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Arno Lukas,Bernd Mayer
The IIOAB Journal , 2010,
Abstract: Omics has massively permeated translational clinical research with numerous diseases being covered by Omics studies from the genome to the metabolome level. Integrating these disease specific Omics tracks appears a logical next step for building the fundament of Systems Biology and Systems Medicine. Here, coherence of individual Omics tracks regarding clinical hypothesis, samples and clinical descriptors, and finally data handling and integration become pivotal. We present a data integration, annotation and relations modeling concept for heterogeneous Omics data and workflows. With molecular features at the center of all Omics we link the result profiles from different Omics tracks characterizing a specific disease phenotype to a common human molecular reference network for allowing a seamless integration and subsequent support in interpretation of Omics screening results. Our concept rests on data structures for representing objects specified by metadata and content. For handling diverse Omics tracks a flexible structure for content is proposed allowing data representation at different levels of granularity as demanded by the type of Omics and specific type of data. Content on the molecular level includes deep annotation of molecular features on gene and protein level. Based on this annotation pair-wise relations between molecular objects are built, traversing the molecular annotation into a network of relations (molecular feature graph). Such a relation network is also built on the Omics data level, combining explicit relations derived from study setup and implicit relations generated by mining metadata and content (Omics data graph). Finally both graphs are merged utilizing the molecular feature level as common denominator, enabling a persistent integration and subsequently interpretation of Omics profiling results in the realm of a given clinical hypothesis. We present a case study on integrating transcriptomics and proteomics data on chronic kidney disease for demonstrating the feasibility of this concept.
Image Annotation Refinement Using Dynamic Weighted Voting Based on Mutual Information  [cached]
Haiyu Song,Xiongfei Li,Pengjie Wang
Journal of Software , 2011, DOI: 10.4304/jsw.6.11.2239-2246
Abstract: Automatic image annotation is a promising solution to narrow the semantic gap between low-level content and high-level semantic concept, which has been an active research area in the fields of image retrieval, pattern recognition, and machine learning. However, even the most dedicated annotation algorithms are often unsatisfactory. Image annotation refinement has attracted much more attention recently. In this paper, a novel refinement algorithm using dynamic voting based on mutual information is proposed. Unlike the traditional refinement algorithm, the proposed algorithm adopts dynamic weighted voting to measure the dependence between the candidate annotations, which not only permits that the annotations with higher probabilities deny the annotations with lower probabilities, but also permits that the annotations with lower probabilities deny the annotations with higher probabilities. The proposed refinement algorithm adopts progressive method instead of iterative, which can significantly decrease the time cost of refining annotations. In order to further improve efficiency without sacrificing precision, we propose the block-based normalized cut algorithm to segment image. Experiments conducted on standard Washington Ground Truth Image Database demonstrate the effectiveness and efficiency of our proposed approach for refining image annotations.
Image annotation refinement based on a random dot product graph

Sun Dengdi,Luo Bin,Guo Yutang,

中国图象图形学报 , 2012,
Abstract: In order to overcome the semantic gap between low-level features and high-level semantic concepts of imagery, a new image annotation refinement approach based on Random Dot Product Graph (RDPG)is proposed. In our approach, the visual features of images are used to construct a semantic graph of the candidate annotations. Then, we reconstruct the semantic graph with a RDPG, find the unobserved relevance in the incompletely observed semantic graph, and transform the random graph into the probabilities of state transition. Combined with Random Walk with Restart (RWR), the final annotations are chosen. This new method incorporates the visual and semantic information of images, and reduces the influence of the scale of database. Experiments conducted on three standard databases demonstrate that our approach outperforms the existing image annotation refinement techniques. The macro F-Score and micro average F-Score can reach 0.784 and 0.743 respectively.
Automatic image annotation refinement based on keyword co-occurrence and WordNet

KE Xiao,LI Dong-yan,CHEN Guo-long,
柯 逍

计算机应用研究 , 2012,
Abstract: Image automatic annotation is a significant and challenging problem in pattern recognition and computer vision areas. At present, most existing image annotation models are influenced by semantic gap problem. This paper proposed a new image automatic annotation refinement method based on keyword co-occurrence to overcome above problem, which used the correlations between keywords in dataset to improve image annotation result. However, above method did not reflect the generalized knowledge of people and easy influenced by the size of dataset. Aiming at above problem, it proposed a new image automatic annotation refinement method based on semantic similarity to overcome above problem. This method used semantic dictionary WordNet to calculate the correlations between keywords and improve the image annotation results. Experimental results conduct on Corel 5K datasets verify the effectiveness of proposed image annotation method. The proposed automatic image annotation model improves the annotation results on all evaluation methods.
Functional Genome Annotation by Combined Analysis across Microarray Studies of Trypanosoma brucei  [PDF]
Hamed Shateri Najafabadi,Reza Salavati
PLOS Neglected Tropical Diseases , 2010, DOI: 10.1371/journal.pntd.0000810
Abstract: Background Functional annotation of trypanosomatid genomes has been a daunting task due to the low similarity of their genes with annotated genes of other organisms. Three recent studies have provided gene expression profiles in several different conditions and life stages for one of the main disease-causing trypanosomatids, Trypanosoma brucei. These data can be used to study the gene functions and regulatory mechanisms in this organism. Methodology/Principal Findings Combining the data from three different microarray studies of T. brucei, we show that functional linkages among T. brucei genes can be identified based on gene coexpression, leading to a powerful approach for gene function prediction. These predictions can be further improved by considering the expression profiles of orthologous genes from other trypanosomatids. Furthermore, gene expression profiles can be used to discover potential regulatory elements within 3′ untranslated regions. Conclusions/Significance These results suggest that although trypanosomatids do not regulate genes at transcription level, trypanosomatid genes with related functions are coregulated post-transcriptionally via modulation of mRNA stability, implying the presence of complex regulatory networks in these organisms. Our analysis highlights the demand for a thorough transcript profiling of T. brucei genome in parallel with other trypanosomatid genomes, which can provide a powerful means to improve their functional annotation.
Image annotation refinement based on contextual graph diffusion

, LIU Zhuoxuan, SHANG Fuhua, SHEN Xukun, WANG Mei, WANG Haochang

- , 2016, DOI: 10.6040/j.issn.1672-3961.1.2015.316
Abstract: 摘要: 提出一种图像标注改善方法,利用数据集蕴含的语境相关信息进行标注改善。构建标签相关图和视觉内容相关图,利用正则化框架将标注改善问题描述为两个无向加权图上的损失函数最小化问题。采用数据分割,逐次优化和放松约束的策略,获得该问题的近似解。该方法充分利用标签的语境相关信息和图像内容相关信息,对数据集分割的粒度具有较好的鲁棒性,具备近似线性的时间复杂度。测试结果表明,该方法适用于大规模数据集,性能优于其它对比方法,可以较大幅度的提升图像标注性能。
Abstract: A new image annotation refinement method was proposed. The initial labels were firstly obtained by annotation methods. Then label relevant graph and visual relevant graph were constructed and mutually reinforced. The semantic optimization problem was formulated into a regularized framework on above undirected weighted graphs. With strategies like data partitioning, successive optimization and constraint relaxation, an approximate optimized solution was got. The refined result could be more related to the content of images by incorporating both visual content and contextual information. Moreover, the proposed method was robust with the partition granularity, and the complexity was approximately linear. Experimental results on large scale web image dataset showed that the proposed method outperformed others, and could achieve both efficiency and capability
MODMatcher: Multi-Omics Data Matcher for Integrative Genomic Analysis  [PDF]
Seungyeul Yoo,Tao Huang,Joshua D. Campbell,Eunjee Lee,Zhidong Tu,Mark W. Geraci,Charles A. Powell,Eric E. Schadt,Avrum Spira,Jun Zhu
PLOS Computational Biology , 2014, DOI: doi/10.1371/journal.pcbi.1003790
Abstract: Errors in sample annotation or labeling often occur in large-scale genetic or genomic studies and are difficult to avoid completely during data generation and management. For integrative genomic studies, it is critical to identify and correct these errors. Different types of genetic and genomic data are inter-connected by cis-regulations. On that basis, we developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors in multiple types of molecular data, which can be used in further integrative analysis. Our results indicate that inspection of sample annotation and labeling error is an indispensable data quality assurance step. Applied to a large lung genomic study, MODMatcher increased statistically significant genetic associations and genomic correlations by more than two-fold. In a simulation study, MODMatcher provided more robust results by using three types of omics data than two types of omics data. We further demonstrate that MODMatcher can be broadly applied to large genomic data sets containing multiple types of omics data, such as The Cancer Genome Atlas (TCGA) data sets.
Techniques for integrating -omics data  [cached]
Siva Prasad Akula,Raghava Naidu Miriyala,Hanuman Thota,Allam Appa Rao
Bioinformation , 2009,
Abstract: The challenge for -omics research is to tackle the problem of fragmentation of knowledge by integrating several sources of heterogeneous information into a coherent entity. It is widely recognized that successful data integration is one of the keys to improve productivity for stored data. Through proper data integration tools and algorithms, researchers may correlate relationships that enable them to make better and faster decisions. The need for data integration is essential for present -omics community, because -omics data is currently spread world wide in wide variety of formats. These formats can be integrated and migrated across platforms through different techniques and one of the important techniques often used is XML. XML is used to provide a document markup language that is easier to learn, retrieve, store and transmit. It is semantically richer than HTML. Here, we describe bio warehousing, database federation, controlled vocabularies and highlighting the XML application to store, migrate and validate -omics data.
The Gene Ontology's Reference Genome Project: A Unified Framework for Functional Annotation across Species  [PDF]
The Reference Genome Group of the Gene Ontology Consortium
PLOS Computational Biology , 2009, DOI: 10.1371/journal.pcbi.1000431
Abstract: The Gene Ontology (GO) is a collaborative effort that provides structured vocabularies for annotating the molecular function, biological role, and cellular location of gene products in a highly systematic way and in a species-neutral manner with the aim of unifying the representation of gene function across different organisms. Each contributing member of the GO Consortium independently associates GO terms to gene products from the organism(s) they are annotating. Here we introduce the Reference Genome project, which brings together those independent efforts into a unified framework based on the evolutionary relationships between genes in these different organisms. The Reference Genome project has two primary goals: to increase the depth and breadth of annotations for genes in each of the organisms in the project, and to create data sets and tools that enable other genome annotation efforts to infer GO annotations for homologous genes in their organisms. In addition, the project has several important incidental benefits, such as increasing annotation consistency across genome databases, and providing important improvements to the GO's logical structure and biological content.
New resources for functional analysis of omics data for the genus Aspergillus
Benjamin M Nitsche, Jonathan Crabtree, Gustavo C Cerqueira, Vera Meyer, Arthur FJ Ram, Jennifer R Wortman
BMC Genomics , 2011, DOI: 10.1186/1471-2164-12-486
Abstract: Based on protein homology, we mapped 97% of the 3,498 GO annotated A. nidulans genes to at least one of seven other Aspergillus species: A. niger, A. fumigatus, A. flavus, A. clavatus, A. terreus, A. oryzae and Neosartorya fischeri. GO annotation files compatible with diverse publicly available tools have been generated and deposited online. To further improve their accessibility, we developed a web application for GO enrichment analysis named FetGOat and integrated GO annotations for all Aspergillus species with public genome sequences. Both the annotation files and the web application FetGOat are accessible via the Broad Institute's website (http://www.broadinstitute.org/fetgoat/index.html webcite). To demonstrate the value of those new resources for functional analysis of omics data for the genus Aspergillus, we performed two case studies analyzing microarray data recently published for A. nidulans, A. niger and A. oryzae.We mapped A. nidulans GO annotation to seven other Aspergilli. By depositing the newly mapped GO annotation online as well as integrating it into the web tool FetGOat, we provide new, valuable and easily accessible resources for omics data analysis and interpretation for the genus Aspergillus. Furthermore, we have given a general example of how a well annotated genome can help improving GO annotation of related species to subsequently facilitate the interpretation of omics data.Gene Ontology (GO) is a framework for functional annotation of gene products aiming to provide a unique vocabulary for living systems [1]. It comprises Biological Process (BP), Molecular Function (MF) and Cellular Component (CC) ontologies. GO terms are organized as directed acyclic graphs (DAG) meaning that GO terms are connected as nodes by directed edges defining hierarchical parent-child relationships. As a consequence, the specificity of GO terms increases with increasing distance from their root node. Enrichment analysis of GO terms is a well accepted approach to di
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