161 results about "Drugs response" patented technology

A computer-implemented system for personalized health optimization constructs a confidence-weighted personal health knowledge graph (PHKG) from heterogeneous data, including wearable sensors, medical devices, lab results, medication logs, and conversational inputs. A multi-stage causal-inference stack identifies modifiable drivers of outcomes using layered methods (e.g., MI, GAM, Neural Granger, DAG-GNN), and simulates candidate interventions. A recommendation engine ranks lifestyle or pharmacologic actions using a benefit-to-friction score, selecting a personalized intervention aligned with user readiness and clinical safety constraints. Interventions may include a minimum effective dose (MED), optimal level, adaptive low-dose, or behavioral challenge. Optional modules include reinforcement learning for timing adaptation and privacy-preserving on-device inference. The system operates across domains including metabolic, cardiovascular, renal, sleep, stress, and medication response, enabling cross-condition synergy evaluation. The architecture is modular, supports runtime plug-in targets, and adapts in real time with or without continuous clinical oversight, depending on deployment.

The invention relates to the technical field of anesthesia effect evaluation, in particular to an anesthesia effect evaluation system based on big data, which comprises a physiological data processing module, a patient grouping and analysis module, an anesthesia risk evaluation module and an anesthesia adjustment support module. And collecting physiological indexes and types and doses of anesthetic drugs. According to the method, the real-time processing capacity of physiological data is enhanced by integrating historical health records and operation information, accurate evaluation of the instant effect of anesthetic is provided by deeply analyzing time sequence changes of heart rate and blood pressure, the response of different patient groups to the anesthetic can be recognized through grouping analysis, and the accuracy of the anesthetic is improved. Therefore, a targeted anesthesia scheme is realized, the anesthetic injection time and dosage are analyzed by using the LSTM network, the dynamic prediction of drug response is optimized, a doctor is supported to adjust the anesthetic dosage according to real-time data, the precision and safety of anesthesia management are greatly improved, and the anesthesia side effect risk is reduced.

Platinum-acridines and analogs thereof as cytotoxic agents for cancer treatment. Also provided methods of using hMATE1 (SLC47A1) as a biomarker to identify tumors that are likely to respond to the agents, and epigenetically sensitizing tumor tissue to anticancer drugs targeting this membrane transporter.

The invention relates to the technical field of clinical medical data visualization, in particular to a visualization system for clinical internal medicine and a visualization method thereof. The method comprises the following steps: acquiring basic information of a clinical patient; according to the basic information of the clinical patient, performing medical treatment and medication event analysis, and performing triple entity relationship processing to generate patient medical record relationship map data; performing patient drug tolerance analysis based on the patient medical record relation map data to generate patient drug tolerance characteristic data; performing clinical physiological index risk probabilistic reasoning according to the drug tolerance characteristic data of the patient to generate individual clinical medication risk data; according to the individual clinical medication risk data, individual characteristic medicine dosage adjustment is carried out, medicine dosage curve visual dynamic display is carried out, and visual clinical medication response data is generated. According to the method, personalized and visual medication decision support is provided for clinicians through patient medication conditions and real-time physiological index analysis.

The invention is applicable to the field of artificial intelligence, and particularly relates to a drug response prediction method capable of explaining a multi-relational graph neural network, which comprises the following steps of: constructing a directed heterogeneous graph containing cell lines, drug nodes, sensitivity, resistance, drug similarity and cell similarity relation types; an R-GCN model is adopted to process the directed heterogeneous graph, node representation is updated through node feature aggregation, positive and negative samples are generated for training, and a link relation between a potential cell line and a drug is predicted; the method comprises the following steps: generating a calculation graph by combining an original graph and a prediction result, extracting a to-be-explained edge neighborhood graph, obtaining an explaining sub-graph by maximizing mutual information and optimizing a structure score by utilizing an optimizable mask and an edge type weighting mechanism, and defining an explaining process from two aspects of mutual information and structure score. According to the method, link prediction is carried out by using the R-GCN model integrated with the attention mechanism, the interpretation subgraph is obtained by using the mask and the adaptive weighting mechanism, and the biological mechanism of the prediction result is clearly explained from multiple aspects.

A federated distributed computational system enables secure drug discovery and resistance tracking through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates molecular dynamics simulations with machine learning models for drug discovery analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for molecular dynamics simulation and resistance pattern detection. Through a distributed graph architecture, the system enables real-world clinical data integration, resistance evolution tracking, and multi-scale tensor-based analysis with adaptive dimensionality control. The system implements real-time drug response prediction through multi-modal data analysis, enabling pharmaceutical companies and research institutions to collaborate on complex drug discovery projects while maintaining strict data privacy controls.

The invention provides a drug response prediction method based on a gene relation network and a drug substructure. The drug response prediction method comprises the following steps: performing feature extraction on three multi-modal features of gene expression, copy number variation and mutation of a cell line by adopting an enhanced graph attention network; acquiring a substructure of the medicine by using a gating message passing neural network, and extracting features by enhancing a graph attention network; constructing two complementary graph structures, namely an atom-bond graph and a bond-angle graph, and performing feature extraction and message passing on the two graphs by using a graph convolutional neural network so as to extract 3D features of the medicine; the characteristics of the drug and the cell line are integrated through concat operation, and drug response prediction is carried out through mlp. According to the drug response prediction method provided by the invention, cell line features are extracted by establishing and using three kinds of omics data with different emphasis, and drug feature extraction is performed by using a drug molecular map, a drug 3D structure, a drug molecular fingerprint and a drug substructure relationship.

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.

The invention relates to the technical field of intelligent anesthesia precise regulation and control, and discloses an anesthetic dosage optimization method based on artificial intelligence. According to the method, a dynamic treatment interval is constructed, and the boundary of the dynamic treatment interval is adaptively adjusted according to the real-time sedation depth and the nociceptive stimulation level. And in the interval, performing pattern recognition on the continuous electroencephalogram signals and the hemodynamic parameters, and marking abnormal events deviating from a standard anesthesia state. And establishing a correlation network of the drug effect chamber concentration and the abnormal events, and generating a virtual drug response curve for predicting the trend of the abnormal events under different doses. Whether dose strategy reconstruction is started or not is determined by comparing the goodness of fit between the prediction curve and the actual physiological trajectory. During reconstruction, contribution weights of historical drug infusion points to abnormal events are backtracked and analyzed, and adjustment coefficients are distributed and integrated into a new infusion sequence. According to the invention, individualization and self-adaptive optimization of anesthesia administration are realized, and the accuracy and safety of anesthesia depth control are improved.

The invention provides a drug screening and curative effect evaluation method and system based on a glioma organ-like model, and relates to the technical field of biological material analys.The method includes the steps that a three-dimensional fluorescence image of the glioma organ-like model is collected, signal intensity analysis and compensation processing are carried out, a growth feature mapping model is constructed, and the three-dimensional fluorescence image of the glioma organ-like model is obtained; and phenotypic characteristics for representing the glioma organoid heterogeneity are obtained. Inputting the phenotypic characteristics into a drug response evaluation network, and calculating a drug sensitivity score; the drug molecular feature library and the drug collaborative analysis model are combined, the synergistic interaction index of different drug combinations is calculated, the optimal drug combination is screened, the drug administration dosage and the drug administration time sequence are determined in combination with phenotypic features, and finally an individualized treatment scheme is generated. According to the invention, the curative effect of the drug on the glioma organs can be effectively evaluated, and the optimal drug combination and administration scheme can be screened out, so that the accuracy and effectiveness of glioma treatment are improved.

The invention is applicable to the technical field of clinical medicine, provides a clinical multi-modal cancer drug response prediction method based on feature reconstruction, constructs a clinical multi-modal model for drug response prediction of diffuse large B-cell lymphoma, and aims to predict the drug response of diffuse large B-cell lymphoma by integrating gene sequencing and clinical multi-modal data. And accurate drug reaction prediction is realized. The model adopts an end-to-end multi-stage processing flow: firstly, extracting gene features through TransP-Net, and processing multi-modal clinical data by using a clinical information encoder; then, pseudo-gene features are generated through a clinical-genome filling module to deal with the data missing problem; and finally, multi-modal deep fusion is realized through a clinical information decoder, and a prediction result is output. According to the method, data characteristics and working processes in a real clinical environment are fully considered, two conditions of complete gene data and missing gene data can be processed at the same time, and the method has a good clinical transformation prospect and application value.

The invention relates to the technical field of medical health management, and discloses an endocrine patient health management system based on big data. According to the system, a multi-source data acquisition module is adopted to acquire heterogeneous health data including physiological monitoring indexes, medication record data, medical image reports and the like, and living habit logs can also be included. The physiological feature noise reduction module is used for extracting steady-state physiological features, the multi-modal fusion module is used for generating a focus dynamic map, and the medication feedback analysis module is used for generating a drug response mode evolution sequence. The data are input into a multi-dimensional decision model, and a health management scheme is output by a personalized scheme generation module. And the living habit analysis module analyzes the living habit logs to generate influence factors, and participates in making a health management scheme. The system realizes multi-source data fusion analysis, can provide a personalized health management scheme for an endocrine patient, improves the disease diagnosis accuracy and treatment effect, and improves the life quality of the patient.

The invention discloses a drug response prediction method based on a graph neural network and a convolutional neural network, and belongs to the technical field of drug response prediction, and the method comprises the following steps: S1, data preprocessing; s2, constructing a network; s3, performing network training; and S4, drug reaction prediction. According to the method, the convolutional neural network and the graph neural network are combined, the drug molecule graph data are added, and the structural features of drug molecules are introduced, so that the final regression prediction is more accurate; feature extraction is carried out on drug molecule fingerprint global features derived from the sequence by using a stacked one-dimensional convolution module, and neighborhood information is enhanced; through a designed information interaction fusion branch, information interaction between drug characteristics and cell line characteristics is realized, interaction between drug reaction information and drug smiles sequence information is also realized, and the prediction performance of the model is improved through an obtained new characteristic vector.

The invention discloses a self-crosslinking hydrogel with a tertiary amine group and preparation and application thereof, and belongs to the field of biomedical polymer materials, the self-crosslinking hydrogel comprises aminophenylboronic acid modified oxidized glucan, tertiary amine compound modified oxidized glucan and a solvent medium; the preparation method comprises the following steps: dissolving amino phenylboronic acid modified oxidized dextran and tertiary amine compound modified oxidized dextran in a solvent medium, mixing, standing for a certain time, and carrying out self-crosslinking to obtain the self-crosslinking hydrogel with tertiary amine groups. A cross-linking point (phenylboronic acid) of the gel network is a response unit of glucose. In addition, the hydrogel can effectively avoid uncontrollable drug release caused by non-responsive factors, and the response release efficiency of the drug is improved; by loading the hypoglycemic drug insulin to be applied to the treatment research process of diabetes, the occurrence of hypoglycemia can be avoided, and meanwhile, the possibility is provided for realizing intelligent controlled release of insulin through blood glucose response.

The invention provides an organoid drug reaction whole-course monitoring system, and relates to the technical field of data monitoring, and the system comprises an acquisition module which is used for acquiring a three-dimensional image sequence of an organoid at a set time interval, and monitoring the concentration data of metabolites in a culture solution to obtain multi-source monitoring data; the processing module is used for performing voxelization processing on the three-dimensional image sequence to form a time sequence three-dimensional voxel data set; and the calculation module is used for calculating barycentric coordinates of the organ-like structure based on the three-dimensional space distribution characteristics of the organ-like structure in the time sequence three-dimensional voxel data set, and establishing two mutually orthogonal space analysis axes in the maximum cross section of the organ-like structure by taking the barycentric coordinates as the center. According to the method, comprehensive and dynamic monitoring and evaluation of the organoid drug reaction process are realized through multi-source data collaborative acquisition, standardized spatial analysis, accurate form dynamic extraction, form and metabolism time sequence correlation modeling and real-time time sequence registration evaluation.

The invention provides a drug recommendation method and system fusing time sequence heterogeneous graph node information, and the method comprises the steps: collecting the drug use information of a current patient at each treatment time node, taking the patient and drugs as nodes, and taking the feedback of the patient to the drugs and the association information between the drugs as the edges between the nodes to construct a time sequence heterogeneous graph data set; performing feature extraction on the time sequence heterogeneous graph data set, and inputting an extraction result into a time perception graph neural network to obtain first drug response prediction probability distribution and second drug response probability distribution output by the time perception graph neural network; and generating a drug recommendation list based on the first drug response prediction probability distribution and the second drug response probability distribution. According to the method, the multi-source heterogeneous data including the electronic medical record of the patient, the medicine components and efficacy and the medicine interaction relationship are fused to realize medicine recommendation, and the accuracy of medicine recommendation is improved.

An ensemble of predictive models that elucidate how cancer mutations impact the response to common replication stress-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multi-drug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK (receptor tyrosine kinase)-JAK-STAT assembly governing resistance. This invention defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine.

The invention relates to the technical field of precision medical treatment, in particular to an interpretation system and method for tumor precision medication and a storage medium, and the system comprises a data collection module which is used for collecting gene data, clinical data and drug response data of tumor patients from a plurality of data sources; the data standardization processing module is used for carrying out formatting and standardization processing on the data, so that the data formats are consistent, and effective processing and analysis can be carried out in subsequent modules; the gene data analysis module is used for performing variation analysis according to the gene data of the patient, identifying a gene mutation site and generating a drug sensitivity analysis result; and the drug screening module is used for screening drugs matched with the gene variation sites of the patient from a drug database according to the gene data analysis result, and providing drug dosage recommendation. By adopting the technical scheme of combining a quantum optimization technology and gene data analysis, the technical effect of remarkably improving the drug screening and dose recommendation precision is achieved.

The invention discloses a dual-channel fusion cancer drug response prediction method, and belongs to the technical field of biological information. The method aims at solving the problems that an existing prediction method is only limited to intra-modal feature extraction, and a complex nonlinear cooperative relation between a drug and cancer cells is difficult to capture. Multi-omics data, drug molecular structure information and cancer cell line drug reaction data are integrated from databases such as CCLE, GDSC and PubChem, and unified input features are formed through standardization and feature construction. Introducing a hierarchical double-attention conversion network into the first channel to carry out characterization learning on the multi-modal features of the drug and the cell line, and constructing a high-order attention neighbor interaction graph convolutional network in the second channel to capture graph structure information of cancer drug response. And carrying out adaptive weighting on output results of the two channels by using a PPO-based fusion module so as to realize a dynamic optimal decision. And finally, generating a drug sensitivity prediction result of the cancer cell line through a classification predictor.

The present invention relates to an organ-on-a-chip with an inverted microweir structure as a cell co-culture platform and a use thereof. By using the multi-organs-on-chip provided by the present invention, it is possible to derive more accurate efficacy and toxicity results for cells according to drug treatment. In particular, when a drug is converted into a metabolite through liver metabolism in a liver-heart co-culture system, the resulting cardiotoxicity and drug responses across multiple organs can be predicted, and thus the chip can serve as an alternative to animal testing, significantly reducing the cost and time required for new drug development and drug screening. Additionally, the chip can be advantageously utilized in studies of cellular microenvironments and research on other organ-on-a-chip systems.

The invention provides a single-cell drug sensitivity prediction algorithm based on multi-modal reinforcement learning, and aims to solve the problem that the accuracy of a traditional drug sensitivity prediction method is limited. According to the system, multi-modal bioinformatics data including cell line gene expression data, proteome data, metabolome data, drug reaction data and single-cell RNA sequencing data are integrated, and feature extraction, fusion and selection are performed by using a deep reinforcement learning technology, so that high-precision prediction of single-cell drug sensitivity is realized. The algorithm mainly comprises five core modules: a multi-modal data integration and preprocessing module, a deep reinforcement learning feature extraction and fusion module, a dynamic feature selection and importance evaluation module, a cross-domain migration and adaptive learning module and a deep reinforcement learning prediction model and performance optimization module. The modules work cooperatively, potential features can be learned automatically from multi-modal data, a feature fusion strategy can be optimized, a key feature subset can be selected dynamically, and a model structure and parameters can be adjusted adaptively according to features of target domain data, so that prediction accuracy and stability are improved remarkably. Experimental results show that the algorithm shows significant advantages in multiple evaluation indexes, and compared with a traditional prediction method, the algorithm has higher prediction accuracy and wider applicability. Therefore, the invention provides a novel and efficient drug sensitivity prediction tool for the fields of personalized medical treatment and drug research and development, and has important practical application value.

Various methods and systems for efficiently providing pharmacogenetic guidelines is disclosed. A drug prescription and a patient genotype can be analyzed to provide a drug therapy recommendation. The analysis can include comparing the patient genotype and the drug name to a drug profile database, a molecular biomarker database, hospital drug reaction records and omics databases, and calculating the patient response to the drug.

Methods for facilitating drug discovery in various models and proposing single cell-type-specific drug candidates, and predicting cellular drug sensitivities. Predictions comprise obtaining bulk RNA sequence data, wherein bulk RNA seq data is pan-cancer cell line transcriptomic data from one or more sources; obtaining single cell RNA sequence data (scRNA-seq) from one or more sources; integrating the bulk RNA sequence data and scRNA-seq data; capturing similarities between the bulk data and the scRNA-seq data based on a shared drug response relative gene and using canonical correlation analysis; extracting one or more drug-response relevant features and selecting a pharmacogenomic subspace for accurately predicting a single-cell drug response.

Method and systems for sorting drug-responsive cell population based on single-cell RNA sequencing (scRNA-seq) data are disclosed. In some examples, the method includes: constructing a target gene regulatory network (GRN) for each of the cell populations based on scRNA-seq data of cell populations in a disease state; virtually knocking out targets in the GRN, and constructing a target-perturbed gene regulatory network (tpGRN); acquiring a low-dimensional representation of each of network nodes from the GRN in the GRN and the tpGRN through manifold alignment; calculating a Euclidean distance for each of the network nodes between the GRN and the tpGRN through a Euclidean distance; scoring a drug response of each of the cell populations by comprehensively considering changing trends of a drug target, a 2-hop node, and an edge of the 2-hop node in the GRN and the tpGRN, and determining a sorting result of the drug-responsive cell population.

The invention discloses a construction method of a model for predicting an esophageal squamous cell carcinoma intervention effect. The construction method comprises the following steps: acquiring detection data of a predictive factor in a sample; the predictive factors at least comprise the content or the expression level of a depletion marker of a functional CD8 T cell in a sample and the content or the expression level of a C1QC + macrophage immune checkpoint molecule; and predicting the intervention effect of the esophageal squamous cell carcinoma according to the detection data of the predictive factor. Establishing a space proteomics map of a complex space tissue of a tumor microenvironment (TME) during treatment; key spatial features which are related to specific cell behaviors and influence treatment results are determined and layered; a powerful prediction model of drug response is developed by utilizing a machine learning technology, and the area under curve (AUC) can reach 97%.

The invention provides a brain disease drug curative effect analysis and prediction system and method, and relates to the technical field of drug analysis. Multi-source heterogeneous data such as genetic variation spectrum, neural image topological characteristics, metabolic trajectory vectors and microbiome dynamic distribution matrix are integrated, and cross-modal dynamic fusion is realized through decomposition. The system constructs a drug prediction coupling network based on dynamic causal inference, analyzes overlapped potential intervention nodes of multiple disease mechanisms, and generates a disease-drug-phenotype multi-dimensional mapping library. A short-term drug response toxicity threshold is predicted through a metabolic entropy change model, a long-term neurological function degeneration trajectory is evaluated in combination with epigenetic drift, and an anti-fact strategy gradient is utilized to dynamically optimize a drug administration scheme. According to the invention, accurate fusion and dynamic analysis of multi-modal data are realized, a balance mechanism of curative effect and risk is established, and accurate drug combination sequence and administration strategy support can be provided for individualized treatment of complex brain diseases.

The invention discloses a drug response prediction model for drug combination based on multi-omics data and transfer learning and application, belongs to the technical field of drug response prediction, and solves the problems that a traditional method is lack of a drug response prediction system based on dosage, is mostly based on single drug response prediction, is lack of modeling ability for drug combination, and cannot predict drug response. Meanwhile, the cost is high, the period is long, and individual differences cannot be reflected. According to the prediction model, digital characteristic data, drug dosage and cell line multi-omics data of drugs are integrated, the prediction model adopts a combined transfer learning algorithm, parameters of a single drug response model are migrated into a drug combination model, a gene function module is introduced to enhance the interpretability of the model, and the prediction model can be applied to drug combination. Four cross validation strategies are adopted for evaluation of the prediction model, so that the drug response of drug combination of different candidate drugs is predicted, the method can be used for predicting the response of the drug combination, and the optimal drug combination and dosage are recommended.

The present disclosure provides an attention-based MIL model for use in extracting and characterising cell features, at both the cell level and the population level. To characterise cells, cells in a well are initially fed into the model. The cells in a well include a points cloud of cells and a point cloud of corresponding cell nuclei. The cells in the well are passed through a pretrained DFN encoder which extrapolates the cell and nuclei features from the cells in the well. These extracted features are then passed through a transformer based encoder to produced transformed versions of the extracted cell and nuclei features. These transformed features are then fed into at least two classifiers. One of the classifiers is a cell-level MLP classifier which is used to compare the cell features with known drug response to attempt to match a phenotype signature with a known drug response. One other classifier is the bag classifier which is used to compare the cell population features with that of known drug responses, again to attempt to match to a phenotype signature. The outputs of the cell classification and the bag classification are used to label the cells in the well with a drug label or a potential drug label / use case.

The invention relates to the technical field of knowledge maps, in particular to a veterinary treatment big data knowledge map construction method, which comprises the following steps of: acquiring animal case symptom characteristics, physical indexes, medical history records and intervention stage data, normalizing the symptom characteristics and encoding medical history to generate a case characteristic vector set; mapping symptoms and medicine nodes to establish a semantic relationship to calculate association strength, embedding physique and medical history to update node confidence to generate a personalized knowledge graph model, dynamically correcting edge weights in combination with feedback and medicine response, and extracting an effective intervention path to construct an association index to generate a veterinary treatment big data knowledge graph. According to the method, through normalization and sequential processing of multi-source case data, feature quantification and tracking are achieved, dynamic association is established based on semantic mapping and graph attention, confidence attenuation and an attribute weighting mechanism are fused, a node relation is optimized and self-adaptive evolution is carried out, individual difference and drug response capture is enhanced, and the updating performance of diagnosis and treatment knowledge is improved; and accurate and intelligent diagnosis and treatment analysis is promoted.

The invention discloses application of a reagent for detecting a predictive factor in preparation of a product for predicting an intervention effect of a patient with esophageal squamous cell carcinoma. The reagent for detecting the predictive factor comprises a reagent for detecting the content or expression level of a depletion marker of functional CD8 T cells in a sample and a reagent for detecting the content or expression level of immune checkpoint molecules of C1QC + macrophages in the sample. According to the method, key spatial features which are related to specific cell behaviors and influence treatment results are determined and layered; a powerful prediction model of drug response is developed by utilizing a machine learning technology, and the area under curve (AUC) can reach 97%.