9 results about "Gene model" patented technology

The application belongs to the technical field of biological gene engineering, and particularly relates to a barley HvCslf6 promoter element and application thereof in regulating the content of barley grain beta-glucan, wherein the barley HvCslf6 promoter element is located at 295bp-300bp upstream of a transcription start site of a barley HvCslf6 gene; the nucleotide sequence of the HvCslf6 promoter element is CCGTTG, and the HvCslf6 promoter element belongs to a CCAAT-box element; the gene number of the HvCslf6 gene in BARLEX Morex v3 Gene Models is HORVU.MOREX.r3.7HG0698110. The application finds a key element which can affect the content of beta-glucan in barley grains in the upstream promoter region of a main effect gene HvCslf6 of barley beta-glucan synthesis by using a gene editing technology, and thus provides a method capable of preparing transgenic plants with changed content of beta-glucan in barley grains, and provides a new means and tool for detecting and regulating the content of beta-glucan in barley grains.

The invention belongs to the technical field of molecular breeding, and particularly relates to application of an E1-SOC1 gene model in cultivation of wide-adaptability soybean varieties. Research finds that three genes (E1 and homologous genes E1La and E1Lb) of an E1 family and two genes (SOC1a and SOC1b) of an SOC1 family are knocked out from soybeans at the same time by utilizing a gene editing technology, and the obtained homozygous five mutants show wide adaptability when planted under field conditions of different latitude regions; and the method has certain wide adaptability in the aspects of plant height, section number, flowering period, mature period and yield. It is indicated that the E1-SOC1 gene module (E1, E1La, E1Lb, SOC1a and SOC1b genes) is expected to be applied to molecular design breeding for breeding wide-adaptability soybean models, and has important application prospects in soybean molecular breeding research.

The application relates to a method for predicting gene mutation based on deep learning using multi-modal slices, image block division is performed on a digestive tract HE slice, and based on the image block division result, each image block of the digestive tract HE slice is classified into a cancerous region; the digestive tract HE slice and an immunohistochemical slice are registered; based on the digestive tract HE slice and the registered immunohistochemical slice, a gene mutation prediction training sample set is constructed, a multi-instance learning model is trained, the training standard is whether a gene is mutated, and a gene mutation prediction model is obtained; gene mutation prediction is performed according to the obtained gene mutation prediction model, a model for predicting gene mutation in combination with multi-modal pathological data of a patient is used, high specificity of the model is ensured, compared with a pathological gene prediction model using single modal data, more modal data is introduced, and the prediction accuracy is effectively improved.

The invention discloses an acute myelogenous leukemia disulfide death gene related prognosis risk model construction method, which comprises the following steps: constructing a disulfide death gene related prognosis feature preliminary model: taking a TCGA-LAML data set as a training set, taking GSE12417 and GSE71014 queues as a verification set, combining a plurality of known disulfide death genes, and constructing the disulfide death gene related prognosis feature preliminary model; and determining a new disulfide death gene data set. And based on a stacking strategy, fitting the preliminary model of the prognosis characteristics related to the disulfide death with a plurality of gene models to obtain an expanded stacking model, and taking the expanded stacking model as a final prognosis model related to the disulfide death of the acute myelogenous leukemia patient. And in combination with a process strategy of data input-feature screening-machine learning training-risk output, the constructed double-sulfur death gene related prognosis features are expanded, and the prediction efficiency of the model is improved.

This invention discloses a method for identifying the effectiveness of Cas targets using a large gene model combined with machine learning (ML), belonging to the fields of biotechnology and artificial intelligence. The method includes: constructing a detection array of A types of joint sequences and target joint sequences, the detection array having A detection units; adding one joint sequence to each detection unit, performing an incubation reaction, acquiring the fluorescence value of each detection unit and normalizing it; extracting key features from each joint sequence using a feature extraction model; constructing a tag set and a training set, constructing an ensemble model and training it using the tag set and training set; combining the feature extraction model and the trained ensemble model to form a machine learning model; for new target sequences and PAM sequences, combining them into a joint sequence to be analyzed, inputting it into the machine learning model to obtain a predicted fluorescence value, and judging the Cas protein's ability to recognize the joint sequence based on the fluorescence value. This invention can assist in targeted therapy detection.

The invention relates to an analysis method for mouse retinal ganglion cell morphological classification, which realizes single-cell-level accurate analysis and high-throughput integration: carrying out whole-cell patch post-clamp dendritic structure reconstruction on retinal ganglion cells, objectively quantifying parameters such as dendritic area, layering depth, dendritic asymmetry and the like, and analyzing the morphological classification of the mouse retinal ganglion cells. The subjective experience dependence of traditional Golgi staining and the resolution limitation of immunohistochemistry are thoroughly eliminated; a hierarchical classification rule is established through an automatic algorithm, manual interpretation deviation is eliminated, and the classification standardization degree is improved; the method breaks through the low-flux bottleneck of a genetic marker technology, does not need to depend on time-consuming gene model construction, synchronously analyzes the morphological diversity of cells through single cell sequencing and morphological reconstruction, and reveals that cells with similar dendritic layering positions can present functional differentiation due to transcriptome differences. The morphological analysis system established by the invention has single cell precision, high-throughput coverage and multi-dimensional relevance, and provides technical support for systematically analyzing the morphological development mechanism and subtype specificity of RGCs.

The invention belongs to the technical field of biological medicines, and particularly relates to an oral microorganism model for predicting whether an esophageal cancer patient progresses six months after radiotherapy and application of the oral microorganism model. The invention provides an oral cavity microbial model for predicting whether an esophageal cancer patient progresses six months after radiotherapy, the oral cavity microbial model is composed of 13 microbial genes shown as SEQ ID NO: 1-13, and the microbial genes are enriched in the oral cavity of a human body. The microbial gene model disclosed by the invention realizes good distinguishing ability on whether the esophageal cancer patient develops six months after radiotherapy, and the feasibility, applicability and universality of the microbial gene distinguishing model in the esophageal cancer patient are proved.

The application is particularly a control method of an Internet of Things gateway fusing communication and AI computing power, and relates to the technical field of Internet of Things gateway control, comprising: constructing a machine-understandable structured function identity file and a dynamic digital gene model for a physical device; analyzing structured service requirements and fusing multi-source context information to generate a device and network joint execution strategy meeting QoS requirements through an AI decision model; and dynamically creating a virtual communication end point based on the execution strategy.In the application, the device structured capability declaration and the dynamic digital gene model realize the transformation of the device from passive response to active capability declaration; the device automatically pushes a standardized description file containing function fingerprints, capability vectors and the like after being powered on, so that the gateway can complete identification and adaptation without pre-configuration; in cooperation with the transparent adaptation characteristics of the virtual communication end point, a standard interface is simulated on the cloud and a local agent is simulated for the device, so that the device can be accessed without modifying the firmware.

A construction method of a gene model for identifying a new subtype of hepatocellular carcinoma, and the use thereof. The method comprises: comparing transcriptome data of multiple patient samples identified pathologically as hepatocellular carcinoma, clustering the data, then selecting a subtype which cannot be fully mapped to a TCGA LIHC sample and designating the subtype as an HCC_1 subtype, obtaining a differentially expressed gene between the HCC_1 subtype and other subtype samples, and constructing, on the basis of the differentially expressed gene, a gene model that identifies the new subtype of hepatocellular carcinoma, wherein the higher the score outputted by the genetic model, the greater the probability of the hepatocellular carcinoma belonging to the HCC_1 subtype and the worse the prognosis.