33 results about "Drug-disease" patented technology

Given two heterogeneous entities, the prevalence of text data provides rich co-occurrence information for them. However, the co-occurrence only is noisy—not only may the co-occurrence just imply an accidental writing, but also it may just reflect the domain-specific common words. Only those strong relevance between entities supported by rich relevance contexts in data can indicate meaningful entity relationships. Strong relevance between heterogeneous entities are mined from their co-occurrences. Drug-disease therapeutic relationships are used as the example to demonstrate an application of this work.

An automated system for use by pharmacies, clinics or health care institutions which provides health information that comprises disease state information, the system having:a) a clinic computer having access to medical information containing a medication profile for the patient, andb) a remote computer having access to a drug-disease databaseThe disease state information being determined by a process where the clinic computer analyzes the medication information, creating a data packet comprising drug products being used, associating a unique identification number (UIDN) to the patient and data packet, and transmitting the UIDN and data packet to the remote computer. The remote computer then cross-referencing the drug products contained within said data packet with individual disease states and identifying disease state information that is relevant to be provided to a patient.

The present invention relates to a drug-disease relationship classification method based on a neural network, which belongs to the technical field of biomedical text mining and data mining, and solvesthe problem of more accurate and effective classification of drug-induced relationship among drug-induced diseases for the drug-induced entities labeled in biomedical literature. The method comprisesS1 constructing a drug-induced relationship candidate case set; 2 performing text processing on that biomedical literature; 3 constructing the domain knowledge; 4 constructing an input vector; 5 constructing a text-level semantic information sub-network model; S6 adopting an attention mechanism to form the final representation of knowledge; S7 constructing a drug-disease relationship classification model; S8 predicting drug-disease relationships in the biomedical literature. The method can automatically identify the entity relationship of drug diseases between sentences and within sentences effectively, and overcomes the existing methods that most systems utilize a large number of feature engineering methods based on the traditional machine learning methods.

Disclosed is a network-based drug efficacy-assessing method, and an apparatus. The method comprises: computing a score for association between drugs and diseases by means of at least one of drug-adjacency-based inference, disease-adjacency-based inference, and module-distance-based inference; building a classifier for determining whether the drug is associated with the disease, using machine learning in which the score is employed as a feature; and determining the association between the drug and the disease by use of the classifier. The method and apparatus can search for drug-disease relation in which a drug can exert its pharmaceutical efficacy on a disease on the basis of a network constructed from protein interaction databases and protein-gene association databases, and can evaluate the molecular interaction of the drug to find out new pharmaceutical effects of the drug with higher precision and sensitivity, whereby a time and cost for the development of new drugs can be reduced.

The invention discloses a drug repositioning method based on low-rank matrix completion. The method comprises the steps that firstly, relevant data of drugs and diseases is integrated to build a drug-disease heterogeneous network, wherein the elements in a matrix comprise drug pairs, disease pairs, known drug-disease pairs and unknown drug-disease pairs; secondly, the matrix is completed by usinga quick matrix completion algorithm, predicted values are assigned to the unknown drug-disease pairs, and according to the completed predicted values, new indications are predicted for all drugs. Themethod is simple and effective, and through the tests on a plurality of data sets and the comparison with other method, it is shown that the method has good predicting performance in drug repositioning.

The invention discloses a drug repositioning method for a COVID-19 treatment drug and application thereof. The method comprises the following steps: s1, collecting a COVID-19 diagnosis and treatment scheme report, and constructing a treatment scheme data set of known COVID-19 diseases; s2, expanding drug-disease-target point relationship data; s3, standardizing entity naming and constructing a network; s4, performing network representation and association degree calculation; and s5, sorting the potential therapeutic drugs. According to the method, the drug disease similarity and the similaritybetween the drug targets are considered, and meanwhile, the drug network structure similarity and the target network structure similarity are combined, so that the prediction accuracy of the drug target interaction relationship is improved. Potential drugs for treating COVID-19 diseases are mined on the basis of the constructed COVID-19 data set, and the potential drugs contributing to COVID-19 treatment in the drugs on the market can be effectively found.

The invention belongs to the field of biomedicine, discloses a method for analyzing an anti-glandular cystitis action mechanism of pachymaran based on network pharmacology, and reports detailed targets and specific pharmacological mechanisms of pachymaran for preventing and treating glandular cystitis for the first time. The method comprises the following main points: obtaining a pachymaran pharmacological target and a glandular cystitis pathogen target through online database analysis of TCMSP, DisGeNET and the like; taking an intersection of the pachymaran and the glandular cystitis target to obtain a drug disease intersection target gene target, and constructing a related protein interaction network to screen a core target; further performing gene ontology GO biological process and KEGGpathway enrichment analysis on the core target by utilizing the R language related packet; and finally, constructing a drug target gene ontology functional pathway disease visualization diagram for deep analysis of a treatment mechanism. The method provided by the invention provides a new idea for explaining a research mechanism of pachymaran for exerting glandular cystitis resistance, and also provides an early-stage research basis for clinical application of pachymaran in glandular cystitis.

The invention discloses a drug-disease relation identification method, system and device. The method includes the following steps that: a drug-disease relation two-dimensional matrix and / or grayscaleimage corresponding to a drug-disease relation to be identified is acquired; and the drug-disease relation two-dimensional matrix and / or the grayscale image is inputted into a convolutional neural network for processing, and an identification result is obtained. The system includes an acquisition module which is used for acquiring a drug-disease relation two-dimensional matrix and / or grayscale image and a processing module which is used for inputting the drug-disease relation two-dimensional matrix and / or grayscale image into the convolutional neural network for processing so as to output an identification result. The device includes a memory that stores at least one program and a processor that executes the at least one program. According to the drug-disease relation identification method, system and device of the present invention, the processing function of the convolutional neural network is utilized, and therefore, a drug-disease treatment relationship can be identified quickly and efficiently, potential drug-disease interaction effects can be identified, and lead compound identification and drug relocation research can be conducted. The identification method, system and device of the present invention are widely applied to the computer aided drug design field.

The invention provides a functional network characteristic analysis method based on breast cancer diseases. The method comprises the following steps: step one, integrating various relationship networks including a drug-disease network, a drug-drug network, a drug-target network, and a target-target network and carrying out a series of network topology structure analyses on the networks; step two,on the basis of the e topological structure relation of breast cancer functional networks, digging 11 kinds of pieces of characteristic information capable of describing the drug-target mutual interaction relationship; and step three developing a related breast cancer functional network feature extraction tool. According to the invention, a functional network analysis method for characteristics ofbreast cancer diseases is established to solve a problem of relocation of the drugs; and the potential targets and new drug uses are predicted by using the method, so that the good prediction effectis obtained.

A computer implemented system and method for matching drugs and diseases and generating a weighted relationship between the matched drugs and diseases. The system stores a quantity of patient medical history billing records identifiable as patient prescription claim records and medical claim records. The system also stores a grouping of drug codes and groupings of diagnosis codes. The system is configured to match drugs identified in a specific patient's prescription claim records with diseases identified in the patient's medical claims. The system generates a weighted relationship between the matched drugs and diseases by calculating a link weight in accordance with a preprogrammed formula for each occurrence of the diagnosis codes identified in the patient's medical claims that is identified as associated with the disease identified and the drug upon which the query is being processed. The link weight provides a statistical match association value to each of the matched diseases.

The present invention provides a drug product for the prevention, improvement in conditions relating to, and / or treatment of diabetes, comprising beta(1->3) glucan derived from vegetable material and having a molecular weight of from about 5,000 to about 20,000. The drug product of the present invention can be widely employed as a diabetes drug for both IDDM and NIDDM, exhibits almost no side effects and does not have an obese effect even when orally administered, can be widely employed as a medical drug for patients in various states of morbidity, and can be employed by patients, generally healthy persons, and other animals in the form of food and drink products such as therapeutic foods and health foods. The present invention provides a method of preventing, improving conditions relating to, and / or treating diabetes comprising administering the above-specified beta(1->3) glucan.

The invention discloses compound pig feed for improving the immunity and promoting growth, and belongs to the technical field of feed. The compound pig feed is prepared from the following components in parts by weight: 30-55 parts of corn flour, 30-55 parts of potato powder, 20-45 parts of sorghum flour, 5-20 parts of bran, 2-12 parts of cavings, 1-5 parts of high-quality fish meal, 20-40 parts ofgrass powder, 1-5 parts of vitamin complex and 5-20 parts of traditional Chinese herbal medicinal additives. The invention further provides a method for preparing the compound pig feed. The feed is reasonable in composition, comprehensive in nutrition, is favorable to disease prevention and immunity enhancement for pigs due to traditional Chinese herbal medicinal powder is added, hardly has residue, drug disease, toxic effect or side effect, and is safe and reliable; and the feed is purely natural and pollution-free and can be used for improving the digesting and absorbing ability and promoting growth, nutrition can be comprehensively absorbed by pigs during the growing period, the growth of pigs can be promoted, and the immunity of pigs can be effectively improved.

The invention discloses a drug new use prediction method based on multi-similarity fusion. The drug new use prediction method comprises the steps of calculating drug similarity by utilizing drug chemical structure data; calculating the drug similarity by using the drug target protein data; calculating the drug similarity by using the drug side effect data; fusing the calculated drug similarities to obtain a fused drug similarity; calculating a drug disease association prediction value by using the fused drug similarity; calculating disease similarity by utilizing the drug disease data, and calculating a drug disease association prediction value based on the disease similarity; and fusing the calculated drug disease association prediction values to obtain a fused drug disease association prediction value. According to the method, weighted summation is carried out on the drug disease correlation prediction value obtained based on drug similarity calculation and the drug disease correlation prediction value obtained based on disease similarity calculation, the prediction value of the fused drug to the disease is obtained, and the reliability and accuracy of the prediction value are improved.

The invention discloses a drug screening method and device and a storage medium. The method comprises the following steps: acquiring a plurality of drugs corresponding to a to-be-detected virus; for each drug in a plurality of drugs, inputting drug molecules of the drug and the base number sequence of the virus to be detected into a trained drug screening model, and determining an effective value corresponding to the drug through the drug screening model; according to the effective value, determining a target drug corresponding to the to-be-detected virus, and wherein the target drug is one or more of a plurality of drugs. According to the method, the trained drug screening model is screened based on deep learning of the drug-virus pair set, so that the operation performance of the drug screening model can be improved, the drug screening period is greatly shortened, the research and development period of drugs aiming at virus infection diseases is shortened, and the drug screening efficiency is improved.

The invention discloses a traditional Chinese medicine formula for relieving hypertension and relates to the technical field of traditional Chinese medicine formulae. The traditional Chinese medicineformula is characterized by consisting of the following raw materials in parts by weight: 20 parts of American ginseng, 10 parts of pine needles, 10 parts of radix codonopsis pilosula, 10 parts of radix ophiopogonis and 5 parts of Chinese magnoliavine fruits. The traditional Chinese medicine formula has the beneficial effects that the traditional Chinese medicine formula is adopted, respective effects of the traditional Chinese medicine components are utilized and matched with one another, and blood pressure of a patient suffering from hypertension can be effectively controlled; the medicine can be taken by the patient for a long time, is good in security and free of toxic or side effect; and for a patient suffering from sudden blood pressure increase, a hypotensive drug can be additionally taken to reduce the blood pressure rapidly, and the medicine can be taken to keep the blood pressure stable in long-term treatment, the blood pressure can be kept at a normal level, and complications caused by unstable blood pressure can be retarded.

The invention relates to an application of terramycin in treatment of acute gastroenteritis of bamboo rats, and belongs to the technical field of drug disease prevention of bamboo rats. The application is characterized in that in the case of a pathogenetic condition, 0.125g of flaky terramycin is crushed and is mixed with 3.125g of feed, the mixture is fed to each rat at each time for twice a day, and the dosage is continuously given for three days. Or, veterinary oxytetracycline hydrochloride solution is injected in leg muscle once a day, 0.4ml of veterinary oxytetracycline hydrochloride solution is injected at each time, and the bamboo rats can recover after being continuously injected for two days. The application provided by the invention has the beneficial effects that the terramycin is good in effect of treating acute gastroenteritis of bamboo rats and quick becomes effective.

The invention discloses a drug disease correlation analysis method based on time factors, and belongs to the technical field of application of complex networks in disease analysis. The drug disease correlation analysis method based on the time factor comprises: firstly, taking the number of the patients as the weight to measure the correlation between the drug and the disease, and then obtaining the positive and negative correlation between the drug and the disease by analyzing the drug use time and the diagnosis time of the patients. The drug disease correlation analysis method based on the time factor can make up for the defects in disease and drug correlation research, provides a direction for experimental research of research after drugs come into the market, and has good popularization and application value.

The invention provides a drug disease association prediction method and device, electronic equipment and a readable storage medium. The drug and disease association prediction method comprises the following steps: acquiring first drug embedding between each first type of node and a drug node, and acquiring first disease embedding between each second type of node and a disease node; obtaining second drug embedding according to the weight of the first drug embedding and the fusion feature of the first drug embedding, and obtaining second disease embedding according to the weight of the first disease embedding and the fusion feature of the first disease embedding; updating the second drug embedding according to a preset drug and disease association relationship to obtain third drug embedding, and updating the second disease embedding according to the preset drug and disease association relationship to obtain third disease embedding; and generating an association prediction result of the target drug corresponding to the drug node for the target disease corresponding to the disease node according to the third drug embedding and the third disease embedding. According to the embodiment of the invention, the processing speed of drug disease relevance prediction can be improved.

The invention relates to a drug disease matching database. The database comprises a drug information database, a matching database, a disease information database and an interaction module, wherein the drug information database is used for storing all drug information data; the matching database is used for storing related data of the drug treatment diseases; the disease information database is used for storing information data of all diseases, including symptoms and complications corresponding to the diseases, names and aliases of the diseases and the like, the matching database is in communication connection with the medicine information database and the disease information database, and the matching database is further in communication connection with an interaction module; the interaction module inputs disease information of a medicine needing to be searched, the disease information can be symptoms or confirmed specific disease names, and the matching database confirms a to-be-analyzed disease according to data stored in the disease information database. After the disease is confirmed, the medicine information related to the disease to be analyzed is called from the medicine information database, and a treatment scheme is generated.

The present invention relates to NS5A inhibitors, to novel pharmaceutical composition, antiviral medicament, method for prophylaxis and treatment of viral diseases, in particular caused by hepatitis C (HCV) virus and hepatitis GBV-C virus.Alkyl [(S)-1-((S)-2-{5-[4-(4-{2-[(S)-1-((S)-2-methoxycarbonylamino-3-methyl-butyryl)-pyrrolidin-2-yl]-3 H-imidazol-4-yl}-buta-1,3-diynyl)-phenyl]-1H-imidazol-2-yl}-pyrrolidine-1-carbonyl)-2-methyl-propyl]-carbamate naphthaline-1,5-disulfonate of the general formula 1, possibly in crystalline or polycrystalline form, have been proposed,wherein: R is C1-C3 alkyl.

The invention belongs to the field of biomedicine, and discloses a luteolin-autism related action gene target and a screening method thereof. According to the invention, a luteolin pharmacological target and an autism related target are obtained through online database analysis such as TCMSP, DisGeNET and the like; taking an intersection of luteolin and autism target spots to obtain a drug disease intersection target gene target spot, and constructing a related protein interaction network to screen a core target spot; further performing gene ontology GO biological process and KEGG pathway enrichment analysis on the core target; the range of new drug research and development is narrowed, the research and development purpose is accurate, and the drug research and development cost is saved; meanwhile, the medicine screening and predicting efficiency is greatly improved, and breakthrough significance is achieved for development and industrialization of rich traditional Chinese medicine resources in China.

The invention provides a functional network characteristic analysis method based on breast cancer diseases. The method comprises the following steps: step one, integrating various relationship networks including a drug-disease network, a drug-drug network, a drug-target network, and a target-target network and carrying out a series of network topology structure analyses on the networks; step two,on the basis of the e topological structure relation of breast cancer functional networks, digging 11 kinds of pieces of characteristic information capable of describing the drug-target mutual interaction relationship; and step three developing a related breast cancer functional network feature extraction tool. According to the invention, a functional network analysis method for characteristics ofbreast cancer diseases is established to solve a problem of relocation of the drugs; and the potential targets and new drug uses are predicted by using the method, so that the good prediction effectis obtained.

According to the medicine disease matching method based on the database, a disease name of the treatment medicine needing to be obtained is input, the matching database matches the corresponding disease data from the disease information database according to the disease name, and the medicine data corresponding to the disease data is obtained; the matching database acquires the required therapeutic drugs from the drug information database according to the drug data and generates the therapeutic scheme, and the side effects of the drugs and complications of diseases need to be considered in theprocess of generating the therapeutic scheme, so that the therapeutic scheme is more practical.