https://github.com/mrzresearcharena/irecspot

iRecSpot-EF: Effective Sequence Based Features for Recombination Hotspot Prediction
https://github.com/mrzresearcharena/irecspot

bioinformatics computational-biology genomics machine-learning recombination-hotspot-prediction

Last synced: about 1 month ago
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iRecSpot-EF: Effective Sequence Based Features for Recombination Hotspot Prediction

• Host: GitHub
• URL: https://github.com/mrzresearcharena/irecspot
• Owner: mrzResearchArena
• License: gpl-3.0
• Created: 2018-03-07T20:19:39.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2020-08-08T08:20:47.000Z (over 4 years ago)
• Last Synced: 2023-10-19T23:54:15.783Z (about 1 year ago)
• Topics: bioinformatics, computational-biology, genomics, machine-learning, recombination-hotspot-prediction
• Language: Python
• Homepage: http://rafsanjani.pythonanywhere.com/
• Size: 901 KB
• Stars: 7
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## iRecSpot-EF: Effective Sequence Based Features for Recombination Hotspot Prediction

### Installation Process :

    Required Python Packages:

    Install: python (version >= 3.5)

    Install: sklearn (version >= 0.19.0)

    Install: numpy (version >= 1.13.0)

    Install: pandas (version >= 0.21.0)

    Install: matplotlib (version >= 2.1.0)

    Install: pickle

    pip install < package name >

    example: pip install sklearn

    or

    We can download from anaconda cloud.

### Code Description :

- **Features Extraction :**

  ```console

  user@machine:~$ python extractionFeatures.py

  ```

  Note #1: It will provide a dataset named **fullDataset.csv** from FASTA sequences.

  ([fullDataset.csv](https://drive.google.com/file/d/1-DHKnHMcVDZATUYZ8BwQzdLUAWQJRxyg/))

  Note #2: **readXY.py** ( This file will fetch data from **hotSpot.fasta** and **coldSpot.fasta** files. )

- **Features Selection :**

  ```console

  user@machine:~$ python selectionFeatures.py

  ```

  Note #1: It will provide a dataset named **selectedDataset.csv** from **fullDataset.csv**.

- **Training Model :**

  ``` console

  user@machine:~$ python modelDump