menu
{ "item_title" : "Gene Network Inference", "item_author" : [" Alberto Fuente "], "item_description" : "Simulation of the Benchmark Datasets.- A Panel of Learning Methods for the Reconstruction of Gene Regulatory Networks in a Systems Genetics Context.- Benchmarking a simple yet effective approach for inferring gene regulatory networks from systems genetics data.- Differential Equation based reverse-engineering algorithms: pros and cons.- Gene regulatory network inference from systems genetics data using tree-based methods.- Extending partially known networks.- Integration of genetic variation as external perturbation to reverse engineer regulatory networks from gene expression data.- Using Simulated Data to Evaluate Bayesian Network Approach for Integrating Diverse Data.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/66/252/204/3662522047_b.jpg", "price_data" : { "retail_price" : "169.99", "online_price" : "169.99", "our_price" : "169.99", "club_price" : "169.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Gene Network Inference|Alberto Fuente

Gene Network Inference : Verification of Methods for Systems Genetics Data

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

Simulation of the Benchmark Datasets.- A Panel of Learning Methods for the Reconstruction of Gene Regulatory Networks in a Systems Genetics Context.- Benchmarking a simple yet effective approach for inferring gene regulatory networks from systems genetics data.- Differential Equation based reverse-engineering algorithms: pros and cons.- Gene regulatory network inference from systems genetics data using tree-based methods.- Extending partially known networks.- Integration of genetic variation as external perturbation to reverse engineer regulatory networks from gene expression data.- Using Simulated Data to Evaluate Bayesian Network Approach for Integrating Diverse Data.

This item is Non-Returnable

Details

  • ISBN-13: 9783662522042
  • ISBN-10: 3662522047
  • Publisher: Springer
  • Publish Date: August 2016
  • Dimensions: 9.21 x 6.14 x 0.31 inches
  • Shipping Weight: 0.47 pounds
  • Page Count: 130

Related Categories

You May Also Like...

    1

BAM Customer Reviews