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Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

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Related Experiment Video

Updated: Jun 4, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature

Shahid¹, Maqsood Hayat², Wajdi Alghamdi³

¹Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, 23200, KP, Pakistan.

Scientific Reports

|January 3, 2025

Summary

This summary is machine-generated.

This study introduces pACP-HybDeep, a novel computational model for predicting anticancer peptides. The model achieves high accuracy, offering a reliable tool for cancer drug discovery and development.

Keywords:

Anticancer peptides Binary tree growth feature selection Deep Hybrid neural network Physiochemical properties Transformer encoder

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Computational Biology
Biotechnology
Drug Discovery

Background:

Cancer poses a global health challenge with high mortality rates.
Traditional cancer therapies have limitations, including cost and side effects.
Anticancer peptides offer targeted action with minimal adverse effects.

Purpose of the Study:

To develop a highly reliable and effective computational model for predicting anticancer peptides.
To address the limitations of existing cancer treatment methods.
To aid researchers in academia and pharmaceutical drug design.

Main Methods:

Numerical encoding of peptides using an attention-based ProtBERT-BFD encoder for semantic features.
Integration of CTDT-based structural information.
Feature selection using a k-nearest neighbor-based binary tree growth (BTG) algorithm.
Training with a CNN+RNN-based deep learning model.

Main Results:

The pACP-HybDeep model achieved a training accuracy of 95.33% and an AUC of 0.97.
Independent dataset validation yielded accuracies of 94.92% (Ind-S1), 92.26% (Ind-S2), and 91.16% (Ind-S3).
The model demonstrated high efficacy and reliability on test datasets.

Conclusions:

The pACP-HybDeep model is a valuable and reliable tool for predicting anticancer peptides.
This computational approach can significantly advance cancer drug discovery.
The model's performance supports its application in pharmaceutical research and development.