Jing Chen
Northwest Normal University
5 Papers
8 Citations
Jing Chen is an academic researcher from Northwest Normal University. The author has contributed to research in topics: Signal & Support vector machine. The author has an hindex of 2, co-authored 5 publications.
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Papers
Novel prediction of interactive mode between antibiotics and their DNA/protein targets based on the antibiotic structure parameters.
Jing Chen,Xiaoquan Lu +1 more
TL;DR: This paper focuses on investigating the structure parameters of antibiotics which have decisive influence on the interactive mode between antibiotics and DNAs and shows that the Best Prediction Set Support Vector Machine Method (BPSSVM) is potent in predicting the interactive modes.
4
Patent
Method for getting frequency of chemical oscillation reaction signal
Xiaoquan Lu,Jing Chen,Hongde Liu +2 more
- 04 Feb 2009
TL;DR: In this article, a frequency acquiring method of chemical oscillating reaction signals is proposed, which adopts the continuous wavelet transformation CWT technology, establishes the wavelet frequency spectrum WFS, and solves the problem that the Fourier analysis can not accurately express the complex signal frequency information.
2
A new approach to the prediction of transmembrane structures
TL;DR: A new approach, maximum spectrum of continuous wavelet transform (MSCWT), is proposed to predict TMHs and the predictions for eight SARS-CoV membrane proteins indicate that MSCWT has the same capacity with software TMpred.
A reliable approach of frequency analysis and its application for oscillating chemical systems
TL;DR: In this paper, a named oscillation frequency spectrum (OFS) was developed for the frequency analysis of oscillating signal, which can be used to analyze the frequency of periodic signal with accuracy.
1
Studies on the interaction between antibiotics and DNA.
TL;DR: Based on 24 parameters that can influence the interaction of selected antibiotic compound (antibiotics) and DNA, multiple linear regression (MLR) and artificial neutral network (ANN) allowed us to propose three models which can predict binding constant and binding mode.