相关实验视频

Updated: Sep 14, 2025

Automated Detection and Analysis of Exocytosis
13:28

Automated Detection and Analysis of Exocytosis

Published on: September 11, 2021

3.6K

一个可解释的单维卷积神经网络与修改的加博波波变换用于对外子的识别

K Jayasree1, Malaya Kumar Hota1

  • 1Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Computer methods in biomechanics and biomedical engineering
|July 21, 2025
PubMed
概括

这项研究引入了一种新的一维卷积神经网络 (1D-CNN),用于在DNA序列中准确识别外子. 建议的模型,不包括聚合层,增强了特征的保存,并使用特定的数字信号处理 (DSP) 方法实现了卓越的性能.

关键词:
卷积神经网络 (CNN) 是一个神经网络.它们是前代子.修改的加博波波变换 (MGWT)多层感知子 (MLP) 是一种多层感知子.短时间离散里叶变换 (ST-DFT)三个基本周期性属性 (TBP)

更多相关视频

Co-culture of Glioblastoma Stem-like Cells on Patterned Neurons to Study Migration and Cellular Interactions10:08

Co-culture of Glioblastoma Stem-like Cells on Patterned Neurons to Study Migration and Cellular Interactions

Published on: February 24, 2021

6.2K

相关概念视频

Deconvolution01:20

Deconvolution

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...
260
Convolution Properties II01:17

Convolution Properties II

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...
288
Convolution Properties I01:20

Convolution Properties I

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:
240
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
519
Exon Recombination02:32

Exon Recombination

The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
Exon shuffling follows “splice frame rules.” Each exon...
3.7K
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

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...
416