22749 results about "Bioinformatics" patented technology

The method can include: recording a neural state, determining a latent array, and determining an output based on the latent array. In variants, the method can function to enable a subject to use neural signals to navigate within a high-dimensional concept space to control an output (e.g., image generation, motor control, communication, etc.).

A system and method for maintaining an oxygen concentration of a biological sample. The oxygen concentration can be maintained by measuring the oxygen concentration within the biological sample and adjusting a rate of an oxygen supplier in response to this measurement. For example, when the oxygen concentration is below a threshold, oxygen can be delivered to the biological sample at a higher rate.

The invention provides a method for establishing a diagnosis and treatment system for digestive system disease multi-modal information. The method comprises the following steps: S1, collecting multi-modal information for labeling and preprocessing; s2, extracting a feature vector and embedding a label into the multi-modal information according to the labeled information; s3, splicing and mapping the feature vector and the tag to a unified dimension to obtain an enhanced feature vector; s4, fusing the enhanced feature vectors to form a multi-modal feature matrix, performing linear mapping and weighted aggregation on the multi-modal feature matrix to obtain global fusion vectors, and collecting to generate a fusion vector sequence; s5, enhancing the time sequence information of the global fusion vector sequence, enhancing the spatial information of the spatial relevance of the specific feature of the part, and performing interactive fusion to obtain a spatio-temporal joint feature; s6, performing classification prediction on the disease stage or the specific pathological type, and outputting a diagnosis result; and S7, performing semantic association on the diagnosis result and the medical knowledge graph, sharing data to an online health intelligent platform, and providing a personalized decision basis for clinicians.

A database structure obtained by means of information retrieval, and a reasoning system, which structure and system specifically relate to a gut microbe knowledge graph system, comprising: a gut microbe knowledge graph consisting of a gut microbe knowledge base, a gut microbe and small-molecule drug therapy association knowledge base, and a clinical medicine database; and a multimodal uncertainty reasoning system, using the gut microbe knowledge graph. The gut microbe knowledge graph system predicts potential diseases, drugs, genes, etc., which are associated with gut microbes.

The invention discloses an overlapped cervical cytoplasm region segmentation method based on deep learning and a conditional diffusion model, and relates to the technical field of artificial intelligence analysis of medical images. According to the method, accurate segmentation of the overlapped cytoplasm region in the cervical cell image is realized through a morphological prior guided conditional diffusion process. The method comprises the following steps: constructing a multi-scale cervical cytoplasm mask pair image; designing a cytoplasm specific data enhancement and preprocessing process; building a multi-branch cervical cell morphology perception condition diffusion network; using a self-adaptive multi-scale combination loss function to optimize training; and a hierarchical classifier is adopted to freely guide sampling for reasoning. According to the method, the frequency domain and space domain features are fused, a cellular morphology and statistics priori knowledge base is established, and a strategy of generating complete cytoplasm by adopting non-overlapped parts is adopted, so that the problem that the traditional method is difficult to segment in complex backgrounds and overlapped regions is successfully solved, and reliable technical support is provided for early screening of cervical cancer.

The invention provides a semi-supervised target detection method for a visible light-infrared multi-mode fusion scene. The method comprises the following steps: constructing a semi-supervised visible light-infrared multi-modal image data set based on an LLVIP data set; on the basis of a YOLOv11 model architecture, constructing a target detection model oriented to multi-modal image feature fusion, and training the target detection model by using a semi-supervised visible light-infrared multi-modal image data set in a deep learning end-to-end mode to obtain a trained target detection model; and inputting a to-be-detected multi-modal image into the trained target detection model, and outputting a target detection result of the to-be-detected multi-modal image by the trained target detection model. The method is based on a semi-supervised learning normal form, so that the precision and robustness of target detection in a multi-modal fusion scene are improved, and the requirements for high efficiency and reliability of target recognition in practical application scenes such as intelligent traffic and intelligent security and protection are met.

The invention belongs to the technical field of medical data processing and artificial intelligence, and particularly relates to a cardiovascular disease risk prediction system based on multi-modal fusion, which comprises a multi-modal data acquisition and preprocessing module, a cross-modal association graph construction module, a dynamic fusion and prediction module based on a graph neural network and an interpretability analysis module. By constructing a heterogeneous graph fusing prior knowledge and data driving and utilizing a graph attention network to perform multi-level dynamic feature fusion, deep integration and interaction of multi-modal data such as genomes, iconography, clinical and intestinal flora metabolism are realized, so that the accuracy and interpretability of cardiovascular disease risk prediction are improved.

The invention relates to the technical field of artificial intelligence, in particular to a pest and disease identification method and system based on image processing and a medium, and the method comprises the steps: obtaining an original image of a plant leaf, and carrying out the preprocessing of the original image, and obtaining a preprocessed image; performing scab region segmentation on the preprocessed image by using an improved U-Net model to obtain a contour and a position of a scab; carrying out feature extraction on the segmented scab region, extracting deep semantic features through a pre-trained ResNet-50 network, and carrying out splicing fusion on the deep semantic features and color features and shape features of the scab to form a comprehensive feature vector; inputting the comprehensive feature vector into an integrated classifier based on XGBoost to carry out disease and insect pest type identification, and outputting disease and insect pest types and corresponding probabilities; the accuracy of pest and disease prediction can be improved.

The invention provides a brain tumor survival prediction method and system based on a multi-modal medical knowledge graph, and belongs to the technical field of brain tumor survival prediction. The multi-modal medical knowledge graph based on third-party knowledge base fusion is constructed; performing feature extraction on the brain tumor multi-modal data; searching an entity corresponding to the brain tumor related data in the multi-modal medical knowledge graph, and converting the entity into feature representation by using an entity representation learning method; the learned feature representation related to the brain tumor type complements the missing data mode, and finally the complemented features are input into a pre-trained survival prediction model to achieve brain tumor survival prediction. According to the multi-modal medical knowledge graph, comprehensive medical knowledge support meeting clinical requirements is provided; the multi-modal mapping knowledge domain is used for missing modal completion of brain tumor survival prediction, and a completion feature is generated by querying an associated entity through the mapping knowledge domain, so that the problem of weak modal missing processing capability in the prior art is solved.

The invention relates to the technical field of brain-computer interfaces, and provides a brain-computer interface instruction issuing method, device and equipment based on regulation enhancement simulation, and the method comprises the steps that an electroencephalogram decoding model comprises an encoder, a feature enhancer and a task classifier, the encoder encodes a real-time electroencephalogram signal to obtain compression representation before nerve regulation, and the feature enhancer is used for classifying the compression representation before nerve regulation; the feature enhancer performs feature enhancement on the compression representation to obtain enhanced representation, and the task classifier classifies the enhanced representation to obtain an electroencephalogram decoding result. According to the method, a feature enhancer is obtained by combining training of a state discriminator based on a sample electroencephalogram signal collected before nerve regulation and a real state label after nerve regulation, and the feature enhancer is driven to learn a feature migration relation between a compression feature before nerve regulation and a feature after nerve regulation; the feature characterization capability of an electroencephalogram decoding model on electroencephalogram signals is remarkably improved, so that the decoding robustness on weak stimulation signals is enhanced on the premise of not depending on high-intensity external stimulation.

A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and / or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and / or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and / or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

The invention provides a multi-modal characterization molecular property prediction method based on hierarchical bidirectional cross attention, and relates to the technical field of machine learning assisted organic chemistry, and the method comprises the following steps: S10, generating same-molecule multiple sequences for data enhancement; s20, coding the sequence features through a pre-trained molecular language model MolBERT; s30, performing multi-modal feature fusion through a layered bidirectional cross attention mechanism; s40, establishing a prediction head; s50, in the reasoning stage, only the feature extraction and fusion steps are executed, and a molecular property prediction result is output through the trained prediction head. According to the method, the molecular sequence, the topological graph structure and the fingerprint features are effectively integrated, so that the prediction precision of the model on a plurality of MoleculeNet (molecular network benchmark) public data sets is superior to that of an existing method.

The invention discloses a body feeling evaluation method based on electroencephalogram-electromyographic signal fusion and dynamic interaction modeling. The body feeling evaluation method comprises the steps that EEG signals and EMG signals in the lower limb movement process of a subject are synchronously collected; carrying out band-pass filtering, artifact removal and wavelet transform processing on the acquired signals, extracting multi-channel time-frequency features, and forming a preprocessing feature matrix; fusing the time-frequency features of the EEG signal and the EMG signal, constructing a multi-modal feature set, and compressing feature dimensions by adopting a sparse coding method; inputting the compressed feature sequence into a neural network model combining a long short-term memory network and an attention mechanism, and carrying out dynamic interaction modeling; and an interaction index sequence is generated based on model output, and an interaction matrix is constructed through a sliding window and Gaussian kernel smoothing processing, so that visualization of brain-muscle interaction strength and dynamic quantification of a proprioceptive function are realized. The invention further provides a system for implementing the method. The method is high in objectivity, high in feature extraction precision and excellent in dynamic modeling capability.

The invention belongs to the field of molecular biological identification, and particularly relates to a specific SNP (Single Nucleotide Polymorphism) site combination for identifying a Wuzhishan pig variety and application. The specific SNP site combination for identifying the Wuzhishan pig variety comprises 77 SNP sites, and the physical positions of the SNP sites are determined by sequence comparison based on a pig reference genome Sscrofa11.1. The specific SNP site combination is screened based on a method of combining whole genome association analysis with selection signal analysis so as to ensure the accuracy of site selection. The selected specific SNP site combination can rapidly realize accurate identification of the Wuzhishan pig variety on the gene level, and has significant application value in the aspects of genetic resource accurate protection and variety utilization of Hainan Wuzhishan pigs.

The invention discloses a colorectal cancer drug relocation method based on multi-omics integration. The system comprises a multi-omics data acquisition and preprocessing module, a tumor microenvironment analysis module, a specific disease network construction module, a multi-dimensional drug relocation module and a result evaluation module. And the tumor microenvironment analysis module comprises cell heterogeneity identification, cell map construction, cell annotation and tumor cell subset annotation. The specific disease network construction module comprises tumor feature expression program extraction, expression program screening, meta-program construction, clinical related meta-program recognition and specific disease protein interaction network construction. And the multi-dimensional drug relocation module comprises a module for identifying diseases by using a random walk algorithm, carrying out drug screening based on disturbance data, carrying out drug screening based on network proximity and carrying out comprehensive drug relocation. From the perspective of single cell data, element programs related to colorectal cancer survival are excavated, corresponding modules are designed, and the efficiency and precision of colorectal cancer targeted drug screening are improved.

The invention discloses a bird identification method based on sound-image multi-modal fusion. The bird identification method comprises the following steps: S1, carrying out standardized frame-level preprocessing on bird audio signals; s2, acoustic features are extracted and enhanced, and an acoustic high-level feature vector which highlights birdsong discrimination information and suppresses environmental noise is obtained; s3, visual image standardization preprocessing; s4, performing visual feature extraction and multi-scale fusion to obtain a visual high-level feature vector which enhances correspondence to the bird key form area and inhibits background interference; s5, performing dynamic weighted fusion on the decision-making layer to obtain a bird existence probability; and S6, comparing the bird existence probability with a preset threshold value of the corresponding bird, and judging whether the bird exists or not and the type of the existing bird. Through cross-modal feature enhancement and adaptive fusion, the precision, robustness and real-time performance of bird recognition in a complex orchard environment are significantly improved, and a core technical support is provided for green intelligent bird repelling.

The invention discloses a multi-mode emotion continuous recognition method for medical treatment, belongs to the technical field of artificial intelligence and medical treatment information, and mainly aims to simulate the dynamic change process of emotion by establishing a Neural ODEs framework and overcome the static property and discreteness of emotion modeling in a traditional method. Through a causal inference technology, emotional features are separated from individual-independent physiological differences, and the generalization ability across individuals is improved. A self-supervised learning method is utilized, the synergistic effect between the EEG and the eye movement signal is improved through cross-modal contrast learning, and the emotion recognition precision is enhanced. The calculation complexity is reduced through a dynamic sparse attention mechanism, and meanwhile, focusing is performed on a key time slice in emotion recognition. Through multi-task joint learning, the model learns multiple tasks such as emotion intensity regression and tested identity recognition during emotion classification, and the personalized emotion recognition capability is improved.

The invention discloses a brain deep nerve stimulation method and system based on adaptive adjustment, and relates to the technical field of brain deep nerve regulation, and the method comprises the steps: collecting a local field potential signal of a brain deep target region of a target patient, and extracting a beta frequency band power spectrum density and a gamma frequency band phase synchronization index as neural activity characteristic parameters; determining an individual baseline value and a preset threshold value based on historical data, and outputting a stimulation adjustment trigger signal when the beta frequency band power spectral density exceeds the individual baseline value and the gamma frequency band phase synchronization index is lower than the preset threshold value; in response to the trigger signal, calculating an optimal stimulation parameter combination through a gradient descent optimization algorithm and executing nerve regulation; and monitoring the signal change after regulation and control, calculating a relative change rate and updating a threshold value. Through a two-parameter joint judgment mechanism and a threshold updating strategy, individualized adaptive adjustment of stimulation parameters is realized, and the stimulation accuracy and the treatment effect are improved.

The invention provides a production strain of an antibiotic-free plasmid, the production strain is a gene editing strain of a PIR strain and is named as PIR1-WN:: 0636 or PIR1-PR: 0636, the production strain contains a nucleotide sequence for coding toxin protein and the antibiotic-free plasmid, and the antibiotic-free plasmid contains a nucleotide sequence for coding antitoxin protein; and preferably, the replicon DNA element of the nonreactive plasmid is R6K-gamma. The toxin protein gene of the production strain disclosed by the invention can be stably passaged, has lethality after being induced and can be used for plasmid screening; according to the invention, the positive rate of transforming the nonreactive plasmid into the PIR1-WN:: 0636 strain is more than 80%, and stable production of the plasmid with a high superhelix ratio can be realized.

The invention discloses a text and image fused online comment toxicity detection and filtering method and system, and relates to the technical field of natural language processing, and the method comprises the steps: extracting a semantic vector of a comment text through a RoBERTa-Marge model; the visual features of the image are extracted through an OfficientNet-V2 model; aligning heterogeneous modal features by adopting a double-flow contrast loss function; self-adaptive decision making of culture sensitivity: loading a regional sensitive rule table according to a user IP address, and dynamically adjusting symbolic semantics; calculating an intimacy correction factor based on the social relationship between the publisher and the receiver; and performing context weighted toxicity scoring, calculating a user historical behavior weight, and outputting a final toxicity probability. According to the method, a deep dynamic mapping mechanism of text and visual features is constructed, heterogeneous features are extracted through RoBERTa-Large and OfficientNet-V2 double-flow architectures, semantic space alignment is forced by utilizing comparative learning, and the problem of image-text splitting detection in the traditional technology is solved.

The invention belongs to the field of bioinformatics, and relates to an improved integrated deep learning cell communication ligand-receptor interaction prediction method. The method comprises the following steps: firstly, carrying out extraction and dimensionality reduction on biological sequence features of a ligand and a receptor, and constructing multi-modal feature input; secondly, constructing an improved deep neural network branch, introducing a batch normalization layer and a Leaky ReLU activation function, solving the problems of gradient disappearance and neuronal necrosis, and improving regularization strength to prevent overfitting; meanwhile, an enhanced heterogeneous graph auto-encoder branch is constructed, the graph embedding dimension is remarkably expanded to improve the feature capacity, and full convergence of the model is ensured by increasing training rounds; thirdly, fusing the improved deep network with the prediction probability of a heterogeneous graph auto-encoder by adopting a weighted integration strategy; and finally, outputting a potential interaction relationship based on the fusion probability. By optimizing the architecture and the strategy, the prediction accuracy and robustness are remarkably improved, and a reliable tool is provided for analyzing a complex cell communication network.

The invention discloses a wild jujube leaf characteristic fingerprint spectrum construction method and system, and belongs to the technical field of traditional Chinese medicine, and the construction method specifically comprises the following steps: I, collecting original spectrum data of each wild jujube leaf sample, and extracting and fusing deep nonlinear characteristics of each original spectrum data to form a unified spectrum fusion characteristic vector; iI, identifying chemical components in each wild jujube leaf sample, extracting chromatographic peak areas and mass-to-charge ratios of different chemical components, and constructing a chemical component association diagram; the method breaks through the limitation of constructing the wild jujube leaf sample characteristic fingerprint spectrum by traditional single data, can intuitively reflect the synergistic change rule of components of wild jujube leaf samples from different producing areas or in different batches, and enhances the interpretability of the wild jujube leaf sample characteristic fingerprint spectrum at the same time; the distinguishing precision of wild jujube leaf sample differences can be improved, a clear judgment basis is provided for wild jujube leaf sample quality evaluation and active ingredient traceability, and the integrity of the wild jujube leaf sample characteristic fingerprint spectrum is guaranteed.

The invention discloses a non-contact multi-modal decoupling emotion recognition method and device in a dialogue scene. The method comprises the following steps: acquiring original data of multiple modals in the dialogue scene; encoding the original data into original features by using a mode-dedicated encoder; projecting the original features by using a shared feature projector to obtain projection features, and performing weighted fusion to obtain shared features; extracting exclusive features from the original features by using a modal-specific expert network, and carrying out weighted fusion on the exclusive features to obtain private features; fusing the shared features and the private features through a cross attention fusion module to obtain multi-modal fusion features; and classifying the multi-modal fusion features by using a first classifier to obtain an emotion recognition result. According to the method, the key problems of high modal feature heterogeneity, inconsistent modal information, unbalanced modal, missing and the like in the field of multi-modal emotion recognition are solved, and the performance and robustness of emotion recognition in a dialogue scene are improved.

The invention belongs to the field of bioinformatics, and relates to an anti-cancer drug response prediction method based on an attention mechanism. The method comprises the following steps: firstly, capturing uniform-dimension drug and cancer cell line characteristics through a multi-layer perceptron; secondly, fusing drug characteristics by adopting a Transform encoder, and constructing a cell encoder for cancer cell line characteristic polymerization; then, designing a cross-modal cross fusion module to promote information interaction between the two; and finally, predicting a semi-suppressed concentration value subjected to logarithmic transformation between the two through a multi-layer perceptron. Experimental results show that compared with an existing optimal method, the method has the advantage that the RMSE is reduced by 2.9%. According to the method, accurate prediction of the anti-cancer drug response is achieved by integrating drug and cancer cell line data, screening of potential anti-cancer drugs can be accelerated, personalized treatment schemes can be optimized, the cure rate of cancer patients is further increased, and the method has great significance in cancer treatment.

This disclosure provides methods for spatial profiling of biological analytes present in a biological sample. Methods include generating feature arrays using a master / copy format using recessed arrays, and methods for using such arrays. For example spatially-tagged analyte capture analytes can be used in spatial detection in methods to determine the location of analytes (e.g., proteins) in biological samples.

This disclosure provides a method, apparatus, electronic device, and storage medium for animal language conversion, relating to the field of artificial intelligence technology, specifically machine learning, deep learning, and natural language processing. The specific implementation involves: acquiring multimodal data related to the animal, including animal vocal data, animal behavioral data, and animal physical characteristics data; preprocessing the multimodal data to obtain fused multimodal data; identifying the animal's current emotion based on the fused multimodal data to obtain an emotion recognition result; and performing semantic mapping and language translation on the emotion recognition result to convert the animal language into human language, obtaining a language conversion result. This disclosure can accurately identify the animal's current emotional state and convert it into human language, thereby achieving deeper emotional communication and understanding between animals and humans, and improving the accuracy and efficiency of cross-species communication.

The invention relates to the technical field of artificial intelligence and data analysis, in particular to an unsupervised anomaly detection method and system based on comparative potential fusion. The method aims at solving the problems that in the prior art, an unsupervised anomaly detection method is limited in feature expression ability, sensitive in noise, insufficient in potential feature discrimination and lack of statistical interpretability in detection results. According to the method, the global potential features generated by comparison learning and the self-encoder reconstruction residual error are fused, the statistical model is combined for self-adaptive threshold judgment, the problems of insufficient feature expression and high noise sensitivity in multi-source heterogeneous time series data anomaly detection are effectively solved, and the method has the advantages that the detection precision and robustness are improved, and the dependence on labeled data is reduced.

The invention discloses a broad-spectrum lytic bacteriophage for preventing and controlling various vibrio infections in an aquaculture environment. The invention relates to V.alginolyticus phage vBValM-R37J, which is preserved in Guangdong Microbial Culture Collection Center (GDMCC) on August 6, 2025, the address is No.100 Courtyard, Xianlie Middle Road, Xianlie District, Guangzhou City, Guangdong Province, the postcode is 510070, and the preservation number is GDMCC NO.66813-B1. The invention further discloses a method for preparing the V.alginolyticus phage vBValM-R37J strain. The bacteriophage R37J is a candidate therapeutic bacteriophage with wide host spectrum, high splitting efficiency, gene safety and environmental stability, and has the potential of being popularized and applied in aquaculture as a novel biological tool for accurately preventing and controlling various vibrio infections. A new biological resource is provided for accurate prevention and control of aquatic pathogenic bacteria, and a theoretical basis and a technical support are provided for popularization and application of a phage therapy in an actual culture system.

The invention relates to the technical field of Chinese trumpet creeper breeding, and discloses a Chinese trumpet creeper-based efficient breeding decision support system and method. The method comprises the following steps: acquiring multi-dimensional phenotypic data in a Chinese trumpet creeper breeding test, wherein the multi-dimensional phenotypic data comprises plant height, leaf area, flowering period and yield indexes; performing normalization processing on the multi-dimensional phenotypic data, eliminating influences of different dimensions, and calculating a variable coefficient of each phenotypic index; then screening out phenotypic indexes of which the stability is higher than a preset threshold value based on the variable coefficient, and constructing a Chinese trumpet creeper phenotypic feature matrix; carrying out dimensionality reduction on the Chinese trumpet creeper phenotypic feature matrix by adopting an adaptive weighting algorithm, and extracting key phenotypic features; inputting the key phenotypic features into a genetic algorithm optimization module, and calculating the contribution degree of each genetic locus in combination with the Chinese trumpet creeper genotype data; and constructing a Chinese trumpet creeper breeding decision model according to the contribution degree of the gene locus, and outputting an optimal breeding combination scheme. The system can optimize the Chinese trumpet creeper breeding process and provide effective support for Chinese trumpet creeper breeding work.

The invention discloses a dynamic identification method for unusual cells before young tumors based on multi-omics data, and relates to the technical field of cell unusual identification. A dynamic correlation intensity matrix and a cumulative effect contribution matrix are constructed, a differentiation screening strategy is implemented according to individual response characteristics, and the unusual cells before young tumors are identified. And the abnormal dynamic high-fidelity identification of the young tumor pre-cells is realized. And aiming at individuals of different response types, an instant path, a long-term path or a double-path fusion strategy is respectively adopted, key behavior data is accurately screened, and the input quality is improved. According to the method, redundant interference is effectively eliminated, the simulation capability of the model on key processes such as immunosuppression and DNA damage accumulation is enhanced, the biological rationality and prediction precision of a cell state evolution sequence are remarkably improved, and the problems of model response lag, low calculation efficiency and output distortion caused by data noise in the prior art are solved; and a reliable technical support is provided for early warning and individualized intervention of precancerous lesions.