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Python实现生物信息学算法设计与实践指南

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"《生物信息学算法：Python设计与实现》是一本由Miguel Rocha和Pedro G. Ferreira合著的专业书籍，两位作者分别来自葡萄牙University of Minho和Ipatimup/i3S。本书隶属于学术出版社Academic Press，是Elsevier的一个分支，总部位于英国伦敦和美国的San Diego、Cambridge以及美国的Kidlington等地。该书出版于2018年，版权归属于Elsevier公司，所有复制或传播必须获得书面许可。本书内容深入浅出，详细介绍了生物信息学算法的设计和Python语言的实现方法。生物信息学是研究生命科学中的数据处理、分析和解读的学科，它在基因组学、蛋白质组学、代谢组学等领域发挥着关键作用。通过学习本书，读者可以掌握如何利用Python这种强大的编程语言来解决生物信息学中的各种问题，如序列比对、基因功能预测、结构生物学分析等。书中涵盖了丰富的实践案例和理论讲解，不仅包括基本概念的阐述，还涉及高级技术的探讨，帮助读者从入门到进阶，逐步提升在生物信息学领域的能力。此外，书中还强调了版权保护的重要性，并提供了关于Elsevier版权政策和如何获取许可的详细信息，确保读者在合法范围内进行学术活动。《生物信息学算法：Python设计与实现》是一本既实用又全面的教材，对于生物信息学专业学生、研究人员和对这一领域感兴趣的开发者来说，都是宝贵的学习资源。通过学习这本书，读者不仅能掌握生物信息学的核心算法，还能提升计算机编程和数据处理技能，适应现代生命科学研究的需求。"

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10 Chapter 2

If a variable is no longer being used it can be removed by using the del clause.

>>> del x

2.2.3 Numerical and Logical Variables

Numeric variables can be either integer, ﬂoating point (real numbers), or complex numbers.

Boolean variables can have a True or False value and are a particular case of the integer

type, corresponding to 1 and 0, respectively.

# integer

>>> sequence_length = 320

# floating

>>> average_score = 23.145

# Boolean

>>> is_sequence = True

>>> contains_substring = False

Multiple variables can also be assigned with the same value in a single line:

>>>a=b=c=1

Multiple values can be assigned to different variables in a single instruction, in the order they

are declared. In this case, variables and values are separated by commas:

>>>a,b,c=1,2,3

Assignments using expressions in the right hand side are possible. In this case, the evaluation

of the expression follows the arithmetic precedence rules.

>>> a = 2∗(1+2)

Variable values can also be swapped in the same line.

>>> a,b,c = c,a,b

The following binary arithmetic operators can be applied to two numeric variables:

• + –sum;

• - – difference;

• * – multiplication;

An Introduction to the Python Language 11

Table 2.2: Mathematical and character functions.

Function Description

abs(x) absolute value of x

round(x, n) x rounded to a precision of n places

pow(x, y) x raised to power of y

ord(c ) ASCII numerical code for character c

chr(x) ASCII string (with a single character) for numerical code x

• / – division;

• ** – exponentiation;

• // – integer division; and,

• % – modulus operator (remainder of the integer division).

All the usual arithmetic priorities apply here as well. Some examples are shown in the follow-

ing code:

>>> x = 5

>>> y = 4

>>> x + y

>>> x ∗ y

>>> x / y

>>>x//y

>>> z = 25

>>> z % x

>>> z % y

>>> x ∗∗ y

625

Table 2.2 describes examples of mathematical functions and functions to convert between

numerical values and characters based on the ASCII code.

Examples of the use of these functions follow:

>>> abs(−3)

12 Chapter 2

>>> round (3.4)

3.0

>>> float(2)

2.0

>>> int(3.4)

>>> int(4.6)

>>> int (−2.3)

−2

>>> 0.00000000000001

1e−14

>>> 2.3e−3

0.0023

>>> chr (97)

’a’

>>> ord(’a’)

The package math includes a vast number of useful mathematical and scientiﬁc functions,

including trigonometric functions (sin, cos, tan), square root (sqrt), and others as factorial,

logarithm (log) and power function (exp), where exp(x) returns e

. By importing this pack-

age, these functions become available in the current working session.

With these capacities, the interactive environment of Python becomes a powerful scientiﬁc

calculator, as shown in the examples below:

>>> import math

>>> math .sqrt (4)

2.0

>>> math .sin (0.5)

0.479425538604203

>>> math.log(x)

1.6094379124341003

>>> math .pi

3.141592653589793

>>> math .tan (math. pi)

−1.2246467991473532 e−16

>>> math .e

2.718281828459045

An Introduction to the Python Language 13

>>> math .exp(1)

2.718281828459045

>>> math .log (math. e)

1.0

Notice in the previous examples that the constants pi and e are also available within the pack-

age.

When updating a variable x through an arithmetic operation that depends on the current state

of x, the assignment operator can be preceded by a mathematical operator, +=, -=, *=, /=, %=

or **=. As an example, the two following expressions are equivalent:

# equivalent statements

>>>a+=3

>>> a = a+3

Given two Boolean variables x and y, the logical operations and, or,andnot provide a logi-

cal result of True or False, returning respectively the logical conjunction, disjunction, and

negation.

2.2.4 Containers

2.2.4.1 Lists

Lists allow the storage and processing of sequences of values of different types. They can be

deﬁned by square brackets enclosing a sequence of comma-separated values. The notation []

deﬁnes an empty list.

A list with the integer values from 1 to 5 and 7 can be declared as follows:

>>>x=[1,2,3,4,5,7]

Each of the values in a list can be accessed by an index that deﬁnes the position of the value

within the sequence. Indexes are integer values that range from 0 (ﬁrst position) to the number

of elements on the list minus 1 (last position). To access the third element of the previously

deﬁned list, we can use the syntax x[2]. Since lists are mutable objects, we can also directly

change their values, for instance with x[0]=−1, setting the ﬁrst element to be −1.

By using negative indexes, the elements of the list can be accessed backwards, where x[−1]

corresponds to the last element of the list, i.e. 7, x[−2] to the second last element, and so on.

Elements can also be removed from lists with the del statement.

14 Chapter 2

>>>x=[1,2,3,4,5,7]

>>> del x [4]

>>> x

[1 , 2 , 3 , 4 , 7]

The list object can handle heterogeneous data. Thus, as the example below shows, a list may

contain data from different types including other lists.

>>> y = [1, 2, "A", "B",[4,"C"]]

The + operator can be used to concatenate (join) lists together:

>>> [1 ,2 ,3] + [4 ,5 ,6]

[1,2,3,4,5,6]

Slicing is a powerful mechanism to generate sub-lists, i.e. lists containing selected el-

ements that preserve their order from the original list. The general syntax for slicing is

list_name[startslice : endslice : step]. Note that a more compact syntax for slicing can be used

by omitting some arguments. In the case where it is possible to omit arguments, default values

are assumed. Also, the endslice is always one position after the last selected element.

Examples of slicing on lists follow below:

>>> x

[1 , 2 , 3 , 4 , 7]

# elements from index 1 to 2

>>> x[1:3]

[2 , 3]

# elements from index 0 to 2

>>> x [:3]

[1 , 2 , 3]

# elements from index 3 to end of list

>>> x [3:]

[4 , 7]

# all elements but the last element

>>> x[:−1]

[1 , 2 , 3 , 4]

# every two elements

>>> x[::2]

[1 , 3 , 7]

# skipping first and last elements

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精简下面表达：Existing protein function prediction methods integrate PPI networks and multivariate bioinformatics data to improve the performance of function prediction. By combining multivariate information, the interactions between proteins become diverse. Different interactions’ functions in functional prediction are various. Combining multiple interactions simply between two proteins can effectively reduce the effect of false negatives and increase the number of predicted functions, but it can also increase the number of false positive functions, which contribute to nonobvious enhancement for the overall functional prediction performance. In this article, we have presented a framework for protein function prediction algorithms based on PPI network and semantic similarity with the addition of protein hierarchical functions to them. The framework relies on diverse clustering algorithms and the calculation of protein semantic similarity for protein function prediction. Classification and similarity calculations for protein pairs clustered by the functional feature are more accurate and reliable, allowing for the prediction of protein function at different functional levels from different proteomes, and giving biological applications greater flexibility.The method proposed in this paper performs well on protein data from wine yeast cells, but how well it matches other data remains to be verified. Yet until now, most unknown proteins have only been able to predict protein function by calculating similarities to their homologues. The predictions result of those unknown proteins without homologues are unstable because they are relatively isolated in the protein interaction network. It is difficult to find one protein with high similarity. In the framework proposed in this article, the number of features selected after clustering and the number of protein features selected for each functional layer has a significant impact on the accuracy of subsequent functional predictions. Therefore, when making feature selection, it is necessary to select as many functional features as possible that are important for the whole interaction network. When an incorrect feature was selected, the prediction results will be somewhat different from the actual function. Thus as a whole, the method proposed in this article has improved the accuracy of protein function prediction based on the PPI network method to a certain extent and reduces the probability of false positive prediction results.

本文提出了一种基于PPI网络和语义相似性，加上蛋白质分层功能的蛋白质功能预测算法框架，对酒葡萄酵母细胞的蛋白质数据表现出良好的效果，但其他数据的效果如何仍有待验证。此外，该框架中的功能特征选择的数量以及...

2型糖尿病发病是环境因素与遗传因素相互作用的结果，已被鉴定与其相关的基因超过400个，这些基因也与肥胖、脂代谢紊乱和心血管病等密切相关。然而，目前检测出来的许多基因突变是低频率、罕见的变异，它们对总体疾病发生风险的作用尚未完全阐明，也缺乏相对应的靶向药物。本研究主要是在前期研究基础上，期望能通过对个体基因组、蛋白质组、代谢组和临床表型的信息等进行多组学数据深度挖掘 (OWAS-DM)，利用GWAS关联的孟德尔遗传-蛋白组量化性状基因座 (MR-pQTL)，MR-mQTL (代谢组)，MR-eQTL (表型组) 共定位分析及生物信息学分析等多组学分析策略，发现2型糖尿病发生发展的潜在机制及标志物。并利用cMAP及分子对接等人工智能虚拟筛选技术结合基于实验的亲和质谱、表面等离子共振及高内涵等筛选技术，发展针对2型糖尿病靶标的高通量药物筛选技术，为糖尿病患者的个体化精准诊断和治疗提供新的手段。翻译成英文

proteomes, metabolomes, and clinical phenotypes (OWAS-DM), quantitative trait loci (MR-pQTL, MR-mQTL, MR-eQTL) associated with GWAS, and bioinformatics analysis. In addition, artificial intelligence ...

ComplexHeatmap version 2.17.0 Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/ Github page: https://github.com/jokergoo/ComplexHeatmap Documentation: http://jokergoo.github.io/ComplexHeatmap-reference If you use it in published research, please cite either one: - Gu, Z. Complex Heatmap Visualization. iMeta 2022. - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 2016. The new InteractiveComplexHeatmap package can directly export static complex heatmaps into an interactive Shiny app with zero effort. Have a try! This message can be suppressed by: suppressPackageStartupMessages(library(ComplexHeatmap))

谢谢你提供的信息！ComplexHeatmap 是一个用于可视化多维基因组数据的 R 包。你可以在 Bioconductor 页面和 Github 页面找到这个包的详细信息和文档。如果你在研究中使用了这个包，请引用 Gu Z....

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