Download Protein module

Integrative analysis of
human protein,
function, and disease
networks
汇报人：周紫维
时
间：10.22
 Protein-protein interaction
(PPI) networks
 protein functions
 protein-pathway associations
 disease-gene 、
disease-disease associations
 Lack the data integration
and analysis
 lack the strategy for
integration
 the interrelated
relationships-poorly
investigated
Method
 Protein module
 topological module
 function module
 disease module
Method
Method
 Protein networks
 Process : HIPPIE、IRefWeb
 Data :1830(P) ,2484(I)
 NeTA (Network topology
algorithm)
 Results : 136 large & 185 small
modules
 Topological Modules
 numbers : 136

1390(P:76%),2228(I:89.7%) ,M
odularity:0.91385
Method
 Function Networks
 Protein-function network
• Connect proteins with
functions(three type)
• Nodes : proteins , genes
• Edges : significant association
 Protein-pathway network
• Connect proteins with
pathway annotations
• DAVID : identify pathway
enrichment
Method
 Functional Modules
• Principle : at least one
function can cover all
• Numbers : 136
Method
 Disease Networks
 Disease-gene networks
 Disease-Disease networks
 Tools
 OMIM (Online Mendelian
Inheritance in Man)
 GWAS (Genome-wide
association studies)
Method
 Disease Modules
 Detect it based on
disease-gene associations
 15 kinds of diseases
 More than two proteins
 numbers : 139
Method
 Integrative Analysis
 annotate protein modules
• disease-overlap-functional
----non-trivial
• disease-overlap-pathway
----significant
 results : 69(non-) , 47(s)
Analysis of integration
Analysis of integration
Analysis of integration
Analysis of integration
Analysis of integration
 Example
 topological module 3
Analysis of integration
 Example
 topological module 51
Comparison
 Compare with existing
methods
 Markov Cluster Algorithm
(MCL)
 default settings ,inflation
parameter r=2
 Random walker (RW)
 probability r = 0.4
 NeTA
Results
 The quality of the topological
modules
 Finding and evaluating
community structure in networks
---calculate modularity
Results
 Three kinds of modules
Results
 non-trivial protein modules &
significant protein modules
Systematic evaluation analysis
 A benchmark network
 database : OMIM & MIPS
human complex
 construct a network
• filter : 1460(P) , 4107(I)
• at least one disease gene in
each interactions
• disease/protein/disease-prot
ein complex
Results
 The quality of the
topological modules
Results
 The numbers of three kind
modules
Results
 The mapping frequency
of
topological modules
Summary
 Reference
• http://www.micans.org/mcl/
• http://download.csdn.net/download/wsworl
f/5212020/
• http://www.nature.com/articles/srep05739?
WT.ec_id=SREP-20140722
• http://journals.aps.org/pre/abstract/10.1103
/PhysRevE.69.026113
• http://bioinformatics.oxfordjournals.org/con
tent/21/16/3448.short
• http://www.nature.com/nprot/journal/v4/n1
/abs/nprot.2008.211.html
• ……
Skip-thought vectors
 Skip-thought model
 Vocabulary expansion
Skip-thought vectors
 RNN
Skip-thought vectors
 RNN
 Gated Recurrent Unit
Recurrent Neural Networks
(GRU)
●距离加权，距离越大，权值越小
●更新权重时，只对对应的单词更新
Skip-thought vectors
 Task
▪ 1.Semantic relatedness
▪ 2.Paragraph detection
▪
3.Image-sentence ranking
▪
4.Classification benchmarks
1.movie reviews sentiment
2.customer product reviews
3.subjectivety/objectivity
classification
4.opinion polarity
5.question-type classification
Skip-thought vectors
 Reference
▪ 1. Recurrent neural network based
language model
▪ 2. Extensions of Recurrent neural network
based language model
▪ 3. Generating Text with Recurrent Neural
Networks
▪
http://blog.csdn.net/heyongluoyao8/article/
details/48636251
▪
http://www.cnblogs.com/tornadomeet/p/34
39503.html