Note: mACPpred A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides

Note: mACPpred A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides

- March 23, 2021

Note: mACPpred A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides

doi: 10.3390/ijms20081964

Gap;

Accurate prediction of ACPs -- problematic in field of immunoinformatics
Performance of ML is not that good, at current moment

This study:

Present novel approaches to precisely predict

Applying two-step feature selection protocol

Contain 7 features that embedded in AA sequences

Composition-based
Physicochemical properties
Profiles

Obtained their corresponding optimal feature-based models

Predicted probability feature vectors

Used as input to support vector machine

Intro;

ACP -- 5-50 AA

In depth mechanisms toward cancers are unknown but thought to be related with membranolytic activities

Identify novel ACP -- time consuming, thus using computational method to predict ACP based on the AA sequences before synthesis -- cost-saving

This study;

Dataset with a lowest redundancy benchmark
Using this dataset to extract feature and building the model

7 features from AA sequences (เป็น molecular descriptor ที่ใช้กับ peptide/protein)

amino acid composition (AAC),
dipeptide composition (DPC),
composition-transition-distribution (CTD),
quasi-sequence-order (QSO),
amino acid index (AAIF),
binary profile (NC5),
conjoint triad (CTF)

Output ในการทำนาย -- ACP and non-ACP

Go to PyBioMed - https://pybiomed.readthedocs.io/en/latest/index.html -- it briefly explains the molecular features of protein.

ใช้ 7 feature ในการหา molecular feature แล้วมาเลือกอีกทีว่า feature ตัวไหนน่าจะมีความสำคัญต่อการเอามาใช้สร้างโมเดลการทำนาย (using machine learning model approach)

Machine learning approaches that they use/ pick one that shows a good performance

Support vector machine learning
Random Forest
K-nearest neighbors (KNN)
Logistic regression

Using 10 fold-cross validation to validate the result of ML -- SVM the most prominent

Then, they realize that some features could be removed since some generate noise.
จริง ๆ แล้ว มีหลายเทคนิคในการที่จะใช้เป็นหลักเกณฑ์ในการเลือก features แต่เขาเลือกใช้ two-step feature

Web Server:

www.thegleelab.org/mACPpred

Downloading dataset:

http://thegleelab.org/mACPpred/ACPData.html

Novelty of this method;

Benchmark or training dataset -- lowest redundancy
First study to employ, CTF and QSO, as features to predict ACP
Most of the existing predictors

Use one feature encoding
Combination of multiple features encoding -- too complex to predict ACP (feature dimension ~ high)

Room for improvement

Using other ML algorithms

Decision tree-based
Neural network-based algorithms

- Incorporation of “novel” features/computational approaches

- Increase the size of training dataset -- require experimental data to do this

Features for peptides

Sequenced based features

Physicochemical properties-based features

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