bioinfolec_7th_20071005
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This document contains code and calculations related to measuring the overlap between two gene sets (CDK2 and CDK6) using Simpson's index. It performs searches on the PubMed database to retrieve the number of publications associated with each gene individually and combined. It then calculates Simpson's index as the proportion of shared publications out of the total for the smaller set. The index value of 0.4326 is returned, indicating moderate overlap between the literature on CDK2 and CDK6.
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This document describes the Apriori algorithm for frequent itemset mining. The Apriori algorithm uses a "bottom-up" approach, where frequent subsets are extended one item at a time to generate larger itemsets. To reduce the number of candidate itemsets, the algorithm prunes any itemset whose subset is not frequent. It performs multiple passes over the transaction database and uses a hash-tree structure to count candidate itemsets efficiently.
Datamining 8th hclustering
The document describes and compares different hierarchical clustering algorithms:
1) Single-link clustering connects clusters based on the closest pair of patterns, forming elongated clusters. Complete-link connects based on the furthest pair, forming more compact clusters.
2) Complete-link is more useful than single-link for most applications as it produces more interpretable hierarchies. However, single-link can extract certain cluster types that complete-link cannot, like concentric clusters.
3) Average group linkage connects clusters based on the average distance between all pairs of patterns in the two clusters. It provides a balance between single and complete link.
Datamining r 4th
1. The document discusses classification algorithms on two datasets: IRIS and USPS.
2. For IRIS, it performs k-Nearest Neighbors (k-NN) classification using 4 features to predict the class of iris flowers.
3. For USPS, it evaluates k-NN for digit recognition on images labeled 0-9, calculating distances between test and training points for varying values of k to optimize classification.
Datamining r 3rd
This document demonstrates using naive Bayes classification to analyze two datasets - contacts and iris data. For each dataset, the data is split into a training set and test set. A naive Bayes classifier model is generated from the training set and used to predict the classes of the test set. The predictions are then compared to the actual classes in the test set to evaluate the accuracy of the naive Bayes model. For both datasets, the naive Bayes model is able to accurately predict most of the test instances.
Datamining r 2nd
The document discusses analyzing a contacts dataset using R. It loads the contacts data, explores various attributes, builds a classification tree to predict "Young" status, and discusses parameter tuning. It also loads iris data, builds a classification tree to predict species using rpart with cp=0.1, plots the tree and data, and performs prediction on a test set with over 96% accuracy.
Datamining 2nd decisiontree
The document discusses several machine learning algorithms and techniques. It introduces classification, pattern recognition, clustering, association rule learning. It then covers decision trees in more detail, explaining the exact cover by 3-set problem, ID3 algorithm, CART, and C4.5 decision tree induction. Random forests are also mentioned briefly. Examples are provided to illustrate calculation of information gain and entropy measures.
Datamining 7th kmeans
The document contains information about k-means clustering:
(1) It describes the basic k-means clustering algorithm which assigns data points to k clusters by minimizing the within-cluster sum of squares.
(2) It provides details on how k-means clustering is implemented, including randomly initializing cluster centers, assigning points to the closest center, and recalculating centers as the mean of each cluster.
(3) It notes some of the challenges with k-means clustering, including that it does not work well for non-convex clusters and can get stuck in local optima depending on random initialization.
Datamining 8th Hclustering
The document describes hierarchical clustering algorithms. It compares the single-link and complete-link algorithms. Single-link produces elongated clusters by connecting nearby points, while complete-link produces more compact clusters by only merging groups whose furthest points are close. Complete-link generally produces more useful hierarchies but is less versatile than single-link. Average linkage is also mentioned as an alternative that calculates distances between groups as the average of all point-point distances.
Datamining 9th Association Rule
The document describes the Apriori algorithm for frequent itemset mining and association rule learning over transactional data. Apriori uses a bottom-up approach where frequent subsets are extended one item at a time, and groups of candidates are tested against the data. The algorithm performs multiple passes over the data and prunes itemsets whose subsets are infrequent. This process counts transaction IDs where itemsets occur and allows mining associations between items in large datasets.
Datamining 7th Kmeans
The document contains data from a k-means clustering algorithm with 5 clusters. It shows the cluster assignments of 50 data points to the 5 clusters over 10 iterations. The data points are numbered 0-49 on the x-axis and assigned to clusters 0-4 on the y-axis. The cluster assignments change over the 10 iterations as the k-means algorithm converges.
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bioinfolec_7th_20071005
- 7. X X ∩Y Y p(x, y) I(X; Y ) = p(x, y) log p(x)p(y) y∈Y x∈X |X ∩ Y | min(|X|, |Y |)
- 8. $ curl quot;http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi? db=pubmed&term=CDK2quot; <?xml version=quot;1.0quot;?> <!DOCTYPE eSearchResult PUBLIC quot;-//NLM//DTD eSearchResult, 11 May 2002//ENquot; quot;http://www.ncbi.nlm.nih.gov/entrez/query/DTD/ eSearch_020511.dtdquot;> <eSearchResult> <Count>3778</Count> <RetMax>20</RetMax> <RetStart>0</RetStart> <IdList> <Id>17904841</Id> <Id>17904366</Id> <Id>17893107</Id> () </eSearchResult>
- 9. $ curl quot;http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi? db=pubmed&term=CDK6quot; () <eSearchResult> <Count>740</Count> <RetMax>20</RetMax> <RetStart>0</RetStart> () </eSearchResult> $ curl quot;http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi? db=pubmed&term=CDK2+CDK6quot; () <eSearchResult> <Count>321</Count> <RetMax>20</RetMax> <RetStart>0</RetStart> () </eSearchResult>
- 10. 321 |X ∩ Y | = min(|X|, |Y |) min(3778, 740) 321 = = 0.438 740
- 13. $ ruby simpson.rb CDK2 CDK6 CDK2 CDK6 3778 742 321 0.432614555256065
- 14. #!/usr/bin/env ruby require 'rexml/document' require 'open-uri' def count(gene) fp = open(quot;http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi? db=pubmed&term=quot;+gene) source = fp.read fp.close doc = REXML::Document.new source return doc.elements['/eSearchResult/Count'].text.to_i end def simpson(gene1_count, gene2_count, gene12_count) if gene1_count <= 0 || gene2_count <= 0 return nil elsif gene1_count < gene2_count return gene12_count.to_f / gene1_count.to_f end return gene12_count.to_f / gene2_count.to_f end
- 15. def main(gene1,gene2) gene1_count = count(gene1) gene2_count = count(gene2) gene12_count = count(gene1 + quot;+quot; + gene2) s = simpson(gene1_count, gene2_count, gene12_count) puts [gene1, gene2, gene1_count, gene2_count, gene12_count, s].join (quot; quot;) end main(ARGV[0],ARGV[1])