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Peptide Identification Using Tandem Mass Spectrometry

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Compression And K-mer Based Approach For Anticancer Peptide Analysis.

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Compression And K-mer Based Approach For Anticancer Peptide Analysis.

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Compression and k-Mer Based Approach for Anticancer Peptide Analysis.

Sarwan Ali, Tamkanat E Ali, Prakash Chourasia

IEEE Transactions on Computational Biology and Bioinformatics

|April 17, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel, parameter-free method for anti-cancer peptide (ACP) classification using Gzip compression and incremental k-mers. It achieves state-of-the-art results, offering an efficient alternative for cancer treatment development.

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Area of Science:

Bioinformatics
Computational Biology
Cancer Research

Background:

Anti-cancer peptide (ACP) sequence classification is vital for advancing cancer treatments.
Current deep learning models for ACP classification demand extensive data and parameters.
Existing compression methods may overlook crucial local sequence information.

Purpose of the Study:

To develop a novel, efficient, and parameter-free method for ACP sequence classification.
To preserve fine-grained amino acid context lost in traditional compression techniques.
To provide a viable alternative to computationally intensive deep learning models.

Main Methods:

Integration of Gzip compression with an incremental k-mer strategy.
Compression of individual k-mers and sequential building of subsequence compressions.

Utilizing Normalized Compression Distance (NCD) and kernel-based embeddings for classification.

Main Results:

Achieved state-of-the-art performance on breast and lung cancer ACP datasets.
Outperformed deep neural networks and large language models in classification accuracy.
Demonstrated effectiveness without requiring custom features or pre-trained models.

Conclusions:

The proposed Gzip-based incremental k-mer method offers a practical and efficient approach to ACP classification.
This parameter-free technique excels in low-resource environments, overcoming limitations of current methods.
The approach preserves essential amino acid-level context for improved classification accuracy in cancer research.