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Combination Therapies and Personalized Medicine

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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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Detection of Human Leukocyte Antigen Biomarkers in Breast Cancer Utilizing Label-free Biosensor Technology

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Identifying anticancer peptides by using improved hybrid compositions.

Feng-Min Li¹, Xiao-Qian Wang¹

¹College of Science, Inner Mongolia Agricultural University, Hohhot, 010018, China.

Scientific Reports

|September 28, 2016

Summary

This summary is machine-generated.

This study developed an improved predictor for identifying anticancer peptides, crucial for developing new cancer drugs. Combining amino acid composition features significantly enhanced prediction accuracy to 93.61%.

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Area of Science:

Biochemistry
Bioinformatics
Computational Biology

Background:

Cancer remains a leading global health threat, necessitating novel therapeutic strategies.
Anticancer peptides (ACPs) show promise as effective anticancer drugs.
Accurate identification of ACPs is critical for drug development.

Purpose of the Study:

To develop and validate an improved computational predictor for identifying anticancer peptides.
To evaluate the efficacy of different feature sets in predicting ACPs.

Main Methods:

Utilized support vector machine (SVM) algorithm for classification.
Employed amino acid composition (AAC), average chemical shifts (acACS), and reduced amino acid composition (RAAC) as predictive features.
Validated model performance using the jackknife test.

Main Results:

The predictor achieved a high overall accuracy of 93.61% in the jackknife test.
The combination of AAC, acACS, and RAAC features proved beneficial for accurate ACP prediction.
Demonstrated the effectiveness of the developed computational approach.

Conclusions:

The developed SVM-based predictor, utilizing combined features, offers a reliable method for identifying anticancer peptides.
This approach can aid in the discovery and development of novel anticancer peptide therapeutics.
Highlights the importance of feature selection in computational drug discovery.