WO2022068186A1 - 质谱可视化方法、终端设备和计算机可读存储介质 - Google Patents

质谱可视化方法、终端设备和计算机可读存储介质 Download PDF

Info

Publication number
WO2022068186A1
WO2022068186A1 PCT/CN2021/089421 CN2021089421W WO2022068186A1 WO 2022068186 A1 WO2022068186 A1 WO 2022068186A1 CN 2021089421 W CN2021089421 W CN 2021089421W WO 2022068186 A1 WO2022068186 A1 WO 2022068186A1
Authority
WO
WIPO (PCT)
Prior art keywords
information
data
browser
mass spectrometry
association relationship
Prior art date
Application number
PCT/CN2021/089421
Other languages
English (en)
French (fr)
Inventor
王鹏飞
张尚
赵重阳
赵兴东
孙建
关灿
Original Assignee
摩赛恩科技(苏州)有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 摩赛恩科技(苏州)有限公司 filed Critical 摩赛恩科技(苏州)有限公司
Publication of WO2022068186A1 publication Critical patent/WO2022068186A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/904—Browsing; Visualisation therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/20—Drawing from basic elements, e.g. lines or circles
    • G06T11/206—Drawing of charts or graphs

Definitions

  • the present application relates to a mass spectrometry visualization method, a terminal device and a computer-readable storage medium, and belongs to the technical field of data processing.
  • a mass spectrometer also known as a mass spectrometer, is an instrument used to separate and detect different isotopes. That is, according to the principle that charged particles can be deflected in the electromagnetic field, it is a kind of instrument that separates and detects the composition of matter according to the mass difference of matter atoms, molecules or molecular fragments.
  • the purpose of this application is to provide a mass spectrometry visualization method, a terminal device and a computer-readable storage medium to solve the problems raised in the above background art.
  • the client or browser requests bioinformatics data
  • the back-end server sends mass spectrometry information data to the browser
  • the browser uses the bioinformatics data and the mass spectrometry information data to create a two-dimensional or three-dimensional visual information map
  • the back-end server establishes an association relationship id;
  • the browser obtains the association relationship id according to the user operation request, and searches the data information of the corresponding visual information graph through the association relationship id;
  • the browser searches for the data information returned by the back-end server according to the association relationship id, and displays the visual information graph corresponding to the association relationship id in the mass spectrum identification interface through the visualization component.
  • the browser establishes a two-dimensional or three-dimensional visualization information graph by using the biometric data and the mass spectrometry information data, specifically:
  • the first process is any one or more of machine learning, deep learning and statistical processing ;
  • the browser converts the m/z information and rt information in the feature information into abscissa and ordinate axes to form a two-dimensional visual information graph; or, the browser converts the m/z information in the feature information into , rt information is converted into the abscissa and ordinate axes, and the isotopic peak, intensity value, matching times, and compound information extracted from the peak are converted into z-coordinates to form a three-dimensional visual information map, and the data index is generated through the association relationship id.
  • the associating the biometric data and the statistical information is specifically:
  • the association between the health information data and the statistical information includes one-to-many, many-to-one and one-to-one data associations.
  • the mass spectrometry visualization method according to the second embodiment of the present invention adds a technical solution for the collaborative interaction of an algorithm server, a back-end server, and a browser, which is used for the interaction of statistical information and visualization results ,include:
  • the client or browser requests bioinformatics data
  • the back-end server sends mass spectrometry information data to the browser
  • the browser uses the bioinformatics data and the mass spectrometry information data to create a two-dimensional or three-dimensional visual information map
  • the back-end server establishes an association relationship id;
  • the browser acquires the mass spectrometry information data, and the mass spectrometry information data includes statistical information generated by an algorithm, raw information data, and user information acquired by a back-end server, and the browser sends the corresponding statistical information according to a user operation request. parameter information to the backend server;
  • the back-end server sends the parameter information to the algorithm server, and the algorithm server performs second processing on the parameter information to obtain result data and sends it to the back-end server; wherein the second processing is statistics, Any one or more of filtering, computing, machine learning, and deep learning algorithms;
  • the back-end server sends the result data to the browser, and after the browser obtains the result data, searches for the corresponding visual information graph according to the association relationship id, and updates the visual information graph, and updates the visual information graph.
  • the updated visual infographic is displayed in the mass spectrum identification interface.
  • the algorithm is a peak detection algorithm.
  • the algorithm server performs second processing on the parameter information, specifically:
  • the algorithm server performs the second processing on the parameter information by means of intersection, union, table union or table filtering.
  • the mass spectrometry visualization method according to the third embodiment of the present invention is an ordinary Internet front-end and back-end server interaction mode in addition to the above methods, including:
  • the browser obtains mass spectrometry information data, the mass spectrometry information data includes the biometric data generated by the algorithm, the user authority information and database information obtained by the back-end server, and the browser obtains the interactive information generated after the user operates the database information. sent to the backend server;
  • the back-end server updates the database information according to the interaction information, and sends the updated database information back to the browser;
  • the browser displays the updated database information in the mass spectrum identification interface.
  • the algorithm is any one of peak detection, peak matching, and peak alignment algorithms.
  • the database information includes: m/z information, rt information, isotopic peak, intensity value, matching times, compound information, secondary spectrum information, corrected retention time information, label information, experimental method information, standard at least one of product verification information and primary spectrum information.
  • the present invention also discloses a terminal device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the above method when the processor executes the computer program .
  • the present invention also discloses a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above method are implemented.
  • the mass spectrometry visualization method of the present invention has the following beneficial effects:
  • the invention digitizes the mass spectral information data, so that it can be further visualized.
  • the traditional data processing software does not perform visual interaction, but only generates a picture display by an algorithm, while this application uses browser drawing to find the associated id between data, so that visual interaction can be performed.
  • Fig. 1 is the flow chart of the first embodiment of the application
  • Fig. 2 is the flow chart of the second embodiment of the application.
  • FIG. 3 is a flowchart of a third embodiment of the present application.
  • FIG. 1 The flowchart of the first embodiment of the mass spectrometry visualization method provided by the present invention is shown in FIG. 1 , including:
  • Step 11 The client or the browser requests the biometric data, the back-end server sends the mass spectrometry information data to the browser, the browser uses the biometric data and mass spectrometry information data to create a two-dimensional or three-dimensional visual information map, and the back-end server establishes an association relationship id.
  • the client or browser requests bioinformatics data from the mass spectrometer or local files, and the back-end server sends the mass spectrometry information data to the browser, including one type of bioinformatics data converted into feature information, digital information, picture information, vector information and Matrix information, the other type is to perform the first processing methods such as machine learning, deep learning, and statistical processing on feature information to obtain statistical information, and the third type is associated data information, that is, the two types of data are related to each other. Yes, it can be one-to-many, many-to-one, one-to-one data association.
  • the browser converts the m/z information and rt information in the feature information into the horizontal and vertical axes to form a two-dimensional visual information graph; or, converts the m/z information and rt information in the feature information into the horizontal and vertical axes , convert the isotopic peaks, intensity values, matching times, and compound information extracted from the peaks into z-coordinates to form a three-dimensional visual information map.
  • Step 12 the browser obtains the association relationship id according to the user operation request, and searches for the data information of the corresponding visual information graph through the association relationship id.
  • the user operation request is that the user performs operations such as clicking, box-selection, drilling down, hovering, and double-clicking on the browser by using a device such as a mouse, a keyboard, or a control panel.
  • Step 13 the browser searches the data information returned by the back-end server according to the association relationship id, and displays the visualization information graph corresponding to the association relationship id in the mass spectrometry identification interface through the visualization component.
  • FIG. 2 The flowchart of the second embodiment of the mass spectrometry visualization method provided by the present invention is shown in FIG. 2 , including:
  • Step 21 The client or the browser requests the biometric data, the back-end server sends the mass spectrometry information data to the browser, the browser uses the biometric data and the mass spectrometry information data to create a two-dimensional or three-dimensional visual information map, and the back-end server establishes an association relationship id.
  • Step 22 The browser obtains mass spectrometry information data, which includes statistical information generated by the algorithm, biometric data and user information obtained by the backend server, and the browser sends parameter information corresponding to the statistical information to the backend server according to the user operation request.
  • the browser obtains the mass spectrometry information data, including statistical information and biometric information generated by the algorithm, and user information obtained by the back-end server.
  • the user selects the statistical information and fills in the parameter range on the browser by using the keyboard, mouse and other devices. For example , select “Model” as “T-test”, select “Optional Parameters” as “p”, fill in the parameter range "less than 0.05,” and the browser sends the parameter information corresponding to the statistical information to the backend server.
  • Step 23 The back-end server sends the parameter information to the algorithm server, and the algorithm server performs second processing on the parameter information, and the resulting data is sent to the back-end server; wherein, the second processing is statistics, filtering, computing, machine learning, and deep learning. any one or more of the algorithms;
  • the back-end server sends the above parameter information submitted by the user to the algorithm server, and the algorithm server performs statistical filtering calculations according to the submitted parameters, including but not limited to intersection, union, table union, table filtering, etc. For example, if the calculation p is less than 0.05, the result "654" is obtained. Sent to the backend server.
  • Step 24 The back-end server sends the result data to the browser. After the browser obtains the result data, it searches for the corresponding visual information graph according to the association relationship id, updates the visual information graph, and displays the updated visual information graph on the mass spectrometry identification interface. middle.
  • the back-end server then transmits the algorithm result data "654" to the browser.
  • the browser After the browser obtains the data, it searches for the corresponding visual information graph, updates the visual information graph, and displays it in the mass spectrometry identification interface.
  • the second embodiment is based on the first embodiment, and adds a technical solution for the collaborative interaction of the algorithm server, the backend server, and the browser, which is used for the interaction of statistical information and visualization results.
  • FIG. 3 The flowchart of the third embodiment of the mass spectrometry visualization method provided by the present invention is shown in FIG. 3 , including:
  • Step 31 the browser obtains mass spectrometry information data, the mass spectrometry information data includes the biometric data generated by the algorithm, the user authority information and the database information obtained by the back-end server, and the browser sends the interactive information generated after the user operates the database information to the backend server. end server.
  • the database information includes: m/z information, rt information, isotopic peaks, intensity values, matching times, compound information, secondary spectrum information, corrected retention time information, label information, experimental method information, standard product verification information and At least one of primary spectral information.
  • the browser obtains the mass spectrometry information data, including the biometric information generated by the algorithm, the user permission information and the database information obtained by the back-end server. After the user selects and labels the database information, the interactive information is generated. For example, the user clicks "label”. , the browser sends interactive information, including feature, sample tissue, compound, device id, label information id, and file storage path information to the back-end server; the algorithm is any one of peak detection, peak matching, and peak alignment algorithms.
  • Step 32 The back-end server updates the database information according to the interaction information, and sends the updated database information back to the browser.
  • updating the database information may include: adding information, deleting information, or modifying information, and the like.
  • Step 33 the browser displays the updated database information in the mass spectrum identification interface.
  • the third embodiment is an ordinary Internet front-end and back-end server interaction mode in addition to the above method.
  • the invention digitizes the mass spectral information data, so that it can be further visualized.
  • the traditional data processing software does not perform visual interaction, but only generates a picture display by an algorithm, while this application uses browser drawing to find the associated id between data, so that visual interaction can be performed.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Artificial Intelligence (AREA)
  • Mathematical Physics (AREA)
  • Evolutionary Computation (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Databases & Information Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Other Investigation Or Analysis Of Materials By Electrical Means (AREA)

Abstract

一种质谱可视化方法、终端设备和计算机可读存储介质，涉及数据处理技术领域，所述方法包括：客户端或浏览器请求生信数据，后端服务器将质谱信息数据发送至浏览器，浏览器利用生信数据和质谱信息数据建立二维或三维的可视化信息图，后端服务器建立关联关系id（11）；浏览器根据用户操作请求获取关联关系id，并通过关联关系id查找其对应的可视化信息图的数据信息（12）；浏览器根据关联关系id查找后端服务器返回的数据信息，并通过可视化组件将关联关系id对应的可视化信息图展示在质谱识别界面中（13）。所述方法在使用峰检测算法的基础上，将质谱信息数据数字化，从而可以进一步可视化。

Description

质谱可视化方法、终端设备和计算机可读存储介质 技术领域
本申请涉及一种质谱可视化方法、终端设备和计算机可读存储介质，属于数据处理技术领域。
背景技术
质谱仪又称质谱计，是用来进行分离和检测不同同位素的仪器。即根据带电粒子在电磁场中能够偏转的原理，按物质原子、分子或分子碎片的质量差异进行分离和检测物质组成的一类仪器。
现有技术中，质谱仪生产的质谱数据信息并不能直接进行可视化，导致不容易进行分析，且难以进行交互。
发明内容
本申请的目的在于，提供一种质谱可视化方法、终端设备和计算机可读存储介质，以解决上述背景技术中提出的问题。
本发明第一种实施例的质谱可视化方法，包括：
客户端或浏览器请求生信数据，后端服务器将质谱信息数据发送至所述浏览器，所述浏览器利用所述生信数据和所述质谱信息数据建立二维或三维的可视化信息图，所述后端服务器建立关联关系id；
所述浏览器根据用户操作请求获取所述关联关系id，并通过所述关联关系id查找其对应的可视化信息图的数据信息；
所述浏览器根据所述关联关系id查找后端服务器返回的所述数据信息，并通过可视化组件将所述关联关系id对应的可视化信息图展示在质谱识别界面中。
可选的，所述浏览器利用所述生信数据和所述质谱信息数据建立二维或三维的可视化信息图，具体为：
将所述生信数据转化为特征信息、数字信息、图片信息、向量信息和矩阵信息；
将所述特征信息进行第一处理得到统计信息，并关联所述生信数 据和所述统计信息；其中，所述第一处理为机器学习、深度学习和统计处理中的任意一种或几种；
所述浏览器将所述特征信息中的m/z信息、rt信息转化为横纵坐标轴，形成二维的可视化信息图；或者，所述浏览器将所述特征信息中的m/z信息、rt信息转化为横纵坐标轴，将峰提取的同位素峰、强度值、匹配次数、化合物信息转化为z坐标，形成三维的可视化信息图，并通过所述关联关系id生成数据索引。
可选的，所述关联所述生信数据和所述统计信息，具体为：
所述生信数据和所述统计信息之间的关联包括一对多、多对一和一对一的数据关联。
本发明第二种实施例的质谱可视化方法，在第一种实施例的基础上，增加了一种算法服务器、后端服务器、浏览器协同交互的技术方案，用于统计信息、可视化结果的交互，包括：
客户端或浏览器请求生信数据，后端服务器将质谱信息数据发送至所述浏览器，所述浏览器利用所述生信数据和所述质谱信息数据建立二维或三维的可视化信息图，所述后端服务器建立关联关系id；
所述浏览器获取所述质谱信息数据，所述质谱信息数据包括算法生成的统计信息、生信数据和后端服务器获取的用户信息，所述浏览器根据用户操作请求发送所述统计信息对应的参数信息至所述后端服务器；
所述后端服务器将所述参数信息发送至算法服务器，所述算法服务器对所述参数信息进行第二处理，得到结果数据发送至所述后端服务器；其中，所述第二处理为统计、过滤、计算、机器学习、深度学习算法中的任意一种或几种；
所述后端服务器将所述结果数据发送至所述浏览器，所述浏览器获取所述结果数据后，根据所述关联关系id查找对应的可视化信息图，并更新所述可视化信息图，将更新后的可视化信息图展示在质谱识别界面中。
可选的，所述算法为峰检测算法。
可选的，所述算法服务器对所述参数信息进行第二处理，具体为：
所述算法服务器采用交、并、表联合或表过滤的方式对所述参数信息进行第二处理。
本发明第三种实施例的质谱可视化方法，是除上述方法外的普通的互联网前后端服务器交互方式，包括：
浏览器获取质谱信息数据，所述质谱信息数据包括算法生成的生信数据、后端服务器获取的用户权限信息和数据库信息，所述浏览器将用户对所述数据库信息进行操作后产生的交互信息发送至所述后端服务器；
所述后端服务器根据所述交互信息更新所述数据库信息，并将更新后的数据库信息发送回所述浏览器；
所述浏览器将更新后的数据库信息展示在质谱识别界面中。
可选的，所述算法为峰检测、峰匹配、峰对齐算法中的任意一种。
可选的，所述数据库信息包括：m/z信息、rt信息、同位素峰、强度值、匹配次数、化合物信息、二级谱信息、校正后的保留时间信息、标签信息、实验方法信息、标准品验证信息和一级谱信息中的至少一种。
本发明还公开了一种终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述方法的步骤。
本发明还公开了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现上述方法的步骤。
本发明的质谱可视化方法相较于现有技术，具有如下有益效果：
本发明在使用峰检测算法的基础上，将质谱信息数据数字化，从而可以进一步可视化。
另外，传统的数据处理软件并没有进行可视化交互，仅仅是算法生成图片展示，而本申请采用浏览器绘图，把数据之间关联id找到，从而可以进行可视化交互。
附图说明
图1为本申请第一种实施例的流程图；
图2为本申请第二种实施例的流程图；
图3为本申请第三种实施例的流程图。
具体实施方式
下面结合实施例详述本申请，但本申请并不局限于这些实施例。
本发明提供的质谱可视化方法的第一实施例的流程图见图1，包括：
步骤11、客户端或浏览器请求生信数据，后端服务器将质谱信息数据发送至浏览器，浏览器利用生信数据和质谱信息数据建立二维或三维的可视化信息图，后端服务器建立关联关系id。
客户端或浏览器从质谱仪或本地文件中请求生信数据，后端服务器把质谱信息数据发送至浏览器，包含一类是生信数据转化为特征信息、数字信息、图片信息、向量信息和矩阵信息，另一类是将特征信息进行机器学习、深度学习、统计处理等第一处理方式得到统计信息，第三类是关联数据信息，即生信数据和统计信息这两类数据是相互关联的，可以是一对多，多对一，一对一的数据关联。然后浏览器将特征信息中的m/z信息、rt信息转化为横纵坐标轴，形成二维的可视化信息图；或者，将特征信息中的m/z信息、rt信息转化为横纵坐标轴，把峰提取的同位素峰、强度值、匹配次数、化合物信息转化为z坐标，形成三维的可视化信息图。
步骤12、浏览器根据用户操作请求获取关联关系id，并通过关联关系id查找其对应的可视化信息图的数据信息。
其中，用户操作请求为用户利用鼠标、键盘或控制面板等设备在浏览器上进行点击、框选、下钻、悬停、双击等操作。用户进行点击、框选、下钻等操作，通过关联关系id生成数据索引用来做交互；例如，点击“特征图”中m/z＝620.3959Da、rt＝170.39s的点，浏览器获取该点的id后，通过关联关系id找到对应“T-test”图、“ROC”图、“Box Plot”图、“HeatMap”图所在表的id信息。
步骤13、浏览器根据关联关系id查找后端服务器返回的数据信 息，并通过可视化组件将关联关系id对应的可视化信息图展示在质谱识别界面中。
通过找到关联关系id，查找后端服务器返回的表数据，进行绘图。例如，在“T-test”图返回p＝0.04，在“ROC”图返回AUC＝0.6422；通过echarts组件进行相应的绘图与展示，例如，绘制“T-test”图、“ROC”图、“Box Plot”图、“HeatMap”图。
本发明提供的质谱可视化方法的第二实施例的流程图见图2，包括：
步骤21、客户端或浏览器请求生信数据，后端服务器将质谱信息数据发送至浏览器，浏览器利用生信数据和质谱信息数据建立二维或三维的可视化信息图，后端服务器建立关联关系id。
步骤22、浏览器获取质谱信息数据，质谱信息数据包括算法生成的统计信息、生信数据和后端服务器获取的用户信息，浏览器根据用户操作请求发送统计信息对应的参数信息至后端服务器。
浏览器获取质谱信息数据，包含算法生成的统计信息和生信信息、后端服务器获取的用户信息，用户利用键盘、鼠标等设备在浏览器上对统计信息进行选择、填写参数范围等操作，例如，选择“模型”为“T-test”，选择“可选参数”为“p”，填写参数范围“小于0.05，”浏览器发送统计信息对应的参数信息给后端服务器。
步骤23、后端服务器将参数信息发送至算法服务器，算法服务器对参数信息进行第二处理，得到结果数据发送至后端服务器；其中，第二处理为统计、过滤、计算、机器学习、深度学习算法中的任意一种或几种；
后端服务器把用户提交的上述参数信息给算法服务器，算法服务器根据提交参数进行统计过滤计算，包含但不限于交、并、表联合、表过滤等，例如计算p小于0.05，得到结果“654”发送给后端服务器。
步骤24、后端服务器将结果数据发送至浏览器，浏览器获取结果数据后，根据关联关系id查找对应的可视化信息图，并更新可视化信息图，将更新后的可视化信息图展示在质谱识别界面中。
后端服务器再把算法结果数据“654”传输给浏览器，浏览器获取数据后，查找对应的可视化信息图，更新该可视化信息图，并展示在质谱识别界面中。
第二种实施例是在第一种实施例的基础上，增加了一种算法服务器、后端服务器、浏览器协同交互的技术方案，用于统计信息、可视化结果的交互。
本发明提供的质谱可视化方法的第三实施例的流程图见图3，包括：
步骤31、浏览器获取质谱信息数据，质谱信息数据包括算法生成的生信数据、后端服务器获取的用户权限信息和数据库信息，浏览器将用户对数据库信息进行操作后产生的交互信息发送至后端服务器。
其中，数据库信息包括：m/z信息、rt信息、同位素峰、强度值、匹配次数、化合物信息、二级谱信息、校正后的保留时间信息、标签信息、实验方法信息、标准品验证信息和一级谱信息中的至少一种。
浏览器获取质谱信息数据，包含算法生成的生信信息、后端服务器获取的用户权限信息和数据库信息，用户对数据库信息进行选择、标注等操作后产生了交互信息，例如，用户点击“标注”，浏览器发送交互信息，包含特征、样本组织、化合物、设备id、标注信息id、文件存储路径信息给后端服务器；其中的算法为峰检测、峰匹配、峰对齐算法中的任意一种。
步骤32、后端服务器根据交互信息更新数据库信息，并将更新后的数据库信息发送回浏览器。
其中，更新数据库信息可以包括：增加信息、删除信息或修改信息等。
步骤33、浏览器将更新后的数据库信息展示在质谱识别界面中。
第三种实施例是除上述方法外的普通的互联网前后端服务器交互方式。
本发明在使用峰检测算法的基础上，将质谱信息数据数字化，从而可以进一步可视化。
另外，传统的数据处理软件并没有进行可视化交互，仅仅是算法生成图片展示，而本申请采用浏览器绘图，把数据之间关联id找到，从而可以进行可视化交互。
以上所述，仅是本申请的几个实施例，并非对本申请做任何形式的限制，虽然本申请以较佳实施例揭示如上，然而并非用以限制本申请，任何熟悉本专业的技术人员，在不脱离本申请技术方案的范围内，利用上述揭示的技术内容做出些许的变动或修饰均等同于等效实施案例，均属于技术方案范围内。

Claims (11)

  1. 一种质谱可视化方法，其特征在于，包括：
    客户端或浏览器请求生信数据，后端服务器将质谱信息数据发送至所述浏览器，所述浏览器利用所述生信数据和所述质谱信息数据建立二维或三维的可视化信息图，所述后端服务器建立关联关系id；
    所述浏览器根据用户操作请求获取所述关联关系id，并通过所述关联关系id查找其对应的可视化信息图的数据信息；
    所述浏览器根据所述关联关系id查找后端服务器返回的所述数据信息，并通过可视化组件将所述关联关系id对应的可视化信息图展示在质谱识别界面中。
  2. 根据权利要求1所述的质谱可视化方法，其特征在于，所述浏览器利用所述生信数据和所述质谱信息数据建立二维或三维的可视化信息图，具体为：
    将所述生信数据转化为特征信息、数字信息、图片信息、向量信息和矩阵信息；
    将所述特征信息进行第一处理得到统计信息，并关联所述生信数据和所述统计信息；其中，所述第一处理为机器学习、深度学习和统计处理中的任意一种或几种；
    所述浏览器将所述特征信息中的m/z信息、rt信息转化为横纵坐标轴，形成二维的可视化信息图；或者，所述浏览器将所述特征信息中的m/z信息、rt信息转化为横纵坐标轴，将峰提取的同位素峰、强度值、匹配次数、化合物信息转化为z坐标，形成三维的可视化信息图，并通过所述关联关系id生成数据索引。
  3. 根据权利要求2所述的质谱可视化方法，其特征在于，所述关联所述生信数据和所述统计信息，具体为：
    所述生信数据和所述统计信息之间的关联包括一对多、多对一和一对一的数据关联。
  4. 一种质谱可视化方法，其特征在于，包括：
    客户端或浏览器请求生信数据，后端服务器将质谱信息数据发送至所述浏览器，所述浏览器利用所述生信数据和所述质谱信息数据建立二维或三维的可视化信息图，所述后端服务器建立关联关系id；
    所述浏览器获取所述质谱信息数据，所述质谱信息数据包括算法生成的统计信息、生信数据和后端服务器获取的用户信息，所述浏览器根据用户操作请求发送所述统计信息对应的参数信息至所述后端服务器；
    所述后端服务器将所述参数信息发送至算法服务器，所述算法服务器对所述参数信息进行第二处理，得到结果数据发送至所述后端服务器；其中，所述第二处理为统计、过滤、计算、机器学习、深度学习算法中的任意一种或几种；
    所述后端服务器将所述结果数据发送至所述浏览器，所述浏览器获取所述结果数据后，根据所述关联关系id查找对应的可视化信息图，并更新所述可视化信息图，将更新后的可视化信息图展示在质谱识别界面中。
  5. 根据权利要求4所述的质谱可视化方法，其特征在于，所述算法为峰检测算法。
  6. 根据权利要求4所述的质谱可视化方法，其特征在于，所述算法服务器对所述参数信息进行第二处理，具体为：
    所述算法服务器采用交、并、表联合或表过滤的方式对所述参数信息进行第二处理。
  7. 一种质谱可视化方法，其特征在于，包括：
    浏览器获取质谱信息数据，所述质谱信息数据包括算法生成的生信数据、后端服务器获取的用户权限信息和数据库信息，所述浏览器将用户对所述数据库信息进行操作后产生的交互信息发送至所述后 端服务器；
    所述后端服务器根据所述交互信息更新所述数据库信息，并将更新后的数据库信息发送回所述浏览器；
    所述浏览器将更新后的数据库信息展示在质谱识别界面中。
  8. 根据权利要求7所述的质谱可视化方法，其特征在于，所述算法为峰检测、峰匹配、峰对齐算法中的任意一种。
  9. 根据权利要求7所述的质谱可视化方法，其特征在于，所述数据库信息包括：m/z信息、rt信息、同位素峰、强度值、匹配次数、化合物信息、二级谱信息、校正后的保留时间信息、标签信息、实验方法信息、标准品验证信息和一级谱信息中的至少一种。
  10. 一种终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至9中任一项所述方法的步骤。
  11. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至9中任一项所述方法的步骤。
PCT/CN2021/089421 2020-09-29 2021-04-23 质谱可视化方法、终端设备和计算机可读存储介质 WO2022068186A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202011057129 2020-09-29
CN202011057129.8 2020-09-29

Publications (1)

Publication Number Publication Date
WO2022068186A1 true WO2022068186A1 (zh) 2022-04-07

Family

ID=77028349

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/089421 WO2022068186A1 (zh) 2020-09-29 2021-04-23 质谱可视化方法、终端设备和计算机可读存储介质

Country Status (2)

Country Link
CN (1) CN113204686A (zh)
WO (1) WO2022068186A1 (zh)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1389801A (zh) * 2001-05-31 2003-01-08 金宝电子工业股份有限公司 可直接在图形上进行数据点编辑的绘图型计算机
CN105572212A (zh) * 2014-10-14 2016-05-11 中国科学院大连化学物理研究所 一种基于可视化质谱信息的生晒参和红参快速识别方法
CN107449849A (zh) * 2017-10-11 2017-12-08 天津中医药大学 一种中药鉴别方法
CN109508384A (zh) * 2018-10-31 2019-03-22 北京国双科技有限公司 知识图谱的处理方法、装置、存储介质和处理器

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8711147B2 (en) * 2008-10-01 2014-04-29 International Business Machines Corporation Method and system for generating and displaying an interactive dynamic graph view of multiply connected objects

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1389801A (zh) * 2001-05-31 2003-01-08 金宝电子工业股份有限公司 可直接在图形上进行数据点编辑的绘图型计算机
CN105572212A (zh) * 2014-10-14 2016-05-11 中国科学院大连化学物理研究所 一种基于可视化质谱信息的生晒参和红参快速识别方法
CN107449849A (zh) * 2017-10-11 2017-12-08 天津中医药大学 一种中药鉴别方法
CN109508384A (zh) * 2018-10-31 2019-03-22 北京国双科技有限公司 知识图谱的处理方法、装置、存储介质和处理器

Also Published As

Publication number Publication date
CN113204686A (zh) 2021-08-03

Similar Documents

Publication Publication Date Title
US10762382B2 (en) Image recognition based on augmented reality
US8827710B2 (en) Realtime user guidance for freehand drawing
KR20180056677A (ko) 유니버셜 번역
US20230222369A1 (en) Facilitating device fingerprinting through assignment of fuzzy device identifiers
US11853450B2 (en) Detection of web application anomalies using machine learning
US10367839B2 (en) Graphical user interface programmed to display complex indicators based on structured threat expressions
JP2019530046A (ja) コンピュータシステムからのユーザ情報の収集
US11789996B2 (en) Nuanced-color search and recommendation system
US9218321B2 (en) Creating tag clouds based on user specified arbitrary shape tags
WO2022100007A1 (zh) 质谱数据自动上传方法及终端设备
JP2024038428A (ja) サンプル分子の特性を予測するための方法、システムおよび非一時的なコンピュータ可読媒体
WO2022068186A1 (zh) 质谱可视化方法、终端设备和计算机可读存储介质
JP6173754B2 (ja) 画像検索システム、画像検索装置および画像検索方法
WO2018053889A1 (zh) 分布式计算框架和分布式计算方法
US20190042852A1 (en) Supplementing a media stream with additional information
US20160063394A1 (en) Computing Device Classifier Improvement Through N-Dimensional Stratified Input Sampling
WO2019024238A1 (zh) 范围值数据统计方法、系统、电子装置及计算机可读存储介质
WO2017132012A1 (en) Image-based quality control
US9798449B2 (en) Fuzzy search and highlighting of existing data visualization
US20180196859A1 (en) Processing datasets of varying schemas from tenants
CN112433651B (zh) 区域识别方法、设备、存储介质及装置
US10685039B2 (en) Methods and system for comparing data of an electronic dataset
JP6777445B2 (ja) 引用マップ生成装置、引用マップ生成方法およびコンピュータプログラム
CN105787132A (zh) 用户行为分析的控制方法和用户行为分析的控制系统
US9679010B2 (en) Methods, systems, and apparatus for search of electronic information attachments

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21873850

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21873850

Country of ref document: EP

Kind code of ref document: A1