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Efficient Parameter Estimation for the Inference of S-system Models of Genetic Networks: Proposition of Further Problem Decomposition and Alternate Function Optimization

Shuhei Kimura, Koki Matsumura, Mariko Okada-Hatakeyama
2011 Chem-Bio Informatics Journal  
This strategy defines the inference of a genetic network consisting of N genes as N subproblems, each of which is a 2(N+1)-dimensional function optimization problem.  ...  The problem decomposition strategy is a very efficient technique for the inference of S-system models of genetic networks.  ...  Genetic network inference as a series of discrimination tasks The LPM-based inference method defines the inference of a genetic network consisting of N genes as N discrimination tasks, each corresponding  ... 
doi:10.1273/cbij.11.24 fatcat:gpbavyowrvcbdbwr7a75v7rjha

DISCOVERY OF REGULATORY INTERACTIONS THROUGH PERTURBATION: INFERENCE AND EXPERIMENTAL DESIGN

TREY E. IDEKER, VESTEINN THORSSONt, RICHARD M. KARP
1999 Biocomputing 2000  
We present two methods to be used interactively to infer a genetic network from gene expression measurements.  ...  The predictor method determines the set of Boolean networks consistent with an observed set of steady-state gene expression profiles, each generated from a different perturbation to the genetic network  ...  In addition, the work of V.T. was supported by a Sloan Foundation/DOE Fellowship in Computational Molecular Biology, and the work of T.I. was supported by a fellowship from the ARCS Foundation.  ... 
doi:10.1142/9789814447331_0029 fatcat:j6onvsi46jfmdgeo6yvkzcolw4

Discovery of regulatory interactions through perturbation: inference and experimental design

T E Ideker, V Thorsson, R M Karp
2000 Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing  
We present two methods to be used interactively to infer a genetic network from gene expression measurements.  ...  The predictor method determines the set of Boolean networks consistent with an observed set of steady-state gene expression profiles, each generated from a different perturbation to the genetic network  ...  In addition, the work of V.T. was supported by a Sloan Foundation/DOE Fellowship in Computational Molecular Biology, and the work of T.I. was supported by a fellowship from the ARCS Foundation.  ... 
pmid:10902179 fatcat:c6o3cgtv6rfo3dnxqs6togpvv4

Modeling gene regulatory networks by incremental evolution and system decomposition

Wei-Po Lee, Yu-Ting Hsiao
2008 2008 Asia Simulation Conference - 7th International Conference on System Simulation and Scientific Computing  
To automate the procedure of network construction, we develop a methodology to infer S-systems as regulatory systems.  ...  However, building regulatory models manually is a tedious task, especially when the number of genes involved increases with the complexity of regulation.  ...  a genetic network.  ... 
doi:10.1109/asc-icsc.2008.4675390 fatcat:hrshihegf5es3in47zustnxh2u

Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation [article]

Mattias Åkesson, Prashant Singh, Fredrik Wrede, Andreas Hellander
2021 arXiv   pre-print
The quality of those statistics acutely impacts the accuracy of the inference task.  ...  Its performance hinges critically on the ability to summarize high-dimensional system responses such as time series into a few informative, low-dimensional summary statistics.  ...  Convolutional Neural Networks The inherent structure in time series makes convolutional networks an attractive option to explore for the task of learning the mapping between time series responses as input  ... 
arXiv:2001.11760v5 fatcat:naabrxugnjczfislovtmahaikq

Nested clade analyses of phylogeographic data: testing hypotheses about gene flow and population history

ALAN R. TEMPLETON
1998 Molecular Ecology  
One such technique is to use the haplotype tree to define a nested series of branches (clades), thereby allowing an evolutionary nested analysis of the spatial distribution of genetic variation.  ...  Each line in the network represents a single mutational change. 0 indicates an interior node in the network that was not present in the sample; that is, these are inferred intermediate haplotypes between  ...  I would also like to thank Robert Ricklefs, Eldredge Bermingham, and an anonymous reviewer for their excellent reviews which have resulted in a stronger article.  ... 
doi:10.1046/j.1365-294x.1998.00308.x pmid:9627999 fatcat:d6yybmhizbam3d2wrx6iiqhkse

Inference in the age of big data: Future perspectives on neuroscience

Danilo Bzdok, B.T. Thomas Yeo
2017 NeuroImage  
imaging, and genetics).  ...  A B S T R A C T Neuroscience is undergoing faster changes than ever before.  ...  Generative adversarial networks are an example of a discriminative-generative hybrid model, where a discriminative component distinguishes real data points as synthesized or real and its generative component  ... 
doi:10.1016/j.neuroimage.2017.04.061 pmid:28456584 fatcat:e6fnp7kiejfedh3afdq3a3v5ei

Automatic Identification of Twin Zygosity in Resting-State Functional MRI [article]

Andrey Gritsenko, Martin A. Lindquist, Gregory R. Kirk, Moo K. Chung
2018 arXiv   pre-print
For this, we project an fMRI signal to a set of basis functions and use the projection coefficients as the compact and discriminative feature representation of noisy fMRI.  ...  Accurate differentiation of twin types allows efficient inference on genetic influences in a population. However, identification of zygosity is often prone to errors without genotying.  ...  o i < θ, (2) where o i is numerical output of the neural network for the i-th pair of twins, and θ is the discrimination threshold.  ... 
arXiv:1807.00244v4 fatcat:iezj75itxbduvdvvg3fpob5zwe

Computational dynamic approaches for temporal omics data with applications to systems medicine

Yulan Liang, Arpad Kelemen
2017 BioData Mining  
Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are  ...  In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working  ...  Availability of data and materials Data sharing not applicable to this article as no datasets were generated or analysed during the current study.  ... 
doi:10.1186/s13040-017-0140-x pmid:28638442 pmcid:PMC5473988 fatcat:rscvtjlpgrf53fbwlt6t4i22em

A survey of application: Genomics and genetic programming, a new frontier

Mohammad Wahab Khan, Mansaf Alam
2012 Genomics  
This is followed by a review of applications in the areas of gene network inference, gene expression data analysis, SNP analysis, epistasis analysis and gene annotation.  ...  The aim of this paper is to provide an introduction to the rapidly developing field of genetic programming (GP). Particular emphasis is placed on the application of GP to genomics.  ...  The results of the literature survey have been arranged into the following broad categories: Genetic network inference Network biology involves the use of networks to represent complexity, computes  ... 
doi:10.1016/j.ygeno.2012.05.014 pmid:22683715 fatcat:xlq5ilckr5fdjgo4ia4k7b6rz4

Supervised Machine Learning for Population Genetics: A New Paradigm

Daniel R. Schrider, Andrew D. Kern
2018 Trends in Genetics  
As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information.  ...  To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data.  ...  We also thank Justin Blumenstiel and Lex Flagel for discussions about image classification in population genetics. D.R.S. was supported by National Institutes of Health (NIH) award K99HG008696.  ... 
doi:10.1016/j.tig.2017.12.005 pmid:29331490 pmcid:PMC5905713 fatcat:xzqm7666breqflmwtno5ntvxty

Intelligent credit scoring model using soft computing approach

A. Lahsasna, R.N. Ainon, Teh Ying Wah
2008 2008 International Conference on Computer and Communication Engineering  
The study concludes with a series of suggestions of other methods to be investigated for credit scoring modelling.  ...  In this survey, the main soft computing methods applied in credit scoring models are presented and the advantages as well as the limitations of each method are outlined.  ...  Genetic Algorithm After the success of neural network in developing accurate credit models, many studies have investigated the application of genetic algorithm as a potential alternative to neural network  ... 
doi:10.1109/iccce.2008.4580635 fatcat:qgj42ys4szgqbga3ny32kuckem

Automatic inference of demographic parameters using Generative Adversarial Networks [article]

Zhanpeng Wang, Jiaping Wang, Michael Kourakos, Nhung Hoang, Hyong Hark Lee, Iain Mathieson, Sara Mathieson
2020 bioRxiv   pre-print
As a result, simulated data often fails to mirror the properties of real genetic data, which limits the scope of methods that rely on it.  ...  Population genetics relies heavily on simulated data for validation, inference, and intuition.  ...  To create a series of training examples, the real data is divided into regions of length L and the middle biallelic S SNPs from each region are retained (encoded as 0/1).  ... 
doi:10.1101/2020.08.05.237834 fatcat:x42sirk4cza6jbcgsncnr6xi44

The Future of Data Analysis in the Neurosciences [article]

Danilo Bzdok, B. T. Thomas Yeo
2016 arXiv   pre-print
We believe that large-scale data analysis will use more models that are non-parametric, generative, mixing frequentist and Bayesian aspects, and grounded in different statistical inferences.  ...  While growing data availability and information granularity have been amply discussed, we direct attention to a routinely neglected question: How will the unprecedented data richness shape data analysis  ...  subsets of most important networks for each task [45] .  ... 
arXiv:1608.03465v1 fatcat:roen4d2axncufftj3ifjjimqpe

Differential expression analysis for sequence count data

Simon Anders, Wolfgang Huber
2010 Genome Biology  
One of the basic statistical tasks is inference (testing, regression) on discrete count values (e.g., representing the number of times a certain type of mRNA was sampled by the sequencing machine).  ...  High-throughput DNA sequencing is a powerful and versatile new technology for obtaining comprehensive and quantitative data about RNA expression (RNA-Seq), protein-DNA binding (ChIP-Seq), and genetic variations  ...  To infer a Boolean network solely from quantitative time series data, the continuous data have to be binarized.  ... 
doi:10.1186/gb-2010-11-10-r106 pmid:20979621 pmcid:PMC3218662 fatcat:ala2sfzzbnfzfgeompuuveneca
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