163 research outputs found
PRNG Random Numbers on GPU
Limited numerical precision of nVidia GeForce 8800 GTX and other GPUs requires careful implementation of PRNGs. The
Park-Miller PRNG is programmed using G80’s native Value4f floating point in RapidMind C++. Speed up is more than 40.
Code is available via ftp ftp://cs.ucl.ac.uk/genetic/gp-code/random-numbers/gpu park-miller.tar.g
CES-484 Row Quantile Normalisation of Microarrays
Variation in tissue sample preparation leads to variation across the Transcriptome not just between
experiments but to between individual microarrays. Normalisation is essential before data from different
arrays can be compared. Quantile normalisation can be used to force data from a single GeneChip to
take a given distribution. However quantile normalisation can be blind to the consistent spatial variation
we note in thousands of Affymetrix’ High-density oligonucleotide array (HDONAs) from NCBI GEO.
We propose a simple computationally efficient normalisation technique which takes into account the
spatial aspect. BioConductor R code is included
CES-486 A Map of Human Gene Expression
We have calculated the correlation between most human genes, using thousands of public Affymetrix
HG-U133 +2 high-density oligonucleotide array (HDONAs). The correspondences show highly structured
interactions between EBI Ensembl exons across a wide range of tissues and disease states taken
from NCBI GEO. Eigen values are used to find and display the principle components of the gene
expression mRNA data. The PCA analysis suggests almost all genes interact in a connected graph.
There are thousands of strongly interacting genes but the whole network is sparse, with many genes
not correlating strongly. So far, few power laws typical of “small world” networks and anticipated in
gene regulatory networks have been found.
The �300 million correlations are organised by gene/exon and are available via a web interface
Generating Random Infix Expressions for GNU coreutils expr
We use the recent random_tree() addition to GPquick [arXiv:2001.04505] to uniformly sample in linear time the space of binary trees.
A unix gawk script transforms these to uniform random infix expressions, as used by Free Software Foundation GNU core utility expr.
It converts from Lisp s-expression like prefix representation used by GPquick to bracketed infix expressions, e.g. "(" 3050 "=" 5514 ")" "-" 3073.
gawk randomly labels internal tree nodes with the
14 functions known to expr
and replaces leafs with randomly chosen positive integers up to 32768.
About 80 percent of random expressions are rejected, since they cause expr to fail, typically due to division by zero
A Field Guide to Genetic Programming
xiv, 233 p. : il. ; 23 cm.Libro ElectrĂłnicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction --
Representation, initialisation and operators in Tree-based GP --
Getting ready to run genetic programming --
Example genetic programming run --
Alternative initialisations and operators in Tree-based GP --
Modular, grammatical and developmental Tree-based GP --
Linear and graph genetic programming --
Probalistic genetic programming --
Multi-objective genetic programming --
Fast and distributed genetic programming --
GP theory and its applications --
Applications --
Troubleshooting GP --
Conclusions.Contents
xi
1 Introduction
1.1 Genetic Programming in a Nutshell
1.2 Getting Started
1.3 Prerequisites
1.4 Overview of this Field Guide I
Basics
2 Representation, Initialisation and GP
2.1 Representation
2.2 Initialising the Population
2.3 Selection
2.4 Recombination and Mutation Operators in Tree-based
3 Getting Ready to Run Genetic Programming 19
3.1 Step 1: Terminal Set 19
3.2 Step 2: Function Set 20
3.2.1 Closure 21
3.2.2 Sufficiency 23
3.2.3 Evolving Structures other than Programs 23
3.3 Step 3: Fitness Function 24
3.4 Step 4: GP Parameters 26
3.5 Step 5: Termination and solution designation 27
4 Example Genetic Programming Run
4.1 Preparatory Steps 29
4.2 Step-by-Step Sample Run 31
4.2.1 Initialisation 31
4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming
5 Alternative Initialisations and Operators in
5.1 Constructing the Initial Population
5.1.1 Uniform Initialisation
5.1.2 Initialisation may Affect Bloat
5.1.3 Seeding
5.2 GP Mutation
5.2.1 Is Mutation Necessary?
5.2.2 Mutation Cookbook
5.3 GP Crossover
5.4 Other Techniques 32
5.5 Tree-based GP 39
6 Modular, Grammatical and Developmental Tree-based GP 47
6.1 Evolving Modular and Hierarchical Structures 47
6.1.1 Automatically Defined Functions 48
6.1.2 Program Architecture and Architecture-Altering 50
6.2 Constraining Structures 51
6.2.1 Enforcing Particular Structures 52
6.2.2 Strongly Typed GP 52
6.2.3 Grammar-based Constraints 53
6.2.4 Constraints and Bias 55
6.3 Developmental Genetic Programming 57
6.4 Strongly Typed Autoconstructive GP with PushGP 59
7 Linear and Graph Genetic Programming 61
7.1 Linear Genetic Programming 61
7.1.1 Motivations 61
7.1.2 Linear GP Representations 62
7.1.3 Linear GP Operators 64
7.2 Graph-Based Genetic Programming 65
7.2.1 Parallel Distributed GP (PDGP) 65
7.2.2 PADO 67
7.2.3 Cartesian GP 67
7.2.4 Evolving Parallel Programs using Indirect Encodings 68
8 Probabilistic Genetic Programming
8.1 Estimation of Distribution Algorithms 69
8.2 Pure EDA GP 71
8.3 Mixing Grammars and Probabilities 74
9 Multi-objective Genetic Programming 75
9.1 Combining Multiple Objectives into a Scalar Fitness Function 75
9.2 Keeping the Objectives Separate 76
9.2.1 Multi-objective Bloat and Complexity Control 77
9.2.2 Other Objectives 78
9.2.3 Non-Pareto Criteria 80
9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80
9.4 Multi-objective Optimisation via Operator Bias 81
10 Fast and Distributed Genetic Programming 83
10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83
10.2 Reducing Cost of Fitness with Caches 86
10.3 Parallel and Distributed GP are Not Equivalent 88
10.4 Running GP on Parallel Hardware 89
10.4.1 Master–slave GP 89
10.4.2 GP Running on GPUs 90
10.4.3 GP on FPGAs 92
10.4.4 Sub-machine-code GP 93
10.5 Geographically Distributed GP 93
11 GP Theory and its Applications 97
11.1 Mathematical Models 98
11.2 Search Spaces 99
11.3 Bloat 101
11.3.1 Bloat in Theory 101
11.3.2 Bloat Control in Practice 104
III
Practical Genetic Programming
12 Applications
12.1 Where GP has Done Well
12.2 Curve Fitting, Data Modelling and Symbolic Regression
12.3 Human Competitive Results – the Humies
12.4 Image and Signal Processing
12.5 Financial Trading, Time Series, and Economic Modelling
12.6 Industrial Process Control
12.7 Medicine, Biology and Bioinformatics
12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii
12.9 Entertainment and Computer Games 127
12.10The Arts 127
12.11Compression 128
13 Troubleshooting GP
13.1 Is there a Bug in the Code?
13.2 Can you Trust your Results?
13.3 There are No Silver Bullets
13.4 Small Changes can have Big Effects
13.5 Big Changes can have No Effect
13.6 Study your Populations
13.7 Encourage Diversity
13.8 Embrace Approximation
13.9 Control Bloat
13.10 Checkpoint Results
13.11 Report Well
13.12 Convince your Customers
14 Conclusions
Tricks of the Trade
A Resources
A.1 Key Books
A.2 Key Journals
A.3 Key International Meetings
A.4 GP Implementations
A.5 On-Line Resources 145
B TinyGP 151
B.1 Overview of TinyGP 151
B.2 Input Data Files for TinyGP 153
B.3 Source Code 154
B.4 Compiling and Running TinyGP 162
Bibliography 167
Inde
G-spots cause incorrect expression measurement in Affymetrix microarrays
Abstract
Background
High Density Oligonucleotide arrays (HDONAs), such as the Affymetrix HG-U133A GeneChip, use sets of probes chosen to match specified genes, with the expectation that if a particular gene is highly expressed then all the probes in that gene's probe set will provide a consistent message signifying the gene's presence. However, probes that contain a G-spot (a sequence of four or more guanines) behave abnormally and it has been suggested that these probes are responding to some biochemical effect such as the formation of G-quadruplexes.
Results
We have tested this expectation by examining the correlation coefficients between pairs of probes using the data on thousands of arrays that are available in the NCBI Gene Expression Omnibus (GEO) repository. We confirm the finding that G-spot probes are poorly correlated with others in their probesets and reveal that, by contrast, they are highly correlated with one another. We demonstrate that the correlation is most marked when the G-spot is at the 5' end of the probe.
Conclusion
Since these G-spot probes generally show little correlation with the other members of their probesets they are not fit for purpose and their values should be excluded when calculating gene expression values. This has serious implications, since more than 40% of the probesets in the HG-U133A GeneChip contain at least one such probe. Future array designs should avoid these untrustworthy probes.
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Affymetrix probes containing runs of contiguous guanines are not gene-specific
High Density Oligonucleotide arrays (HDONAs), such as the Affymetrix HG-U133A GeneChip, use sets of probes chosen to match specified genes, with the expectation that if a particular gene is highly expressed then all the probes in the designated probe set will provide a consistent message signifying the gene's presence. However, we demonstrate by data mining thousands of CEL files from NCBI's GEO database that 4G-probes (defined as probes containing sequences of four or more consecutive guanine (G) bases) do not react in the intended way. Rather, possibly due to the formation of G-quadruplexes, most 4G-probes are correlated, irrespective of the expression of the thousands of genes for which they were separately intended. It follows that 4G-probes should be ignored when calculating gene expression levels. Furthermore, future microarray designs should make no use of 4G-probes
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