iPredCNC

A Cascade Sub set Feature selection of Cancer lectins discrimination model

A Brief

iPredCNC a new bioinformatics feature encoding tool, for targeting cancer lectins from protein sequence solely. iPredCNC, first encode the features through Casncade federated feature source, achives best performance via ANN

Feature Used

CTD (Composition Transition Distribution
Moran
Geary
NMBA
QSOD
KSGPAAC ( K Spaced Group Pairs Amino Acid Composition)
Pseudo AAC (Amino Acid Composition)
Conjoint Traid

Algorithm

ANN Best performance

Getting Started

Following are list of todo, before making run of the propose model.

Prerequisites

Following are the lib need to be installed....

What things you need to install the software and how to install them

python3.6 follow
keras 2.2.4 follow
Flask 1.0.2 follow
scikit-learn 0.19.1 follow
scipy 1.1.0 follow
numpy1.15.4 follow
matplotlib3.0.2 follow
tensorflow 1.12.0 follow

$pip install <lib_name>

Evaluate a model.

EvaluateModel.py

EvaluateModel.py is used for Evaluating a pre-trained model, stored for each successive layeres 1&2.

Making prediction.

ModelPrediction.py

ModelPrediction.py is used to use to Obtained class probibilities for successive Layeres 1&2 on feeding and unseen Fasta Sequence or Sequence File.

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

Inspiration.

zaheerkhancs/iPredCNC