By ~ 13

Author(s):

JEFF W. CHOU ◽

RICHARD S. PAULES ◽

PIERRE R. BUSHEL

Keyword(s):

Gene Expression ◽

Linear Regression ◽

Microarray Data ◽

Expression Patterns ◽

Microarray Gene Expression Data ◽

Systematic Variation ◽

Data Sets ◽

Microarray Gene Expression ◽

Pixel Intensity ◽

Non Linear

Normalization removes or minimizes the biases of systematic variation that exists in experimental data sets. This study presents a systematic variation normalization (SVN) procedure for removing systematic variation in two channel microarray gene expression data. Based on an analysis of how systematic variation contributes to variability in microarray data sets, our normalization procedure includes background subtraction determined from the distribution of pixel intensity values from each data acquisition channel and log conversion, linear or non-linear regression, restoration or transformation, and multiarray normalization. In the case when a non-linear regression is required, an empirical polynomial approximation approach is used. Either the high terminated points or their averaged values in the distributions of the pixel intensity values observed in control channels may be used for rescaling multiarray datasets. These pre-processing steps remove systematic variation in the data attributable to variability in microarray slides, assay-batches, the array process, or experimenters. Biologically meaningful comparisons of gene expression patterns between control and test channels or among multiple arrays are therefore unbiased using normalized but not unnormalized datasets.

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Towards Framing an Integrated Model for Optimization and Clustering (IMOC) High-Dimensional Non-Linear DNA Data Processing using ACO and DENCLUE

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a9753.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 603-608

Keyword(s):

Data Processing ◽

Dna Microarray ◽

Microarray Data ◽

Integrated Model ◽

Biological Data ◽

High Dimensional ◽

Dna Microarray Data ◽

Proposed Model ◽

Non Linear ◽

Gene Data

The Gene Expression data contains important information about the biological reactions that are carried out in creatures that are based on the surroundings. For understanding those processes in a better manner, the hidden expressions from the high-dimensional non-linear data are to be processed effectively. Moreover, the intricacy of biological data increases the challenges in High-dimensional non-linear gene data processing. For handling the complications, incorporation of clustering techniques is employed for identifying the patterns appropriately. Furthermore, this makes clarity in gene functions, regulations, inherent expressions, categories from noisy data input. In this paper, an Integrated Model for Optimization and Clustering (IMOC) is developed for efficient gene data processing for cancer detection application. Two efficient algorithms are integrated in this work, Ant Colony Optimization for effectively determine the features of gene data and Density based Clustering (DENCLUE) is for clustering the DNA data based on the determined features. For evaluating the proposed model, the benchmark datasets such as DNA Microarray Data of Leukemia and DNA Microarray Data of Colon Cancer are used. Further, the results show that the proposed model outperforms the existing models in accuracy and efficiency rates.

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A Hyper-Heuristic for Descriptive Rule Induction

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch200 ◽

2008 ◽

pp. 3164-3175

Author(s):

Tho Hoan Pham ◽

Tu Bao Ho

Keyword(s):

Microarray Data ◽

Rule Induction ◽

Exhaustive Search ◽

Relative Accuracy ◽

Divide And Conquer ◽

The Third ◽

Search Heuristics ◽

Linear Rule ◽

Non Linear ◽

Covering Algorithms

There are in general three approaches to rule induction: exhaustive search, divide-and conquer, and separate-and-conquer (or its extension as weighted covering). Among them, the third approach, according to different rule search heuristics, can avoid the problem of producing many redundant rules (limitation of the first approach) or non-overlapping rules (limitation of the second approach). In this chapter, we propose a hyper-heuristic to construct rule search heuristics for weighted covering algorithms that allows producing rules of desired generality. The hyper-heuristic is based on a PN space, a new ROC-like tool for analysis, evaluation, and visualization of rules. Well-known rule search heuristics such as entropy, Laplacian, weight relative accuracy, and others are equivalent to ones proposed by the hyper-heuristic. Moreover, it can present new non-linear rule search heuristics, some are especially appropriate for description tasks. The non-linear rule search heuristics have been experimentally compared with others on the generality of rules induced from UCI datasets and used to learn regulatory rules from microarray data.

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B. The Non-Linear Calculations for Pulsating Stars

Symposium - International Astronomical Union ◽

10.1017/s0074180900019008 ◽

1967 ◽

Vol 28 ◽

pp. 105-176

Author(s):

Robert F. Christy

Keyword(s):

Major Part ◽

Afternoon Session ◽

Discussion Session ◽

Pulsating Stars ◽

Fourth Symposium ◽

Non Linear ◽

Considerable Discussion ◽

Informal Approach ◽

Preceding Session

(Ed. note: The custom in these Symposia has been to have a summary-introductory presentation which lasts about 1 to 1.5 hours, during which discussion from the floor is minor and usually directed at technical clarification. The remainder of the session is then devoted to discussion of the whole subject, oriented around the summary-introduction. The preceding session, I-A, at Nice, followed this pattern. Christy suggested that we might experiment in his presentation with a much more informal approach, allowing considerable discussion of the points raised in the summary-introduction during its presentation, with perhaps the entire morning spent in this way, reserving the afternoon session for discussion only. At Varenna, in the Fourth Symposium, several of the summaryintroductory papers presented from the astronomical viewpoint had been so full of concepts unfamiliar to a number of the aerodynamicists-physicists present, that a major part of the following discussion session had been devoted to simply clarifying concepts and then repeating a considerable amount of what had been summarized. So, always looking for alternatives which help to increase the understanding between the different disciplines by introducing clarification of concept as expeditiously as possible, we tried Christy's suggestion. Thus you will find the pattern of the following different from that in session I-A. I am much indebted to Christy for extensive collaboration in editing the resulting combined presentation and discussion. As always, however, I have taken upon myself the responsibility for the final editing, and so all shortcomings are on my head.)

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