Difference between revisions of "User:Boris/Temp/APB"

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;Hardware
 
;Hardware
* [[High performance computing]] (... at the bench: GPUs, FPGAs, Clusters)
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* High performance computing <!-- (... at the bench: GPUs, FPGAs, Clusters) -->
* [[Cloud computing]]
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* Cloud computing
  
 
; Systems and Tools
 
; Systems and Tools
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* [[Apache]]
 
* [[Apache]]
 
* [[MySQL]]
 
* [[MySQL]]
* [[Tools for the bioinformatics lab]] (APB, CLUSTAL, EMBOSS, PHYLIP, T-Coffee, HMMER)
+
* [[Tools for the bioinformatics lab]]
* [[GBrowse]] (installation notes, viewing annotations, LDAS installation notes, LDAS usage)
+
* [[GBrowse|GBrowse and LDAS]]
  
 
;Programming
 
;Programming
* [[IDE|IDE (Integrated Development Environment)]] (Komodo)
+
* [[IDE|IDE (Integrated Development Environment)]]
 
* [[Regular Expressions]]
 
* [[Regular Expressions]]
 
* [[Screenscraping]]
 
* [[Screenscraping]]
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* [[PHP]]
 
* [[PHP]]
 
* [[Relational database principles]]
 
* [[Relational database principles]]
* [[BioPython]] (scope, highlights, installation, use, support)
+
* BioPython <!-- (scope, highlights, installation, use, support) -->
* [[Graphical output]] (PNG and SVG)
+
* Graphical output <!-- (PNG and SVG) -->
 
* [[Autonomous agents]]
 
* [[Autonomous agents]]
  
 
:; Algorithms on Sequences
 
:; Algorithms on Sequences
** [[Dynamic Programming]] (Dynamic Programming and Optimal Pairwise Sequence Alignment, appropriate problems for DP, procedural and recursive formulation of solutions)
+
** [[Dynamic Programming]]
 
** [[Multiple Sequence Alignment]]
 
** [[Multiple Sequence Alignment]]
** [[Genome Assembly]] (long and short reads)
+
** [[Genome Assembly]]
 
:; Algorithms on Structures
 
:; Algorithms on Structures
 
** [[Docking]]
 
** [[Docking]]
** [[Protein Structure Prediction]] (''ab initio'')
+
** Protein Structure Prediction <!-- ''ab initio'' -->
 
:; Algorithms on Trees
 
:; Algorithms on Trees
** [[Computing with trees]] (Bayesian approaches for phylogenetic trees, tree comparison)
+
** Computing with trees <!-- Bayesian approaches for phylogenetic trees, tree comparison) -->
 
:; Algorithms on Networks
 
:; Algorithms on Networks
** [[Network metrics]] (Degree distributions, Centrality metrics, other metrics on topology, small-world- vs. random-geometric controversy)
+
** Network metrics <!-- (Degree distributions, Centrality metrics, other metrics on topology, small-world- vs. random-geometric controversy) -->
 
*** [[Dijkstras Algorithm]]
 
*** [[Dijkstras Algorithm]]
 
*** [[Floyd Warshall Algorithm]]
 
*** [[Floyd Warshall Algorithm]]
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;Communication and collaboration
 
;Communication and collaboration
 
* [[MediaWiki]]
 
* [[MediaWiki]]
* [[Web Communication]]
 
 
* [[HTML essentials]]
 
* [[HTML essentials]]
 
* [[HTML 5]]
 
* [[HTML 5]]
 
* [[SADI|SADI Semantic Automated Discovery and Integration]]
 
* [[SADI|SADI Semantic Automated Discovery and Integration]]
* [[CGI]] (introduction, configuring apache, executing a script, query string input)
+
* [[CGI]]
  
 
; Statistics
 
; Statistics
 
* [[Pattern discovery]]
 
* [[Pattern discovery]]
* [[Correlation]] (Covariance matrices and their interpretation, application to large problems, collaborative filtering, MIC and MINE)
+
* Correlation <!-- (Covariance matrices and their interpretation, application to large problems, collaborative filtering, MIC and MINE) -->
* [[Clustering methods]] (Algorithms and choice (including: hierarchical, model-based and partition clustering, graphical methods (MCL), flow based methods (RRW) and spectral methods). Implementation in R if possible)
+
* Clustering methods <!-- (Algorithms and choice (including: hierarchical, model-based and partition clustering, graphical methods (MCL), flow based methods (RRW) and spectral methods). Implementation in R if possible) -->
* [[Cluster metrics]] (Cluster quality metrics (Akaike, BIC)–when and how)
+
* Cluster metrics <!-- (Cluster quality metrics (Akaike, BIC)–when and how) -->
* [[Map equation|The Map Equation]] (A network flow approach to hierarchical partitioning of large datasets)
+
* [[Map equation|The Map Equation]]  
* [[Machine learning]] (Classification problems: Neural Networks, HMMs, SVM..)
+
* Machine learning <!-- (Classification problems: Neural Networks, HMMs, SVM..) -->
 
* [[R]]
 
* [[R]]
** [[R plotting]]
+
** R plotting
** [[R programming]] (Scope, passing parameters, scalars, matrices, lists, functions)
+
** [[R programming]]
** [[R EDA]]
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** R EDA
** [[R regression]] (linear and non-linear, Calculation, confidence limits and interpretation)
+
** R regression
** [[R PCA]]
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** R PCA
** [[R Clustering]]
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** R Clustering
** [[R Classification]] (Phrasing inquiry as a classification problem, dealing with noisy data, machine learning approaches to classification, implementation in R)
+
** R Classification <!-- Phrasing inquiry as a classification problem, dealing with noisy data, machine learning approaches to classification, implementation in R) -->
** [[R hypothesis testing]] (Major approaches and when to apply them (including simulation methods for arbitrary PDFs))
+
** R hypothesis testing
** [[Bioconductor]] (Scope, contents, highlights use)
+
** [[Bioconductor]]
  
 
;Applications
 
;Applications
* [[Data integration]] (BioMart: Biodata integration, and data-mining of complex, related, descriptive data)
+
* [[Data integration]] <!-- Add BioMart: Biodata integration, and data-mining of complex, related, descriptive data -->
* [[Text mining]] (Use cases, tasks and metrics, taggers, vocabulary mapping, Practicals: R-support, Python/Perl support, others...)
+
* Text mining <!-- (Use cases, tasks and metrics, taggers, vocabulary mapping, Practicals: R-support, Python/Perl support, others...) -->
 
* [[HMMER]]
 
* [[HMMER]]
* [[High-throughput sequencing]]
+
* High-throughput sequencing
* [[Functional annotation]] (GFF)
+
* Functional annotation <!-- GFF -->
* [[Microarray analysis]] (... in R: differential expression and multiple testing; Loading and normalizing data, calculating differential expression, LOWESS, the question of significance, FWERs: Bonferroni and FDR; SAM and LIMMA)
+
* Microarray analysis <!-- (... in R: differential expression and multiple testing; Loading and normalizing data, calculating differential expression, LOWESS, the question of significance, FWERs: Bonferroni and FDR; SAM and LIMMA) -->
  
 
</div>
 
</div>

Revision as of 17:46, 18 September 2012


Hardware
  • High performance computing
  • Cloud computing
Systems and Tools
Programming
Algorithms on Sequences
Algorithms on Structures
    • Docking
    • Protein Structure Prediction
Algorithms on Trees
    • Computing with trees
Algorithms on Networks
Communication and collaboration
Statistics
Applications