14 results about "Biological attributes" patented technology

Systems, methods, software and computer-usable media for annotating biomolecule-related data are disclosed. In certain exemplified embodiments, the biomolecules can be nucleic acids and the data can be sequence-related data. In various embodiments, systems can include one or more public or private biological attributes (e.g., annotation information databases, data storage devices and systems, etc.) sources, one or more genomic features data sources (e.g., genomic variant tools, genomic variant databases, genomic variant data storage devices and systems, etc.), a computing device (e.g., workstation, server, personal computer, mobile device, etc.) hosting an annotations module and / or a reporting module, and a client terminal.

The invention discloses an intelligent method and system for evaluating the ecological friendliness of fishes in a flow field, and belongs to the technical field of water conservancy projects. The method comprises the following steps: acquiring hydraulic parameter data of a target flow field and movement behavior data of fishes; constructing an input feature set based on the hydraulic parameters, the biological attribute parameters and the water entry features, constructing an output tag set based on the behavior mode tags, and training by adopting a random forest algorithm to obtain a fish behavior prediction model; inputting to-be-evaluated fish individuals and flow field parameters into the model to obtain prediction probabilities of different behavior modes; and the comprehensive ecological friendliness of the flow field is evaluated by performing statistical analysis on the prediction probabilities of a plurality of individuals. The system comprises a data acquisition module, a model training module and an evaluation application module. The method can accurately predict the behavior probability of fishes in the complex flow field, realizes quantitative and efficient evaluation of the ecological friendliness of the flow field, and provides a scientific basis for fish passing facility design and ecological restoration.

This invention belongs to the field of artificial intelligence technology, specifically a deep learning-based method for early warning and auxiliary diagnosis of animal epidemics in animal husbandry and veterinary medicine. It includes constructing an environmental-physiological feature decoupling model to separate environmental stress from pathological deviations; using graph convolutional neural networks to analyze the dynamic evolution of group social topology and capture individual out-of-group tendencies; employing a spatiotemporal multi-scale feature fusion architecture to integrate and process physiological sequences, posture features, and social relationships; and using a biological attribute mapping layer to reverse-map the neural network state into clinical pathological descriptors. This application achieves environmental interference removal and accurate capture of the epidemic incubation period, provides logically traceable auxiliary diagnostic reports, and significantly improves the certainty of epidemic prevention decisions.

The invention relates to the technical field of soil quality evaluation and improvement, in particular to a shajiang black soil structural obstacle factor and threshold value calculation method, which comprises the following steps: firstly, screening 22 structural obstacle factors covering soil physicochemical and biological attributes, and constructing a combined weight by adopting an entropy weight method, a principal component analysis method and an average weight method; the method comprises the following steps: calculating the membership of each factor by combining ring-type, peak-type and S-type membership functions, obtaining a soil structural obstacle comprehensive index through an accumulation method, dividing obstacle grades, determining a threshold value of each factor by taking a gradient boosting regression tree as a core and combining T test and random forest verification, and finally constructing a minimum data set based on principal component analysis and clustering analysis; the problems that an existing method is one-sided in obstacle factor screening, high in weight determination subjectivity, single in threshold calculation method and difficult to popularize are solved.

This invention discloses a circRNA-disease association prediction method based on multi-source feature fusion, belonging to the field of disease-aided diagnosis technology. The method first constructs a circRNA-disease association network and extracts the GIPK functional features of the disease and the Transformer sequence features of the circRNA; then, it systematically learns the network topology embedding at three scales: microscopic, mesoscopic, and macroscopic; finally, it deeply fuses the above multi-source features and inputs them into an XGBoost classifier. This invention systematically solves the three major technical defects of existing technologies—"lack of biological semantics," "single network representation," and "inefficient feature fusion"—by introducing biological attribute features, multi-scale network structure features, and a deep fusion strategy, achieving high-precision, high-robustness, and highly biologically interpretable association prediction.

The application discloses a CircRNA-miRNA correlation prediction method and device and a medium. The method comprises the following steps: constructing a data set, wherein the data set comprises a plurality of CircRNAs and a plurality of miRNAs; based on the data set, biological attribute features are extracted from word embeddings of CircRNA sequences and miRNA sequences; behavior features are extracted based on isomorphic graph embedding; training features of a fusion carrier are obtained based on the biological attribute features and the behavior features, so that the interaction scores of circRNAs and miRNAs are learned, and potential CMAs are inferred. The application successfully predicts the complex relationship between circRNAs and miRNAs, the accuracy is 82.90%, and the AUC is 0.9075.

A method of verifying multi-modal medical data is proposed. The method comprises: accessing multi-modal medical data of a subject, the multi-modal medical data comprising a medical image of a specimen slide, wherein a specimen in the specimen slide was collected from the subject; generating a prediction pertaining to a biological attribute of the medical image based on the medical image; determining a degree of consistency between the biological attribute of the medical image and other modalities of the multi-modal medical data; and outputting, based on the degree of consistency, an indication of whether the multi-modal medical data contain inconsistency.

The present application relates to the technical field of identification coding, and in particular to an electronic quarantine identification traceability management system. The present application ensures the authenticity and reliability of the data source by obtaining the birth date and immunization date of animals; calculates the age difference of adjacent individuals and immunization, combines historical data to dynamically construct batch consistency mean and dispersion, and realizes the quantitative characterization of the batch biological attribute consistency; uses attribute outlier coefficient to quantify the standardized deviation of individuals relative to the normal level of the batch, and introduces the scanning time for correction, which unifies the physical operation rate and the biological attribute deviation into a dimensionless operation abnormality evaluation index; the simulator extremely fast single factor is amplified due to the scanning time approaching zero, and the heterogeneous individual mixing factor is directly reflected due to the increase of the attribute outlier coefficient, which solves the two types of violation problems without increasing the hardware cost; finally, the reliability and accuracy of the electronic quarantine identification traceability are significantly improved through the index.

The application relates to an artificial intelligence technology and discloses a product risk resistance ability identification method, which comprises the following steps: obtaining a to-be-protected product, to-be-protected product coordinates and biological attributes of the to-be-protected product; calculating an initial risk resistance score of the to-be-protected product according to historical business records and a preset risk calculation strategy; obtaining geomorphic features in a field sampling set by using a geographical feature extraction network to obtain a geomorphic feature vector set; performing biological-environment matching on the geomorphic feature vector set, a quantized biological attribute vector and a coordinate vector by using an adaptive identification model to obtain an environment adaptation level; and performing weight calculation on the initial risk resistance score by using an environment score corresponding to the environment adaptation level to obtain a risk resistance ability value of the to-be-protected product. The application further discloses a product risk resistance ability identification device, an electronic device and a storage medium. The application can improve the accuracy of product risk control.

The application belongs to the technical field of exosome purification and characterization, and particularly relates to an exosome purification method and a quality control method suitable for industrial level. The application establishes an exosome purification process combining TFF and whole strong anion exchange chromatography, and selects a nuclease to remove chromatin contamination, aiming to provide an alternative reference for industrial level large-scale preparation of natural exosomes or engineered exosomes for clinical purification. Meanwhile, the application also establishes a whole exosome quality control system combining NTA, WB, TEM, ExoView and FFF-MALS-DLS. By using the exosome purification method and the quality control method, the physical properties and biological attributes of exosomes in the sample can be linked, the most accurate exosome characterization under the current technological conditions can be realized, the quality control of exosome samples at each stage of the process can be realized, and exosomes with almost no chromatin contamination can be obtained.

The invention provides a specimen data labeling method and system applied to a water ecology laboratory, and the method comprises the steps: obtaining a specimen sample collected by the water ecology laboratory and corresponding multi-source environment metadata, and carrying out the construction of an environmental factor response curve and the extraction of a biological attribute time-varying feature of an environmental parameter sequence and biological attribute observation data; coupling the multi-source environment metadata with the specimen sample attributes to generate an initial specimen characteristic spectrum; performing feature space mapping, multi-modal semantic verification and entity relationship reasoning on the context-enhanced annotation vector to generate a context-enhanced annotation vector; performing historical trajectory similarity comparison and multi-dimensional evolution trend prediction on the data to generate an intermediate evolution label set; and the intermediate evolution label set is optimized and integrated through multi-dimensional semantic consistency verification and an evolution path, and a labeling label with ecological evolution semantics is generated. Through the method provided by the invention, the recording capability of the labeling result on the ecological evolution process of the specimen can be improved.

The invention provides a traditional Chinese medicine growth quality evaluation method, which comprises the following steps: acquiring multi-source data, namely acquiring data of an ecological environment, biological attributes and planting management; preprocessing the multi-source data; constructing an index system, wherein the index system comprises a target layer, a system layer, a category layer and an index layer; determining the weight of each index; calculating a coupling coordination degree model to obtain a coupling coordination degree value; obstacle factors are diagnosed, coordination levels are divided, and the obstacle factors are positioned and recognized; and outputting an evaluation report. According to the invention, through a mode of combining multi-source growth data processing, multi-index coupling coordination degree modeling and an obstacle factor identification algorithm, a mixed framework combining a multi-index coupling coordination degree model and an obstacle degree diagnosis model is constructed; quantitative evaluation and dynamic early warning of an internal mechanism of a complex organism-environment-management system in a traditional Chinese medicine growth system are realized.

The embodiment of the specification discloses a privacy protection method, device and equipment for biological identification. The method is applied to a terminal device and includes the following steps: obtaining a biological identification request, wherein the biological identification request carries biological information of a target user; inputting the biological information into a biological attribute model obtained through model training in advance, extracting user attribute information of the target user from the biological information through the biological attribute model, and obtaining user attribute information corresponding to the biological information; sending the user attribute information corresponding to the biological information to a server to trigger the server to determine a privacy protection model for desensitizing the biological information based on the distribution of the user attribute information of the target user; receiving the privacy protection model issued by the server, desensitizing the biological information based on the privacy protection model, obtaining desensitized biological information, and performing biological identification processing corresponding to the biological identification request based on the desensitized biological information.

The invention relates to the technical field of intelligent decision making, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a biological attribute recognition method, device, equipment and medium based on artificial intelligence. Obtaining a target biological information file; identifying a target type of a target organism in the target biological information file, and calling a special biological identification model interface corresponding to the target business scene according to the target type; calling a target biological recognition model of the target business scene by using the special biological recognition model interface; performing feature recognition on a target organism in the target business scene by using the target organism recognition model to obtain a biological feature of the target organism; and performing confidence coefficient detection on the biological characteristics, and when the confidence coefficient reaches a preset threshold value, determining an attribute recognition result of the target organism according to the biological characteristics. The efficiency of biological attribute recognition is improved.