바이오파이썬

About 바이오파이썬 Android App

생명 및 뇌과학 정보의 생산성 있는 분석 및 시각화를 위한 IT 실습

part I Practical Computing for Biologists
Data analysis with Python
Python Basics I
Python Basics II
Python Modules & NumPy and Matplotlib
Biopython I, II
Data management and relational databases
Data analysis with Python

part II Managing Your Biological Data with Python
Ipython notebook server setting on Biolinux 8 virtual machine
ch1. The Python Shell
ch2. Your First Python Program
ch3. Analyzing a Data Column
ch4. Parsing Data Records
ch5. Searching Data
ch6. Filtering Data
ch7. Managing Tabular Data
ch8. Sorting Data
ch9. Pattern Matching and Text Mining
ch10. Divide a Program into Functions
ch11. Managing Complexity with Classes
ch12. Debugging
ch15. Writing Good Programs
ch16. Creating Scientific Diagrams
ch17. Creating Molecule Images with PyMOL
ch18. Manipulating Images
ch19. Working with Sequence Data
ch20. Retrieving Data from Web Resources
ch21. Working with 3D Structure Data

part III Python for Bioinformatics
1. Introduction
2. NumPy and SciPy
3. Image Manipulation
4. Akando and Dancer Modules
5. Statistics
6. Parsing DNA Data Files
7. Sequence Alignment
8. Dynamic Programming
9. Tandem Repeats
10. Hidden Markov Models
11. Genetic Algorithms
12. Multiple Sequence Alignment
13. Gapped Alignments
14. Trees
15. Text Mining
16. Complexity
17. Clustering
18. Self-Organizing Maps
19. Principals
20. Species Identification
21. Fourier Transforms and Correlations
22. Correlations
23. Numerical Sequence Alignment
24. Gene Expression Array Files
25. Spot Finding and Measurement
26. Spreadsheet Arrays and Displaying the Data
27. Applications with Expression Arrays

Building Machine Learning Systems with Python(한국어판)
1장. 기계 학습 파이썬으로 시작하기
2장. 실제 예제를 이용한 분류법 학습
3장. 군집화: 관련된 게시물 찾기
4장. 주제 모델링
5장. 분류 I: 형편없는 답변 감지
6장. 분류 II: 감성 분석
7장. 회귀: 추천
8장. 회귀: 향상된 추천
9장. 분류III: 음악 장르 분류
10장. 컴퓨터 비전: 패턴 인식
11장. 차원 수 줄이기
12장. (조금 더 큰) 빅데이터
13장. 기계 학습을 더 배울 수 있는 자료

링크
http://biopy.github.io
[github] https://github.com/biopy/biopy.github.io
[facebook] http://goo.gl/GJUxC6
[ipython notebooks] http://goo.gl/IGfjoOIT lab for analysis and visualization of productive life and brain science information

part I Practical Computing for Biologists
Data analysis with Python
Python Basics I
Python Basics II
Python Modules & NumPy and Matplotlib
Biopython I, II
Data management and relational databases
Data analysis with Python

part II Managing Your Biological Data with Python
Ipython notebook server setting on Biolinux 8 virtual machine
ch1. The Python Shell
ch2. Your First Python Program
ch3. Analyzing a Data Column
ch4. Parsing Data Records
ch5. Searching Data
ch6. Filtering Data
ch7. Managing Tabular Data
ch8. Sorting Data
ch9. Pattern Matching and Text Mining
ch10. Divide a Program into Functions
ch11. Managing Complexity with Classes
ch12. Debugging
ch15. Writing Good Programs
ch16. Creating Scientific Diagrams
ch17. Creating Molecule Images with PyMOL
ch18. Manipulating Images
ch19. Working with Sequence Data
ch20. Retrieving Data from Web Resources
ch21. Working with 3D Structure Data

part III Python for Bioinformatics
1. Introduction
2. NumPy and SciPy
3. Image Manipulation
4. Akando and Dancer Modules
5. Statistics
6. Parsing DNA Data Files
7. Sequence Alignment
8. Dynamic Programming
9. Tandem Repeats
10. Hidden Markov Models
11. Genetic Algorithms
12. Multiple Sequence Alignment
13. Gapped Alignments
14. Trees
15. Text Mining
16. Complexity
17. Clustering
18. Self-Organizing Maps
19. Principals
20. Species Identification
21. Fourier Transforms and Correlations
22. Correlations
23. Numerical Sequence Alignment
24. Gene Expression Array Files
25. Spot Finding and Measurement
26. Spreadsheet Arrays and Displaying the Data
27. Applications with Expression Arrays

Building Machine Learning Systems with Python (Korean Version)
Chapter 1. Getting Started with Machine Learning Python
Chapter 2. Taxonomy learning using real-world examples
Chapter 3. Clustering: Find related posts
Chapter 4. Topic Modeling
Chapter 5. Category I: a lousy answer detection
Chapter 6. Classification II: sensitivity analysis
Chapter 7. Regression: Recommended
Chapter 8. Regression: Improved recommendation
Chapter 9. Category III: music genre classification
Chapter 10. Computer vision: pattern recognition
Chapter 11. D can reduce
Chapter 12. (Slightly larger) Big Data
Chapter 13. Material that can be a machine learning to learn more

Link
http://biopy.github.io
[Github] https://github.com/biopy/biopy.github.io
[Facebook] http://goo.gl/GJUxC6
[Ipython notebooks] http://goo.gl/IGfjoO