Computational Systems Biology Methods and Protocols.7z

Table 1
The general key algorithms used in machine learning

Methods Description URL

C4.5 It is an algorithm used to generate a decision tree

as an extension of earlier ID3 algorithm, and

the decision trees can be used for classification

[23]

http://www.cs.waikato.ac.nz/ml/weka/

PageRank It is an algorithm used to rank websites by

Google search engine results underlying an

assumption that more important websites are

likely to receive more links from other

websites [24]

http://www.google.com

K-means It aims to partitionNsamples intoKgroups

where each sample belongs to the group with

the nearest mean, serving as a prototype of the

sample group [25]

https://github.com/mlpack/mlpack

Apriori It identifies the frequent individual items in the

database and extending them to larger and

larger item sets as long as those item sets

appear sufficiently often in the database [26]

http://www.borgelt.net/software.html

EM The expectation–maximization (EM) algorithm is

an iterative method to find maximum

likelihood or maximum posteriori estimates of

parameters in statistical models, where the

model depends on unobserved latent variables

[27]

http://wiki.stat.ucla.edu/socr/index.php/

SOCR_EduMaterials_Activities_2D_

PointSegmentation_EM_Mixture

AdaBoost It is a machine learning meta-algorithm used in

conjunction with many other types of learning

algorithms to improve their performance. The

individual learners can be weak, but as long as

the performance of each one is slightly better

than random guessing, the final model can be

proven to converge to a strong learner [28]

http://luispedro.org/software/milk/

KNN It is a nonparametric method used for

classification and regression, where the input

consists of thekclosest training examples in

the feature space and the output depends on

whether KNN is used for classification or

regression [29]

http://www.cs.waikato.ac.nz/ml/weka/

Naive Bayes They are a family of simple probabilistic classifiers

based on applying Bayes’ theorem with strong

(naive) independent assumptions between the

features [30]

http://jbnc.sourceforge.net/

Revisit of Machine Learning Supported Biological and Biomedical Studies 187