关于我们

     大数据挖掘与生物信息学研究组成立于2013年,是一个充满朝气、学术氛围浓厚、思想文化活跃、团结友爱的团队,由教师、研究生和本科生组成,关注大数据、数据挖掘、生物信息学等计算机领域当前热点问题,主要研究内容包括全基因组互作模式识别、组学数据特征识别、群体智能算法、稀疏表示特征选择方法、信息熵评价测度、无线传感器网络节点定位、图挖掘方法等。欢迎有志学子加入我们!
 

软件

 

CINOEDV

Co-Information based N-Order Epistasis Detector and Visualizer

EpiMiner

A three-stage co-information based method for detecting and visualizing epistatic interactions

AntMiner

Incorporating heuristic information into ant colony optimization for epistasis detection

EpiSIM

Simulation of multiple epistasis, linkage disequilibrium patterns and haplotype blocks for genome-wide interaction analysis

IOBLPSO PSOMiner

Improved Opposition-Based Learning Particle Swarm Optimization (IOBLPSO) & the general Particle Swarm Optimization (PSOMiner) for epistasis detection

IACO

An Improved Ant Colony Optimization Algorithm for the Detection of SNP-SNP Interactions

SIPSO

SIPSO: Selectively Informed Particle Swarm Optimization Based on Mutual Information to Determine SNP-SNP Interactions

epiACO

epiACO: A Method for Identifying Epistasis Based on Ant Colony Optimization Algorithm
 


数据

 

AMD Data

A real SNP data set of age-related macular degeneration

100-SNP Data

Simulated 100-SNP data sets in "Performance analysis of novel methods for detecting epistasis "

10000-SNP Data

Simulated 10000-SNP data sets in "Performance analysis of novel methods for detecting epistasis "

CBIL Data

CBIL SNP data sets simulated by Computational Bioinformatics and Bio-imaging Laboratory

Himmelstein Data

Himmelstein simulated SNP data sets with high-order functional SNPs

GMCM2016 Data

SNP data simulated and used for National Postgraduate Mathematic Contest in Modeling 2016 B Problem