-
公开(公告)号：KR100839600B1
公开(公告)日：2008-06-19
申请号：KR1020060121753
申请日：2006-12-04
Applicant: 한국전자통신연구원
Abstract: 1. 청구범위에 기재된 발명이 속한 기술분야
본 발명은 특이 결합부위 자동추출을 이용한 리간드 검색 장치 및 그 방법에 관한 것임.
2. 발명이 해결하려고 하는 기술적 과제
본 발명은 단백질 결합부위들 간의 서열과 함께 구조를 탐색하거나 단백질 결합부위들 간의 구조를 탐색한 후에 자동으로 특이결합부위를 추출하여 리간드를 검색함으로써, 선택성이 높은 약을 설계할 수 있고, 단백질 구조 기반 신약 설계 과정을 좀더 편리하고 신속하게 수행할 수 있도록 하기 위한, 특이 결합부위 자동추출을 이용한 리간드 검색 장치 및 그 방법을 제공하는데 그 목적이 있음.
3. 발명의 해결방법의 요지
본 발명은, 리간드 검색 장치에 있어서, 입력 데이터를 처리하기 위한 입력 데이터 처리부; 상기 입력 데이터 처리부로부터의 데이터에서 결합부위를 추출하기 위한 결합부위 추출부; 비교 방식을 사용자가 선택할 수 있도록 지원하기 위한 사용자 선택 입력부; 상기 사용자 선택 입력부의 선택 정보에 따라 목표 단백질의 결합부위와 데이터베이스의 단백질의 유사결합부위를 비교하여 추출하기 위한 유사결합부위 비교부; 및 상기 유사결합부위 비교부로부터의 특이결합부위에 대하여 가상 스크리닝을 수행하여 리간드를 검색하기 위한 리간드 검색부를 포함함.
4. 발명의 중요한 용도
본 발명은 구조 기반 신약 설계(Structure based drug design) 등에 이용됨.
리간드 검색, 특이 결합부위 자동추출, 유사결합부위 구조탐색-
公开(公告)号：KR100839580B1
公开(公告)日：2008-06-19
申请号：KR1020060123349
申请日：2006-12-06
Applicant: 한국전자통신연구원
IPC: G01N33/48
Abstract: 1. 청구범위에 기재된 발명이 속한 기술분야
본 발명은 3차원 상대적 방향각과 푸리에 디스크립터를 이용한 단백질 구조 비교 장치 및 그 방법에 관한 것임.
2. 발명이 해결하려고 하는 기술적 과제
본 발명은 3차원 상대적 방향각(3D RDA)과 푸리에 디스크립터(Fourier Descriptor)를 이용하여 단백질 구조의 특징을 기술하여 3차원 단백질 데이터베이스에서 질의 단백질과 유사한 단백질을 실시간으로 검색하기 위한 단백질 구조 비교 장치 및 그 방법을 제공하는데 그 목적이 있음.
3. 발명의 해결방법의 요지
본 발명은, 단백질 구조 비교 장치에 있어서, 외부로부터 입력되는 검색 대상 단백질 데이터 또는 질의 단백질 데이터에 대하여 3차원 상대적 방향각을 코딩하기 위한 3차원 상대적 방향각 코딩 수단; 상기 3차원 상대적 방향각 코딩 수단에서 코딩한 3차원 상대적 방향각 코딩값을 푸리에 변환하여 푸리에 계수를 구하기 위한 푸리에 변환 수단; 상기 푸리에 변환 수단에서 구한 검색 대상 단백질 데이터에 대한 푸리에 계수와 질의 단백질 데이터에 대한 푸리에 계수를 비교하기 위한 비교 수단; 및 상기 비교 수단의 비교 결과에 따라 유사한 순서대로 단백질 데이터를 출력하기 위한 출력 수단을 포함함.
4. 발명의 중요한 용도
본 발명은 유사 단백질 검색 시스템 등에 이용됨.
단백질 구조 비교, 3차원 상대적 방향각, 푸리에 디스크립터, 푸리에 변환, 푸리에 계수, 유사 단백질 실시간 검색-
公开(公告)号：KR1020080052295A
公开(公告)日：2008-06-11
申请号：KR1020070083454
申请日：2007-08-20
Applicant: 한국전자통신연구원
CPC classification number: C12Q1/6806 , G06F19/18
Abstract: An apparatus for detecting transcriptional regulation between genes is provided to improve accuracy of detection by using transcriptional factors which is biologically present between genes, form a network of biological information by predicting the transcriptional regulation between genes, and obtain various transcriptional regulations between genes through the network or map. An apparatus for detecting transcriptional regulation between genes comprises: a storage unit for storing regulator gene, at least one transcriptional factor corresponding to the regulator gene, and a profile(sequence information) of the transcriptional factor; an input unit(13) for inputting the information of a first gene and a second gene; search units(14,15) for searching the profile of the first gene information inputted in the storage unit; and a transcriptional regulation-detecting unit(16) for detecting transcriptional regulation of the first gene and second gene by performing the local sequence alignment by using the searched transcriptional factor profile and the nucleic acid sequence of the second gene.
Abstract translation: 提供了一种用于检测基因之间转录调控的装置，通过使用生物学上存在于基因之间的转录因子来提高检测的准确性，通过预测基因之间的转录调节形成生物信息网络，并通过网络获得基因之间的各种转录调控 或地图。 用于检测基因之间的转录调控的装置包括：用于存储调节基因的存储单元，与调节基因相对应的至少一个转录因子和转录因子的谱（序列信息）; 用于输入第一基因和第二基因的信息的输入单元（13）; 搜索单元（14,15），用于搜索在所述存储单元中输入的所述第一基因信息的简档; 以及转录调节检测单元（16），用于通过使用所搜索的转录因子谱和第二基因的核酸序列进行局部序列比对来检测第一基因和第二基因的转录调控。
-
公开(公告)号：KR1020080030138A
公开(公告)日：2008-04-04
申请号：KR1020060095817
申请日：2006-09-29
Applicant: 한국전자통신연구원
CPC classification number: G06F17/30616 , G06F17/30734
Abstract: A method and an apparatus of protein name normalization using ontology mapping are provided to recognize accurately the protein written on the literatures by mapping the protein name recognized in the biological literatures into a normalized protein ontology. A literature recognition part(110) extracts a protein name and species data by accepting biological literatures input. An abbreviated word dictionary DB(130) is composed of pairs of an abbreviated protein names and an original protein names. An abbreviated protein name restoration part(120) restores the abbreviated protein name into the original protein name. A synonym dictionary DB(150) is constructed through the ontology. An inverted index structure DB(160) of the synonym dictionary has an inverted index structure of the synonym dictionary. A protein code analysis part(140) analyzes the protein code by calculating similarity of the protein code by comparing the extracted protein name and the inverted index structure DB of the synonym dictionary. An ontology ID allocating part(190) allocates final ontology IDs by protein name based on the protein code and kind.
Abstract translation: 提供使用本体映射的蛋白质名称归一化的方法和装置，通过将生物学文献中识别的蛋白质名称映射成归一化的蛋白质本体来准确地识别文献中写的蛋白质。 文献识别部分（110）通过接受输入的生物文献来提取蛋白质名称和物种数据。 缩写词典DB（130）由缩写蛋白质名称和原始蛋白质名称的对组成。 缩写蛋白质名称恢复部分（120）将缩写蛋白质名称恢复为原始蛋白质名称。 同义词字典DB（150）是通过本体构建的。 同义字典的反向索引结构DB（160）具有同义字典的反向索引结构。 蛋白质代码分析部分（140）通过比较提取的蛋白质名称和同义词典的反向索引结构DB来计算蛋白质代码的相似性来分析蛋白质代码。 本体ID分配部分（190）基于蛋白质代码和种类，通过蛋白质名称分配最终本体ID。
-
公开(公告)号：KR100799541B1
公开(公告)日：2008-01-31
申请号：KR1020060121754
申请日：2006-12-04
Applicant: 한국전자통신연구원
Abstract: A device and a method for predicting feature of unknown protein are provided to predict the feature of the unknown protein in a PPI(Protein-Protein Interaction) network by utilizing a feature relation matrix using level normalization of GO(Gene Ontology) terms and PPI data. A GO term normalizer(21) normalizes a GO term level by calculating similarity of the GO terms found in an external PPI network(14). A feature relation matrix generator(22) generates the feature relation matrix by using the data received from the PPI network and the GO term normalizer. A Chi-square value calculator(23) calculates a Chi-square value by using the data received from the PPI network and the GO terminal level normalizer. A protein feature predictor(24) predicts the feature of the unknown protein by using the Chi-square value and the feature relation matrix.
Abstract translation: 提供了一种用于预测未知蛋白质特征的装置和方法，以通过使用GO（基因本体）术语和PPI数据的水平归一化的特征关系矩阵来预测PPI（蛋白质 - 蛋白质相互作用）网络中未知蛋白质的特征 。 GO术语归一化器（21）通过计算外部PPI网络（14）中发现的GO术语的相似性来对GO项目级别进行归一化。 特征关系矩阵生成器（22）通过使用从PPI网络和GO项目归一化器接收的数据来生成特征关系矩阵。 卡方值计算器（23）通过使用从PPI网络和GO终端级别归一化器接收的数据来计算卡方值。 蛋白质特征预测器（24）通过使用卡方值和特征关系矩阵预测未知蛋白质的特征。
-
Patent Agency Ranking
公开(公告)号：KR1020070060993A
公开(公告)日：2007-06-13
申请号：KR1020060024786
申请日：2006-03-17
Applicant: 한국전자통신연구원
Abstract: A method and a system for verifying protein-protein interaction with text mining are provided to avoid a duplicated experiment by utilizing knowledge proved through documents before the estimated protein-protein interaction is experimentally proved and generate a measure for estimating performance of an estimation system by verifying a result of the estimation system. An ontology database(160) stores protein-protein interaction and hierarchical structure information among proteins. A text mining part(120) extracts the protein-protein interaction from protein-related documents by using a text mining method. An ontology mapper(130) maps the extracted protein-protein interaction to an ontology ID by using the ontology database. An information filter(140) filters the information having high weight based on a frequency of the information and an effect factor of the protein-related documents among the mapped protein-protein interaction information. An information indexer indexes protein-related document/sentence, ontology ID, protein-protein interaction, and precision information.
Abstract translation: 提供了一种用于验证与文本挖掘的蛋白质 - 蛋白质相互作用的方法和系统，以在通过实验证明估计的蛋白质 - 蛋白质相互作用之前利用通过文献证明的知识来避免重复的实验，并通过验证来生成估计估计系统的性能的度量 是估算系统的结果。 本体数据库（160）在蛋白质之间存储蛋白质 - 蛋白质相互作用和层次结构信息。 文本挖掘部分（120）通过使用文本挖掘方法从蛋白质相关文档中提取蛋白质 - 蛋白质相互作用。 本体映射器（130）通过使用本体数据库将提取的蛋白质 - 蛋白质相互作用映射到本体ID。 信息过滤器（140）基于所述信息的频率和所映射的蛋白质 - 蛋白质相互作用信息之间的蛋白质相关文档的影响因子来过滤具有高权重的信息。 信息索引器索引蛋白质相关文档/句子，本体ID，蛋白质 - 蛋白质相互作用和精确度信息。
-
公开(公告)号：KR100463840B1
公开(公告)日：2004-12-29
申请号：KR1020020082340
申请日：2002-12-23
Applicant: 한국전자통신연구원
Abstract: PURPOSE: A method for automatically generating a template for constructing a protein interaction network is provided to reduce a network construction expense by automatically generating the basic template for the interaction network of an object protein based on the interaction network of the proteins, which are present in other species, similar to the object protein. CONSTITUTION: The proteins of a function similar to the object protein are searched from many species(S202). The interaction network of the searched proteins is generated based on a previously defined interaction relation database(S204). A similarity relation between the proteins existing in the generated interaction networks is set(S206). The network template of the object protein is generated by integrating the protein nodes between the different networks setting the similarity relation(S207).
Abstract translation: 目的：提供一种用于构建蛋白质相互作用网络的模板的自动生成方法，以通过基于蛋白质的相互作用网络自动生成目标蛋白质的相互作用网络的基本模板来降低网络构建费用，所述网络存在于 其他物种，与目标蛋白相似。 构成：从许多物种中搜索与目标蛋白质相似功能的蛋白质（S202）。 基于先前定义的交互关系数据库生成搜索到的蛋白质的交互网络（S204）。 设置生成的交互网络中存在的蛋白质之间的相似性关系（S206）。 目标蛋白质的网络模板是通过在设定相似性关系的不同网络之间集成蛋白质节点而生成的（S207）。
-
公开(公告)号：KR1020040065523A
公开(公告)日：2004-07-22
申请号：KR1020030095247
申请日：2003-12-23
Applicant: 한국전자통신연구원
CPC classification number: G06F17/30784 , G06F17/30858 , H04N21/23109 , H04N21/84
Abstract: PURPOSE: A nonlinear quantization and similarity matching method for retrieving a video sequence having plural image frames is provided to configure a bit expression of an edge histogram descriptor having reduced bits for a video sequence including plural image sets, and to retrieve the video sequence with extracted information from the coded expression, thereby reducing the number of bits. CONSTITUTION: A system selects one image frame of a video sequence as a target image frame(S110), and divides the selected frame into sub images(S111). The system extracts edge histograms from the sub images(S112), and decides whether the edge histograms are generated for all the sub images(S113). If so, the system increases a constant by 1 to select a next image frame of the video sequence(S115). The system decides whether all image frames are selected from the video sequence(S116). If so, the system generates a representative edge histogram bin as the first image descriptor(S117), and creates a quantization index value group(S118).
Abstract translation: 目的：提供一种用于检索具有多个图像帧的视频序列的非线性量化和相似性匹配方法，以配置包括多个图像集的视频序列的具有缩减比特的边缘直方图描述符的比特表达式，并且提取出视频序列 来自编码表达式的信息，从而减少位数。 构成：系统选择视频序列的一个图像帧作为目标图像帧（S110），并将所选择的帧划分为子图像（S111）。 该系统从子图像中提取边缘直方图（S112），并判定是否为所有子图像生成边缘直方图（S113）。 如果是这样，系统将常数增加1以选择视频序列的下一个图像帧（S115）。 系统决定是否从视频序列中选择所有图像帧（S116）。 如果是，则系统生成代表性边缘直方图单元作为第一图像描述符（S117），并创建量化索引值组（S118）。
-
公开(公告)号：KR1020040055893A
公开(公告)日：2004-06-30
申请号：KR1020020082340
申请日：2002-12-23
Applicant: 한국전자통신연구원
Abstract: PURPOSE: A method for automatically generating a template for constructing a protein interaction network is provided to reduce a network construction expense by automatically generating the basic template for the interaction network of an object protein based on the interaction network of the proteins, which are present in other species, similar to the object protein. CONSTITUTION: The proteins of a function similar to the object protein are searched from many species(S202). The interaction network of the searched proteins is generated based on a previously defined interaction relation database(S204). A similarity relation between the proteins existing in the generated interaction networks is set(S206). The network template of the object protein is generated by integrating the protein nodes between the different networks setting the similarity relation(S207).
Abstract translation: 目的：提供一种自动生成用于构建蛋白质相互作用网络的模板的方法，以通过基于蛋白质的相互作用网络自动生成对象蛋白质的相互作用网络的基本模板来降低网络构建费用，该模板存在于 其他物种，类似于对象蛋白质。 构成：从许多物种中搜索类似于对象蛋白的功能蛋白（S202）。 基于先前定义的交互关系数据库生成所搜索的蛋白质的相互作用网络（S204）。 设置生成的交互网络中存在的蛋白质之间的相似关系（S206）。 通过对设置相似关系的不同网络之间的蛋白质节点进行整合来生成对象蛋白质的网络模板（S207）。
-
公开(公告)号：KR1020040028658A
公开(公告)日：2004-04-03
申请号：KR1020037005476
申请日：2001-10-18
Applicant: 한국전자통신연구원
IPC: G06F17/30 , H04N21/232 , G06T5/40 , G06T1/00
CPC classification number: G06F17/30259 , H04N19/98
Abstract: PURPOSE: A non-linear quantization and similarity matching methods for retrieving image data is provided to construct a database to store image information representing a plurality of images with fewer bits, and to retrieve corresponding images in response to a query image based on a database with a high retrieval speed and accuracy. CONSTITUTION: L.times.5 number of normalized edge histogram bins are calculated to generate L number of edge histograms of a target image, wherein L is a positive integer and each edge histogram has five normalized edge histogram bins and represents a spatial distribution of five reference edges in a sub-image, wherein the reference edges include four directional edges and a non-directional edge(S101). The L.times.5 number of normalized edge histogram bins are non-linearly quantized to generate L.times.5 number of quantization index values for the target image(S103). The L.times.5 number of quantization index values are stored in the database(S105). And the steps S101 to S105 are repeated until all of the stored images are processed to construct the database having the image information(S107).
Abstract translation: 目的：提供用于检索图像数据的非线性量化和相似性匹配方法，以构建数据库以存储表示具有较少位的多个图像的图像信息，并且基于具有 检索速度快，准确度高。 规定：数量5的归一化边缘直方图区块被计算以生成目标图像的L个边缘直方图，其中L是正整数，并且每个边缘直方图具有五个标准化的边缘直方图区段，并且表示五个空间分布 参考边缘在子图像中，其中参考边缘包括四个方向边缘和非方向边缘（S101）。 第5次归一化边缘直方图区间被非线性量化，以产生目标图像的数量5个量化索引值（S103）。 将数量为5的量化索引值存储在数据库中（S105）。 并且重复步骤S101至S105，直到所有存储的图像被处理以构成具有图像信息的数据库（S107）。
-
-
-
-
-
-
-
-
-
Search Results
133
records
(took 0.033 seconds)
