Related Experiment Video
Updated: Oct 25, 2025
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Review and Comparative Analysis of Machine Learning-based Predictors for Predicting and Analyzing Anti-angiogenic
Phasit Charoenkwan1, Wararat Chiangjong2, Md Mehedi Hasan3
1Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand.
Abstract:
Cancer is one of the leading causes of death worldwide and the underlying angiogenesis represents one of the hallmarks of cancer. Efforts are already under way for the discovery of anti-angiogenic peptides (AAPs) as a promising therapeutic route, which tackle the formation of new blood vessels. As such, the identification of AAPs constitutes a viable path for understanding their mechanistic properties pertinent for the discovery of new anti-cancer drugs. In spite of the abundance of peptide sequences in public databases, experimental efforts in the identification of anti-angiogenic peptides have progressed very slowly owing to high expenditures and laborious nature. Owing to its inherent ability to make sense of large volumes of data, machine learning (ML) represents a lucrative technique that can be harnessed for peptide-based drug discovery. In this review, we conducted a comprehensive and comparative analysis of ML-based AAP predictors in terms of their employed feature descriptors, ML algorithms, cross-validation methods and prediction performance. Moreover, the common framework of these AAP predictors and their inherent weaknesses are also discussed. Particularly, we explore future perspectives for improving the prediction accuracy and model interpretability, which represent an interesting avenue for overcoming some of the inherent weaknesses of existing AAP predictors. We anticipate that this review would assist researchers in the rapid screening and identification of promising AAPs for clinical use.
Insights
Machine learning (ML) accelerates the discovery of anti-angiogenic peptides (AAPs) crucial for cancer therapy. This review analyzes ML predictors, highlighting their strengths and weaknesses to guide future development of novel anti-cancer drugs.
Area of Science:
- Biochemistry and Molecular Biology
- Computational Biology and Bioinformatics
- Pharmacology and Drug Discovery
Background:
- Angiogenesis is a key hallmark of cancer, driving tumor growth and metastasis.
- Anti-angiogenic peptides (AAPs) offer a promising therapeutic strategy by inhibiting new blood vessel formation.
- Experimental identification of AAPs is slow and costly, necessitating advanced computational approaches.
Purpose of the Study:
- To conduct a comprehensive comparative analysis of machine learning (ML)-based AAP predictors.
- To evaluate the feature descriptors, ML algorithms, cross-validation methods, and prediction performance of existing AAP predictors.
- To identify weaknesses in current ML-based AAP prediction frameworks and explore future directions for improvement.
Main Methods:
- Systematic review and comparative analysis of published ML-based AAP predictors.
- Evaluation of diverse feature descriptors and ML algorithms used in AAP prediction.
- Assessment of cross-validation strategies and prediction performance metrics.
Main Results:
- Identified common frameworks and inherent weaknesses across various ML-based AAP predictors.
- Highlighted variability in feature descriptors, algorithms, and validation methods.
- Demonstrated the potential of ML in accelerating AAP discovery compared to traditional experimental methods.
Conclusions:
- ML offers a powerful approach to overcome the limitations of experimental AAP identification.
- Improving prediction accuracy and model interpretability is crucial for advancing ML-based AAP discovery.
- This review provides insights for researchers to rapidly screen and identify potential AAPs for anti-cancer drug development.
More Related Videos
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
09:03Monitoring Functionality and Morphology of Vasculature Recruited by Factors Secreted by Fast-growing Tumor-generating Cells
Published on: November 23, 2014