ArticleBMC bioinformatics2019
A general-purpose signal processing algorithm for biological profiles using only first-order derivative information.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Sensory responses of olfactory and vomeronasal neurons in does are influenced by buck odors and their reproductive status.Chemical senses · 2025Article
- Early detection of dark-affected plant mechanical responses using enhanced electrical signals.Plant methods · 2024Article
- Evaluating the Modulation of the Acoustic Startle Reflex in Children and AdolescentsFrontiers in neuroscience · 2022Article
- Review of Laser Raman Spectroscopy for Surgical Breast Cancer Detection: Stochastic Backpropagation Neural Networks.Sensors (Basel, Switzerland) · 2020Review
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2 authors.
Funding
Abstract
backgroundAutomatic signal-feature extraction algorithms are crucial for profile processing in bioinformatics. Both baseline drift and noise seriously affect the position and peak area of signals. An efficient algorithm named the derivative passing accumulation (DPA) method for simultaneous baseline correction and signal extraction is presented in this article. It is an efficient method using only the first-order derivatives which are obtained through taking the simple differences.
resultsWe developed a new signal feature extracting procedure. The vector representing the discrete first-order derivative was divided into negative and positive parts and then accumulated to build a signal descriptor. The signals and background fluctuations are easily separated according to this descriptor via thresholding. In addition, the signal peaks are simultaneously located by checking the corresponding intervals in the descriptor. Therefore, the eternal issues of parsing the 1-dimensional output of detectors in biological instruments are solved together. Thereby, the baseline is corrected, and the signal peaks are extracted.
conclusionsWe have introduced a new method for signal peak picking, where baseline computation and peak identification are performed jointly. The testing results of both authentic and artificially synthesized data illustrate that the new method is powerful, and it could be a better choice for practical processing.
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