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Monday, December 20, 2010
Saturday, December 18, 2010
User-defined Functions:gcufbioinfo
2:53 PM
GCbioinfo
Let's create another function. How about reverse?
>>> def reverse(s):
... """Return the sequence string in reverse order."""
... letters = list(s)
... letters.reverse()
... return ''.join(letters)
...
>>> reverse('CCGGAAGAGCTTACTTAG')
'GATTCATTCGAGAAGGCC' There are a few new things in this function that need explanation. First, we've used an argument name of "s" instead of "dna". You can name your arguments whatever you like in Python. It is something of a convention to use short names based on their expected value or meaning. So "s" for string is fairly common in Python code. The other reason to use "s" instead of "dna" in this example is that this function works correctly on any string, not just strings representing dna sequences. So "s"...
User-defined Functions:gcufbioinfo
2:29 PM
GCbioinfo
Here is the process for creating your own function in Python. The first line begins with the keyword def, is followed by the name of the function and any arguments (expected input values) surrounded by parentheses, and ends with a colon. Subsequent lines make up the body of the function and must be indented. If a string comment appears in the first line of the body, it becomes part of the documentation for the function. The last line of a function returns a result.
Let's define some functions in the PyCrust shell. Then we can try each function with some sample data and see the result returned by the function.
>>> def transcribe(dna):...
Python Functions:gcufbioinfo
1:41 PM
GCbioinfo
Functions perform an operation on one or more values and return a result. Python comes with many pre-defined functions, as well as the ability to define your own functions. Let's look at a couple of the built-in functions: len() returns the number of items in a sequence; dir() returns a list of strings representing the attributes of an object; list() returns a new list initialized from some other sequence.
>>> dna = 'CTGACCACTTTACGAGGTTAGC'
>>> bases = ['A', 'C', 'G', 'T']
>>> len(dna)
22
>>> len(bases)
4
>>> dir(dna)
['__add__', '__class__', '__contains__', '__delattr__',
'__doc__', '__eq__', '__ge__', '__getattribute__', '__getitem__',
'__getslice__', '__gt__', '__hash__', '__init__', '__le__',
'__len__', '__lt__', '__mul__', '__ne__',...
Python Lists:gcufbioinfo
1:20 PM
GCbioinfo
Where Python strings are limited to characters, Python lists have no limitations. Python lists are ordered sequences of arbitrary Python objects, including other lists. In addition, you can insert, delete and replace elements in a list. Lists are written as a series of objects, separated by commas, inside of square brackets. Let's look at some lists, and some operations you can perform on lists.
>>> bases = ['A', 'C', 'G', 'T']
>>> bases
['A', 'C', 'G', 'T']
>>> bases.append('U')
>>> bases
['A', 'C', 'G', 'T', 'U']
>>> bases.reverse()
>>> bases
['U', 'T', 'G', 'C', 'A']
>>>...
Python Strings:gcufbioinfo
1:05 PM
GCbioinfo
Let's take a look at the example code in more detail. The first thing we did was to create a string and assign it to a variable. Strings in Python are sequences of characters. You create a string literal by enclosing the characters in single ('), double (") or triple (''' or """) quotes. In the example we assigned the string literal CTGACCACTTTACGAGGTTAGC to the variable named dna.
>>> dna = 'CTGACCACTTTACGAGGTTAGC'Then we simply typed the name of the variable, and Python responded by displaying the value of that variable, surrounding the value with quotes to remind us that the value is a string.
>>> dna
'CTGACCACTTTACGAGGTTAGC'...
Beginning Python for Bioinformatics:gcufbioinfo
12:36 PM
GCbioinfo
Bioinformatics, the use of computers in biological research, is the newest wrinkle on one of the oldest pursuits--trying to uncover the secret of life. While we may not know all of life's secrets, at the very least computers are helping us understand many of the biological processes that take place inside of living things. In fact, the use of computers in biological research has risen to such a degree that computer programming has now become an important and almost essential skill for today's biologists.
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What I concluded:
6:57 AM
GCbioinfo
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ConclusionFrom all these research papers I have concluded that from all the data mining techniques PCA is considered to be the best because it reduces large data set into smaller ones and narrow down our research that we can useful information from small data sets. Dimension of a large dataset can be reduced by using principal component analysis which is considered as...
Predicting Breast Cancer Survivability Using Data Mining Techniques
6:55 AM
GCbioinfo
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Last edited byQuratt ul ain Siddique
Summary:This paper presents the prediction of the survivability rate of cancer using data mining techniques. In this paper scientists investigated three data mining like Naïve Bayes, the back-propagated neural network, and the C4.5 decision tree algorithms. C4.5 decision tree algorithm is considered to best from remaining two methods....
Combined Supervised and Unsupervised Learning in Genomic Data Mining
6:50 AM
GCbioinfo
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Last edited byQuratt ul ain Siddique
Summary:In this paper they introduced the most comprehensive method for predicting the function of proteins. Their approach differs in several respects from the earlier work in that it uses a multistage decomposition that makes use of both unsupervised and supervised machine learning techniques; they refer to this as Unsupervised-Supervised...
Acute Coronary Syndrome Prediction Using Data Mining Techniques- An Application
6:45 AM
GCbioinfo
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Last edited byQuratt ul ain Siddique
Summary:In this research paper data mining techniques are used to investigate the factors that are responsible for enhancing the risk of acute coronary syndrome. They have applied binary regression to factors that effecting the dependent variable. For the better performance of regression model in predicting coronary syndrome the reduction...
Biological Data Mining for Genomic Clustering Using Unsupervised Neural Learning:
6:40 AM
GCbioinfo
Last edited by
Quratt ul ain Siddique
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Summary:
Among the well-known techniques of DNA-string matching are the Smith-Waterman algorithm, for local alignment, the Needleman-Wunsch algorithm for global alignment, Hidden Markov’s model, matrix model, evolutionary algorithms for multiple sequence alignment etc. These works, though extremely valuable, have their limitations.Principal...
Data Mining and Visualization of Mouse Genome Data:
6:35 AM
GCbioinfo
Last edited by
Quratt ul ain Siddique
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Summary:
This paper discusses the data mining of the genomics of the mouse that is an area of importance because of its relationship to understanding of basic genetics of other mammals and in particular the...
