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Related Concept Videos

Deconvolution01:20

Deconvolution

262
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
262
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

432
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
432
Convolution Properties II01:17

Convolution Properties II

292
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
292
Convolution Properties I01:20

Convolution Properties I

243
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
243

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Deep Neural Networks for Image-Based Dietary Assessment
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Deep learning based deconvolution methods: A systematic review.

Alba Lomas Redondo1, Jose M Sánchez Velázquez1, Álvaro J García Tejedor1

  • 1CEIEC, Universidad Francisco de Vitoria (UFV), Pozuelo de Alarcón, 28223, Madrid, Spain.

Computational and Structural Biotechnology Journal
|June 30, 2025
PubMed
Summary

Artificial Intelligence (AI) and Deep Learning (DL) are advancing cellular deconvolution for RNA sequencing analysis. High-quality reference profiles are crucial for accurate cell composition determination in complex samples.

Keywords:
Artificial intelligenceCellular deconvolutionComputational biologyDeep learningNeural networkRNA–seqTranscriptomics datascRNA–seq

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Cellular deconvolution is vital for analyzing complex biological samples.
  • RNA sequencing provides rich transcriptomic data.
  • Artificial Intelligence (AI) and Deep Learning (DL) offer powerful tools for biological data analysis.

Purpose of the Study:

  • To systematically review AI and DL applications in cellular deconvolution tools.
  • To focus on the analysis of transcriptomics data from RNA sequencing.
  • To highlight the importance of reference profiles for deconvolution accuracy.

Main Methods:

  • Systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines.
  • Examination of AI and DL methodologies for cellular deconvolution.
  • Analysis of datasets and findings related to DL-driven deconvolution.

Main Results:

  • Identified key research gaps in current deconvolution methodologies.
  • Emphasized the need for standardized approaches and improved model interpretability.
  • Highlighted the critical role of high-quality reference profiles for accurate cellular composition analysis.

Conclusions:

  • AI and DL are significantly impacting cellular deconvolution tool development.
  • Future research should focus on standardization and interpretability.
  • Collaboration between computational and biological sciences is essential for progress.