Gene marker detection method based on terahertz signal

Abstract

The invention relates to a gene marker detection method based on a terahertz signal, which comprises the following steps: irradiating a to-be-detected gene sample by using a terahertz pulse, and acquiring the terahertz signal generated after the gene sample is reflected; performing denoising processing on the terahertz signal to obtain a denoised terahertz signal; establishing a database according to a signal of a known gene marker in a terahertz wave band; comparing the current gene sample with the terahertz signal of the gene marker in the database to obtain a similar value; and when the similarity value exceeds a preset threshold value, determining that the current gene sample has a corresponding gene marker. According to the method, the specific terahertz signal database of the gene markers is established, and the current gene sample is compared with the terahertz signals of the gene markers in the database, so that the requirements of clinical and laboratory on rapid detection of the gene markers can be met, and the detection accuracy and reliability are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of gene marker detection, and in particular to a gene marker detection method based on terahertz signals. Background Art

[0002] As important carriers of genetic information, genetic markers play a crucial role in disease diagnosis, precision medicine, drug development, and biomedical research. Traditional genetic marker detection methods primarily include PCR (polymerase chain reaction), gene chips, fluorescence in situ hybridization (FISH), and sequencing technologies. While these methods offer high sensitivity and specificity, they suffer from common drawbacks such as complex procedures, cumbersome testing processes, high time costs, and expensive equipment, limiting their widespread adoption and application for real-time dynamic monitoring.

[0003] In recent years, electromagnetic waves in the terahertz band have become a key research area in biomedical testing due to their non-ionizing properties, strong penetrating power, and sensitivity to the unique vibrational modes of biomolecules. Terahertz technology can non-destructively and label-freely probe the molecular conformations and interactions of biomacromolecules, offering new possibilities for the detection of genetic markers.

[0004] Existing terahertz-based detection technologies are mostly used for imaging and structural analysis of proteins, cells and tissues, but the specific detection of gene markers still has problems such as insufficient sensitivity, low signal-to-noise ratio and limited recognition accuracy. Summary of the Invention

[0005] To solve the above problems, an embodiment of the present invention aims to provide a gene marker detection method based on terahertz signals.

[0006] A method for detecting gene markers based on terahertz signals, comprising:

[0007] Step 1: Use a terahertz pulse to irradiate the gene sample to be detected, and obtain the terahertz signal generated after the gene sample is reflected;

[0008] Step 2: performing denoising processing on the terahertz signal to obtain a denoised terahertz signal;

[0009] Step 3: Establish a database based on the signals of known gene markers in the terahertz band;

[0010] Step 4: Compare the terahertz signals of the current gene sample with those of the gene markers in the database to obtain a similarity value;

[0011] Step 5: When the similarity value exceeds the preset threshold, the current gene sample has the corresponding gene marker;

[0012] Step 6: When the similarity value does not exceed the preset threshold, the current gene sample does not have a corresponding gene marker.

[0013] Preferably, the step 2: performing denoising processing on the terahertz signal to obtain a denoised terahertz signal includes:

[0014] Step 2.1: Perform wavelet transform on the terahertz signal to obtain the wavelet coefficients of each layer;

[0015] Step 2.2: Determine the denoising threshold according to the number of decomposition layers of the terahertz signal;

[0016] Step 2.3: When the wavelet coefficient is greater than or equal to the denoising threshold, the wavelet coefficient is processed using the first denoising function to obtain a denoised wavelet coefficient;

[0017] Step 2.4: When the wavelet coefficient is less than the denoising threshold, the wavelet coefficient is processed using a second denoising function to obtain a denoised wavelet coefficient;

[0018] Step 2.5: Reconstruct the denoised wavelet coefficients to obtain the denoised terahertz signal.

[0019] Preferably, the step 2.2: determining the denoising threshold according to the number of decomposition layers of the terahertz signal includes:

[0020] The median of the wavelet coefficients at each decomposition level is extracted, and the denoising threshold at each decomposition level is determined based on the median of the wavelet coefficients; wherein the calculation formula of the denoising threshold is:

[0021]

[0022] Among them, λ j represents the denoising threshold at the jth decomposition level, represents the median of the wavelet coefficients, and N represents the length of the signal.

[0023] Preferably, in step 2.3, the first denoising function is:

[0024]

[0025] in, represents the wavelet coefficient after denoising, W j,k represents the kth original wavelet coefficient at the jth decomposition level, λ j It represents the denoising threshold at the jth decomposition level, a represents the preset parameter, and sign() represents the sign function.

[0026] Preferably, in step 2.4, the second denoising function is:

[0027]

[0028] in, represents the wavelet coefficient after denoising, W j,k represents the kth original wavelet coefficient at the jth decomposition level, λ j represents the denoising threshold at the jth decomposition level, and a represents the preset parameter.

[0029] Preferably, the step 2.5: reconstructing the denoised wavelet coefficients to obtain the denoised terahertz signal includes:

[0030] Evaluating the initially reconstructed terahertz signal to obtain a signal quality evaluation value;

[0031] The signal quality evaluation value calculation formula is:

[0032]

[0033] Where s(n) represents the original terahertz signal, represents the initially reconstructed terahertz signal, n represents the sampling point number, N represents the sampling length, SNR represents the first signal quality evaluation value, and RMSE represents the second signal quality evaluation value;

[0034] Determine whether the signal quality evaluation value is within a preset range. When the signal quality evaluation value is not within the preset range, change the preset parameters until the signal quality evaluation value of the initially reconstructed terahertz signal falls within the preset range, and use it as the denoised terahertz signal.

[0035] Preferably, the step 4: comparing the terahertz signals of the current gene sample with those of the gene markers in the database to obtain a similarity value includes:

[0036] Step 4.1: Construct an error function based on the difference between the terahertz signal of the current gene sample and the gene markers in the database; wherein the error function is:

[0037]

[0038] Among them, E represents the error function, S(f i ) represents the amplitude of the terahertz signal of the current gene sample after denoising at the i-th point, represents the amplitude of the terahertz signal of the gene marker in the database at the i-th point, and a represents the similarity value;

[0039] Step 4.2: Derivative the error function to find the point where the derivative of the error function is zero to obtain the similarity value.

[0040] The present invention also provides a gene marker detection system based on terahertz signals, comprising:

[0041] A signal acquisition module is used to irradiate the gene sample to be detected with a terahertz pulse and obtain the terahertz signal generated after the gene sample is reflected;

[0042] A denoising module, configured to perform denoising processing on the terahertz signal to obtain a denoised terahertz signal;

[0043] A database construction module is used to establish a database based on the signals of known gene markers in the terahertz band;

[0044] A similarity value calculation module is used to compare the terahertz signals of the current gene sample with those of the gene markers in the database to obtain a similarity value;

[0045] The first judgment module is used to determine the presence of corresponding gene markers in the current gene sample when the similarity value exceeds a preset threshold;

[0046] The second judgment module is used to determine that the current gene sample does not have a corresponding gene marker when the similarity value does not exceed a preset threshold.

[0047] The present invention also provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and wherein the computer program, when executed by the processor, implements the steps of the above-mentioned method for detecting genetic markers based on terahertz signals.

[0048] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps in the above-mentioned method for detecting genetic markers based on terahertz signals are implemented.

[0049] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0050] The present invention relates to a method for detecting gene markers based on terahertz signals. Compared with the existing technology, the present invention establishes a terahertz signal database unique to gene markers and compares the terahertz signals of the current gene sample with those of the gene markers in the database. This method can not only meet the clinical and laboratory needs for rapid detection of gene markers, but also greatly improve the accuracy and reliability of detection.

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 A flow chart of a gene marker detection method based on terahertz signals provided by the present invention;

[0054] Figure 2 This is a schematic diagram of a gene marker detection system based on terahertz signals provided by the present invention. DETAILED DESCRIPTION

[0055] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0056] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0057] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediary; or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0058] See also Figure 1 , a gene marker detection method based on terahertz signals, comprising:

[0059] Step 1: Use a terahertz pulse to irradiate the gene sample to be detected, and obtain the terahertz signal generated after the gene sample is reflected;

[0060] Step 2: performing denoising processing on the terahertz signal to obtain a denoised terahertz signal;

[0061] Furthermore, step 2: performing denoising processing on the terahertz signal to obtain a denoised terahertz signal, comprising:

[0062] Step 2.1: Perform wavelet transform on the terahertz signal to obtain the wavelet coefficients of each layer;

[0063] Step 2.2: Determine the denoising threshold according to the number of decomposition layers of the terahertz signal;

[0064] The selection of the threshold has a significant impact on the effectiveness of signal noise reduction. If the threshold is too small, the signal noise reduction will be incomplete and the noise signal will be retained. If the threshold is too large, the effective signal will be ignored and the signal will be distorted. The signal distribution characteristics show that the noise signal is mainly concentrated in the wavelet coefficients of the first layer. As the number of wavelet decomposition layers increases, the noise signal will rapidly decrease. However, traditional threshold functions do not take this into account. Among them, this paper proposes a new denoising threshold based on the trend of noise signal changes at different scales.

[0065] In step 2.2, the median of the wavelet coefficients at each decomposition level is extracted, and the denoising threshold at each decomposition level is determined based on the median of the wavelet coefficients; wherein the calculation formula of the denoising threshold is:

[0066]

[0067] Among them, λ j represents the denoising threshold at the jth decomposition level, represents the median of the wavelet coefficients, and N represents the length of the signal.

[0068] Step 2.3: When the wavelet coefficient is greater than or equal to the denoising threshold, the wavelet coefficient is processed using the first denoising function to obtain a denoised wavelet coefficient;

[0069] In step 2.3, the first denoising function is:

[0070]

[0071] in, represents the wavelet coefficient after denoising, W j,k represents the kth original wavelet coefficient at the jth decomposition level, λ j It represents the denoising threshold at the jth decomposition level, a represents the preset parameter, and sign() represents the sign function.

[0072] Step 2.4: When the wavelet coefficient is less than the denoising threshold, the wavelet coefficient is processed using a second denoising function to obtain a denoised wavelet coefficient;

[0073] In step 2.4, the second denoising function is:

[0074]

[0075] in, represents the wavelet coefficient after denoising, W j,k represents the kth original wavelet coefficient at the jth decomposition level, λ j represents the denoising threshold at the jth decomposition level, and a represents the preset parameter.

[0076] Step 2.5: Reconstruct the denoised wavelet coefficients to obtain the denoised terahertz signal.

[0077] In step 2.5, include:

[0078] Evaluating the initially reconstructed terahertz signal to obtain a signal quality evaluation value;

[0079] The signal quality evaluation value calculation formula is:

[0080]

[0081] Where s(n) represents the original terahertz signal, represents the initially reconstructed terahertz signal, n represents the sampling point number, N represents the sampling length, SNR represents the first signal quality evaluation value, and RMSE represents the second signal quality evaluation value;

[0082] Determine whether the signal quality evaluation value is within a preset range. When the signal quality evaluation value is not within the preset range, change the preset parameters until the signal quality evaluation value of the initially reconstructed terahertz signal falls within the preset range, and use it as the denoised terahertz signal.

[0083] In practical applications, although the hard threshold function can better reduce the noise of the signal, the hard threshold is discontinuous at the threshold, which will cause the pseudo-Gibbs effect when processing noise, and has certain limitations; while the soft threshold function improves the discontinuity of the hard threshold function at the threshold, it will cause the loss of high-frequency signals when processing noise, resulting in signal distortion. In the present invention, when a→0, the improved denoising function can be converted into a hard threshold function, and when the adjustable coefficient a tends to 1, the improved denoising function can be close to the soft threshold function. Therefore, when the improved threshold function takes different values ​​of a, the function can be converted between soft and hard threshold functions, and its continuity and smoothness are better, thereby greatly improving the denoising effect.

[0084] Step 3: Establish a database based on the signals of known gene markers in the terahertz band;

[0085] Step 4: Compare the terahertz signals of the current gene sample with those of the gene markers in the database to obtain a similarity value;

[0086] Furthermore, step 4: comparing the terahertz signals of the current gene sample with those of the gene markers in the database to obtain similarity values, including:

[0087] Step 4.1: Construct an error function based on the difference between the terahertz signal of the current gene sample and the gene markers in the database; wherein the error function is:

[0088]

[0089] Among them, E represents the error function, S(f i ) represents the amplitude of the terahertz signal of the current gene sample after denoising at the i-th point, represents the amplitude of the terahertz signal of the gene marker in the database at the i-th point, and a represents the similarity value;

[0090] Step 4.2: Derivative the error function to find the point where the derivative of the error function is zero to obtain the similarity value, and the similarity value solved is:

[0091]

[0092] Step 5: When the similarity value exceeds the preset threshold, the current gene sample has the corresponding gene marker;

[0093] Step 6: When the similarity value does not exceed the preset threshold, the current gene sample does not have a corresponding gene marker.

[0094] The present invention establishes a terahertz signal database unique to gene markers and compares the terahertz signals of the current gene sample with those of the gene markers in the database. This not only meets the clinical and laboratory needs for rapid detection of gene markers, but also greatly improves the accuracy and reliability of detection.

[0095] See also Figure 2 The present invention also provides a gene marker detection system based on terahertz signals, comprising:

[0096] A signal acquisition module is used to irradiate the gene sample to be detected with a terahertz pulse and obtain the terahertz signal generated after the gene sample is reflected;

[0097] A denoising module, configured to perform denoising processing on the terahertz signal to obtain a denoised terahertz signal;

[0098] A database construction module is used to establish a database based on the signals of known gene markers in the terahertz band;

[0099] A similarity value calculation module is used to compare the terahertz signals of the current gene sample with those of the gene markers in the database to obtain a similarity value;

[0100] The first judgment module is used to determine the presence of corresponding gene markers in the current gene sample when the similarity value exceeds a preset threshold;

[0101] The second judgment module is used to determine that the current gene sample does not have a corresponding gene marker when the similarity value does not exceed a preset threshold.

[0102] Compared with the prior art, the beneficial effects of the gene marker detection system based on terahertz signals provided by the present invention are the same as the beneficial effects of the gene marker detection method based on terahertz signals described in the above technical solution, and are not described in detail here.

[0103] The present invention also provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus. The invention is characterized in that when the computer program is executed by the processor, the steps of the above-mentioned method for detecting genetic markers based on terahertz signals are implemented. Compared with the prior art, the beneficial effects of the electronic device provided by the present invention are the same as the beneficial effects of the method for detecting genetic markers based on terahertz signals described in the above-mentioned technical solution, and are not elaborated herein.

[0104] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps in the above-mentioned method for detecting genetic markers based on terahertz signals are implemented. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present invention are the same as the beneficial effects of the method for detecting genetic markers based on terahertz signals described in the above technical solution, and are not elaborated here.

[0105] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technical solution that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for detecting gene markers based on terahertz signals, characterized in that: include: Step 1: Use a terahertz pulse to irradiate the gene sample to be detected, and obtain the terahertz signal generated after the gene sample is reflected; Step 2: performing denoising processing on the terahertz signal to obtain a denoised terahertz signal; Step 3: Establish a database based on the signals of known gene markers in the terahertz band; Step 4: Compare the terahertz signals of the current gene sample with those of the gene markers in the database to obtain a similarity value; Step 5: When the similarity value exceeds the preset threshold, the current gene sample has the corresponding gene marker; Step 6: When the similarity value does not exceed the preset threshold, the current gene sample does not have a corresponding gene marker.

2. The method for detecting gene markers based on terahertz signals according to claim 1, wherein: The step 2: performing denoising processing on the terahertz signal to obtain a denoised terahertz signal, comprising: Step 2.1: Perform wavelet transform on the terahertz signal to obtain the wavelet coefficients of each layer; Step 2.2: Determine the denoising threshold according to the number of decomposition layers of the terahertz signal; Step 2.3: When the wavelet coefficient is greater than or equal to the denoising threshold, the wavelet coefficient is processed using the first denoising function to obtain a denoised wavelet coefficient; Step 2.4: When the wavelet coefficient is less than the denoising threshold, the wavelet coefficient is processed using a second denoising function to obtain a denoised wavelet coefficient; Step 2.5: Reconstruct the denoised wavelet coefficients to obtain the denoised terahertz signal.

3. The method for detecting gene markers based on terahertz signals according to claim 2, characterized in that: The step 2.2: determining the denoising threshold according to the number of decomposition layers of the terahertz signal, includes: The median of the wavelet coefficients at each decomposition level is extracted, and the denoising threshold at each decomposition level is determined based on the median of the wavelet coefficients; wherein the calculation formula of the denoising threshold is: Among them, λ j represents the denoising threshold at the jth decomposition level, represents the median of the wavelet coefficients, and N represents the length of the signal.

4. The method for detecting gene markers based on terahertz signals according to claim 3, characterized in that: In step 2.3, the first denoising function is: in, represents the wavelet coefficient after denoising, W j,k represents the kth original wavelet coefficient at the jth decomposition level, λ j It represents the denoising threshold at the jth decomposition level, a represents the preset parameter, and sign() represents the sign function.

5. The method for detecting gene markers based on terahertz signals according to claim 4, characterized in that: In step 2.4, the second denoising function is: in, represents the wavelet coefficient after denoising, W j,k represents the kth original wavelet coefficient at the jth decomposition level, λ j represents the denoising threshold at the jth decomposition level, and a represents the preset parameter.

6. The method for detecting gene markers based on terahertz signals according to claim 5, characterized in that: The step 2.5: reconstructing the denoised wavelet coefficients to obtain the denoised terahertz signal includes: Evaluating the initially reconstructed terahertz signal to obtain a signal quality evaluation value; The signal quality evaluation value calculation formula is: Where s(n) represents the original terahertz signal, represents the initially reconstructed terahertz signal, n represents the sampling point number, N represents the sampling length, SNR represents the first signal quality evaluation value, and RMSE represents the second signal quality evaluation value; Determine whether the signal quality evaluation value is within a preset range. When the signal quality evaluation value is not within the preset range, change the preset parameters until the signal quality evaluation value of the initially reconstructed terahertz signal falls within the preset range, and use it as the denoised terahertz signal.

7. The method for detecting gene markers based on terahertz signals according to claim 6, characterized in that: Step 4: comparing the terahertz signals of the current gene sample with those of the gene markers in the database to obtain a similarity value, including: Step 4.1: Construct an error function based on the difference between the terahertz signal of the current gene sample and the gene markers in the database; wherein the error function is: Among them, E represents the error function, S(f i ) represents the amplitude of the terahertz signal of the current gene sample after denoising at the i-th point, represents the amplitude of the terahertz signal of the gene marker in the database at the i-th point, and a represents the similarity value; Step 4.2: Derivative the error function to find the point where the derivative of the error function is zero to obtain the similarity value.

8. A gene marker detection system based on terahertz signals, characterized in that: include: A signal acquisition module is used to irradiate the gene sample to be detected with a terahertz pulse and obtain the terahertz signal generated after the gene sample is reflected; A denoising module, configured to perform denoising processing on the terahertz signal to obtain a denoised terahertz signal; A database construction module is used to establish a database based on the signals of known gene markers in the terahertz band; A similarity value calculation module is used to compare the terahertz signals of the current gene sample with those of the gene markers in the database to obtain a similarity value; The first judgment module is used to determine the presence of corresponding gene markers in the current gene sample when the similarity value exceeds a preset threshold; The second judgment module is used to determine that the current gene sample does not have a corresponding gene marker when the similarity value does not exceed a preset threshold.

9. An electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, wherein: When the computer program is executed by the processor, the steps of the method for detecting gene markers based on terahertz signals according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for detecting gene markers based on terahertz signals according to any one of claims 1 to 7 are implemented.

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