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Apoptosis

Apoptosis

Apoptosis is a combination of two Greek words, 'apo' and 'ptosis,' meaning separation and falling off, respectively. Hippocrates used this word to describe gangrene, which was caused due to bandaging of fractured bones. Apoptosis was distinguished from necrosis in 1970 when John Kerr reported observations of morphological changes occurring during apoptosis. During one experiment, he observed that the disruption of blood supply to the liver tissue resulted in a size...

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Related Experiment Video

Updated: Mar 13, 2026

A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

Published on: December 1, 2018

Detrended cross-correlation coefficient: Application to predict apoptosis protein subcellular localization.

Yunyun Liang¹, Sanyang Liu¹, Shengli Zhang¹

¹School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China.

Mathematical Biosciences

|November 7, 2016

Summary

This summary is machine-generated.

This study presents a novel method for predicting apoptosis protein subcellular localization using position-specific scoring matrices (PSSMs) and detrended cross-correlation. The approach offers a reliable tool for understanding programmed cell death mechanisms.

Keywords:

Detrended cross-correlation coefficient Position-specific scoring matrix Subcellular localization Support vector machine

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Area of Science:

* Molecular Biology
* Biochemistry
* Bioinformatics

Background:

* Apoptosis (programmed cell death) is crucial for organism development and homeostasis.
* Understanding the subcellular localization of apoptosis proteins aids in elucidating apoptosis mechanisms.
* Predicting apoptosis protein localization from primary sequences remains a challenge.

Purpose of the Study:

* To develop a novel computational method for predicting the subcellular localization of apoptosis proteins.
* To utilize position-specific scoring matrices (PSSMs) and detrended cross-correlation (DCCA) for improved prediction accuracy.

Main Methods:

* A PSSM-based approach incorporating the DCCA coefficient of non-overlapping windows was developed.
* A 190-dimensional feature vector was constructed using two established datasets (CL317 and ZD98).
* Support Vector Machine (SVM) was employed as the classification algorithm.

Main Results:

* The proposed method demonstrated reliable performance in predicting apoptosis protein subcellular localization.
* Rigorous jackknife cross-validation tests were conducted on the CL317 and ZD98 datasets.
* The novel approach offers a significant advancement over existing sequence-based prediction methods.

Conclusions:

* The developed PSSM-based method provides a novel and dependable tool for predicting apoptosis protein subcellular localization.
* This advancement contributes to a deeper understanding of apoptosis mechanisms through accurate protein localization prediction.