Annotation Model Research Articles

A Secure Communicating MD5 based DICOM Annotation tool

DICOM proceeds through a volume of .dcm files and maintaining all standard as well but still it is not free from attacks. In this paper I present the RTStructure based GUI and the security model of DICOM communication. Here, I use massage digest with the metadata of each volume of DICOM. Metadata is nothing but data about data and it is required for constructing each volume of .dcm files. In my case, it is standardized and static with the help of python packages. After successful construction .dcm files, cryptographic hash function is applied. Hence, a secure RT-Structured based annotation model is ready for storing and accessing medical information among various medical devices by maintaining HIPPA and PACS standard.

A Secure Communicating MD5 based DICOM Annotation tool

DICOM proceeds through a volume of .dcm files and maintaining all standard as well but still it is not free from attacks. In this paper I present the RTStructure based GUI and the security model of DICOM communication. Here, I use massage digest with the metadata of each volume of DICOM. Metadata is nothing but data about data and it is required for constructing each volume of .dcm files. In my case, it is standardized and static with the help of python packages. After successful construction .dcm files, cryptographic hash function is applied.  Hence, a secure RT-Structured based annotation model is ready for storing and accessing medical information among various medical devices by maintaining HIPPA and PACS standard. &nbsp

EP393 - Development of a data model and programmatic tooling for annotation of functional assay evidence used in sequence variant interpretation

eP393 - Development of a data model and programmatic tooling for annotation of functional assay evidence used in sequence variant interpretation

Robust 3D face reconstruction from single noisy depth image through semantic consistency

This paper addresses the 3D face reconstruction and semantic annotation from a single-view noisy depth image. A deep neural network-based coarse-to-fine framework is presented to take advantage of 3D morphable model (3DMM) regression and per-vertex geometry refinement. The low-dimensional subspace coefficients of the 3DMM initialize the global facial geometry, being prone to be over-smooth because of the low-pass characteristics of the shape subspace. The proposed geometry refinement subnetwork predicts per-vertex displacements to enrich local details, which is learned from unlabelled noisy depth images based on the registration-like loss. In order to guarantee the semantic correspondence between the resultant 3D face and the depth image, a semantic consistency constraint is introduced to adapt an annotation model learned from the synthetic data to real noisy depth images. The resultant depth annotations are required to be consistent with the label propagation from the coarse and refined parametric 3D faces. The proposed coarse-to-fine reconstruction scheme and the semantic consistency constraint are evaluated on the depth-based 3D face reconstruction and semantic annotation. The series of experiments demonstrate that the proposed approach achieves the performance improvements over compared methods regarding 3D face reconstruction and depth image annotation.

Searching the PDF Haystack: Automated Knowledge Discovery in Scanned EHR Documents.

Clinicians express concern that they may be unaware of important information contained in voluminous scanned and other outside documents contained in electronic health records (EHRs). An example is "unrecognized EHR risk factor information," defined as risk factors for heritable cancer that exist within a patient's EHR but are not known by current treating providers. In a related study using manual EHR chart review, we found that half of the women whose EHR contained risk factor information meet criteria for further genetic risk evaluation for heritable forms of breast and ovarian cancer. They were not referred for genetic counseling. The purpose of this study was to compare the use of automated methods (optical character recognition with natural language processing) versus human review in their ability to identify risk factors for heritable breast and ovarian cancer within EHR scanned documents. We evaluated the accuracy of the chart review by comparing our criterion standard (physician chart review) versus an automated method involving Amazon's Textract service (Amazon.com, Seattle, Washington, United States), a clinical language annotation modeling and processing toolkit (CLAMP) (Center for Computational Biomedicine at The University of Texas Health Science, Houston, Texas, United States), and a custom-written Java application. We found that automated methods identified most cancer risk factor information that would otherwise require clinician manual review and therefore is at risk of being missed. The use of automated methods for identification of heritable risk factors within EHRs may provide an accurate yet rapid review of patients' past medical histories. These methods could be further strengthened via improved analysis of handwritten notes, tables, and colloquial phrases.

A Semi-supervised Learning Approach Based on Adaptive Weighted Fusion for Automatic Image Annotation

To learn a well-performed image annotation model, a large number of labeled samples are usually required. Although the unlabeled samples are readily available and abundant, it is a difficult task for humans to annotate large numbers of images manually. In this article, we propose a novel semi-supervised approach based on adaptive weighted fusion for automatic image annotation that can simultaneously utilize the labeled data and unlabeled data to improve the annotation performance. At first, two different classifiers, constructed based on support vector machine and covolutional neural network, respectively, are trained by different features extracted from the labeled data. Therefore, these two classifiers are independently represented as different feature views. Then, the corresponding features of unlabeled images are extracted and input into these two classifiers, and the semantic annotation of images can be obtained respectively. At the same time, the confidence of corresponding image annotation can be measured by an adaptive weighted fusion strategy. After that, the images and its semantic annotations with high confidence are submitted to the classifiers for retraining until a certain stop condition is reached. As a result, we can obtain a strong classifier that can make full use of unlabeled data. Finally, we conduct experiments on four datasets, namely, Corel 5K, IAPR TC12, ESP Game, and NUS-WIDE. In addition, we measure the performance of our approach with standard criteria, including precision, recall, F-measure, N+, and mAP. The experimental results show that our approach has superior performance and outperforms many state-of-the-art approaches.

Facet-oriented Modelling

Models are the central assets in model-driven engineering (MDE), as they are actively used in all phases of software development. Models are built using metamodel-based languages, and so objects in models are typed by a metamodel class. This typing is static, established at creation time, and cannot be changed later. Therefore, objects in MDE are closed and fixed with respect to the class they conform to, the fields they have, and the well-formedness constraints they must comply with. This hampers many MDE activities, like the reuse of model-related artefacts such as transformations, the opportunistic or dynamic combination of metamodels, or the dynamic reconfiguration of models. To alleviate this rigidity, we propose making model objects open so that they can acquire or drop so-called facets . These contribute with a type, fields and constraints to the objects holding them. Facets are defined by regular metamodels, hence being a lightweight extension of standard metamodelling. Facet metamodels may declare usage interfaces , as well as laws that govern the assignment of facets to objects (or classes). This article describes our proposal, reporting on a theory, analysis techniques, and an implementation. The benefits of the approach are validated on the basis of five case studies dealing with annotation models, transformation reuse, multi-view modelling, multi-level modelling, and language product lines.

Research on personalized image retrieval technology of video stream big data management model

The irrelevant background information in the personalized image is easy to be quantified into the same word as the main target, and the quantization process will inevitably cause the loss of a lot of visual information. This phenomenon will seriously reduce the quality of the generated theme when the personalized image content is complex. This paper proposes a Multi-Source Big Data Fusion Annotation (MSBDFA) model. The model obtains similar personalized images by analyzing the relevant multi-source information of the personalized images, and uses the annotations of the similar personalized images to label the personalized images. For the personalized images with complex background visual information, the personalized image retrieval based on complete information modeling uses the high-dimensional Gaussian distribution to directly model the continuous visual features of the personalized images, and uses the two-level spectral clustering algorithm to distribute the regional topics, so as to embed the complete local information contained in the visual features into the global features of the personalized image. Therefore, this method can completely retain visual information during the modeling process, so that the targets buried in the complex background can be better classified. The experimental results on the standard database show that the method proposed in this paper can generate high-quality personalized image subjects in complex scenes and has good retrieval performance.

Functional annotation and comparative modeling of ligninolytic enzymes from Trametes villosa (SW.) Kreisel for biotechnological applications

Functional annotation of Trametes villosa genome was performed to search Class II peroxidase proteins in this white-rot fungus, which can be valuable for several biotechnological processes. After sequence identification and manual curation, five proteins were selected to build 3 D models by comparative modeling. Analysis of sequential and structural sequences from selected targets revealed the presence of two putative Lignin Peroxidase and three putative Manganese Peroxidase on this fungal genome. All 3 D models had a similar folding pattern from selected 3 D structure templates. After minimization and validation steps, the best 3 D models were subjected to docking studies and molecular dynamics to identify structural requirements and the interactions required for molecular recognition. Two reliable 3 D models of Class II peroxidases, with typical catalytic site and architecture, and its protein sequences are indicated to recombinant production in biotechnological applications, such as bioenergy. Communicated by Ramaswamy H. Sarma

AI-Based Semantic Multimedia Indexing and Retrieval for Social Media on Smartphones

To cope with the growing number of multimedia assets on smartphones and social media, an integrated approach for semantic indexing and retrieval is required. Here, we introduce a generic framework to fuse existing image and video analysis tools and algorithms into a unified semantic annotation, indexing and retrieval model resulting in a multimedia feature vector graph representing various levels of media content, media structures and media features. Utilizing artificial intelligence (AI) and machine learning (ML), these feature representations can provide accurate semantic indexing and retrieval. Here, we provide an overview of the generic multimedia analysis framework (GMAF) and the definition of a multimedia feature vector graph framework (MMFVGF). We also introduce AI4MMRA to detect differences, enhance semantics and refine weights in the feature vector graph. To address particular requirements on smartphones, we introduce an algorithm for fast indexing and retrieval of graph structures. Experiments to prove efficiency, effectiveness and quality of the algorithm are included. All in all, we describe a solution for highly flexible semantic indexing and retrieval that offers unique potential for applications such as social media or local applications on smartphones.

A Platform-Aware Model-Driven Embedded Software Engineering Process Based on Annotated Analysis Models

In this work a platform-aware model-driven engineering process for building component-based embedded software systems using annotated analysis models is described. The process is supported by a framework, called MICOBS, that allows working with different component technologies and integrating different tools that, independently of the component technology, enable the analysis of non-functional properties based on the principles of composability and compositionality. An actor, called Framework Architect, is responsible for this integration. Three other actors take a relevant part in the analysis process. The Component Provider supplies the components, while the Component Tester is in charge of their validation. The latter also feeds MICOBS with the annotated analysis models that characterize the extra-functional properties of the components for the different platforms on which they can be deployed. The Application Architect uses these components to build new systems, performing the trade-off between different alternatives. At this stage, and in order to verify that the final system meets the extra-functional requirements, the Application Architect uses the reports generated by the integrated analysis tools. This process has been used to support the validation and verification of the on-board application software for the Instrument Control Unit of the Energetic Particle Detector of the Solar Orbiter mission.

Automatic Image Annotation by Sequentially Learning From Multi-Level Semantic Neighborhoods

Automatic image annotation is a key technology in image understanding and pattern recognition, and is becoming increasingly important in order to annotate large-scale images. In the past decade, the nearest neighbor model-based AIA (Automatic image annotation) methods have been proved to be the most successful in all classical models. This model has four major challenges including semantic gap, label-imbalance, wider range labels, and weak-labeling. In this paper, we propose a novel annotation model based on three-pass KNN (k-Nearest Neighbor) to address the aforementioned challenges. The key idea is to identify appropriate neighbors at each pass KNN. In the first pass KNN, we identify the several most relevant categories based on label feature rather than visual feature as traditional models. In the second pass KNN, we determine the relevant images based on multi-modal (visual and textual label) embedding features. As the test image has not been annotated with any label, we propose a pre-annotation strategory before image annotation to improve the semantic level. In the third pass KNN, we capture relevant labels from semantically and visually similar images and propagate them to the given unlabeled image. In contrast with traditional nearest neighbor based methods, our method can inherently alleviate the problems of semantic gap, label-imbalance, and wider range labels. In addition, to alleviate the issue of weak-labeling, we propose label refinement for training images. Extensive experiments on three classical benchmark datasets and MS-COCO demonstrate that the proposed method significantly outperforms the state-of-the-art in terms of per-label and per-image metrics.

DeepNOG: fast and accurate protein orthologous group assignment.

MotivationProtein orthologous group databases are powerful tools for evolutionary analysis, functional annotation or metabolic pathway modeling across lineages. Sequences are typically assigned to orthologous groups with alignment-based methods, such as profile hidden Markov models, which have become a computational bottleneck.ResultsWe present DeepNOG, an extremely fast and accurate, alignment-free orthology assignment method based on deep convolutional networks. We compare DeepNOG against state-of-the-art alignment-based (HMMER, DIAMOND) and alignment-free methods (DeepFam) on two orthology databases (COG, eggNOG 5). DeepNOG can be scaled to large orthology databases like eggNOG, for which it outperforms DeepFam in terms of precision and recall by large margins. While alignment-based methods still provide the most accurate assignments among the investigated methods, computing time of DeepNOG is an order of magnitude lower on CPUs. Optional GPU usage further increases throughput massively. A command-line tool enables rapid adoption by users.Availabilityand implementationSource code and packages are freely available at https://github.com/univieCUBE/deepnog. Install the platform-independent Python program with $pip install deepnog.Supplementary information Supplementary data are available at Bioinformatics online.

Helixer: cross-species gene annotation of large eukaryotic genomes using deep learning.

MotivationCurrent state-of-the-art tools for the de novo annotation of genes in eukaryotic genomes have to be specifically fitted for each species and still often produce annotations that can be improved much further. The fundamental algorithmic architecture for these tools has remained largely unchanged for about two decades, limiting learning capabilities. Here, we set out to improve the cross-species annotation of genes from DNA sequence alone with the help of deep learning. The goal is to eliminate the dependency on a closely related gene model while also improving the predictive quality in general with a fundamentally new architecture.ResultsWe present Helixer, a framework for the development and usage of a cross-species deep learning model that improves significantly on performance and generalizability when compared to more traditional methods. We evaluate our approach by building a single vertebrate model for the base-wise annotation of 186 animal genomes and a separate land plant model for 51 plant genomes. Our predictions are shown to be much less sensitive to the length of the genome than those of a current state-of-the-art tool. We also present two novel post-processing techniques that each worked to further strengthen our annotations and show in-depth results of an RNA-Seq based comparison of our predictions. Our method does not yet produce comprehensive gene models but rather outputs base pair wise probabilities.Availability and implementationThe source code of this work is available at https://github.com/weberlab-hhu/Helixer under the GNU General Public License v3.0. The trained models are available at https://doi.org/10.5281/zenodo.3974409Supplementary information Supplementary data are available at Bioinformatics online.

Transcriptome Profiling Provides Insights Into Potential Antagonistic Mechanisms Involved in Chaetomium globosum Against Bipolaris sorokiniana.

Chaetomium globosum Kunze is recognized as a potential biocontrol fungus against spot blotch of wheat caused by Bipolaris sorokiniana. Its molecular mechanism of biocontrol activity and the biosynthetic pathways involved have not been yet elucidated. Here, global transcriptome profiling of C. globosum strain Cg2 during interaction with B. sorokiniana isolate BS112 using RNA-seq was performed in order to gain insights into the potential mechanisms of antagonism. The Illumina HiSeq platform (2 × 150 bp) yielded an average of 20–22 million reads with 50–58% GC. De novo assembly generated 45,582 transcripts with 27,957 unigenes. Transcriptome analysis displayed distinct expression profiles in the interaction (Cg2–BS112), out of which 6,109 unique differentially expressed genes were present. The predominant transcripts classified as genes involved in “catalytic activity” constituted 45.06%, of which 10.02% were associated with “hydrolytic activity” (GO:0008152), and similarly, in the biological process, 29.18% of transcripts were involved in “metabolic activity” (GO:0004096 and GO:0006979). Heat map and cluster categorization suggested an increase in the expression levels of genes encoding secondary metabolites like polyketide synthase (GO:0009058), S-hydroxymethyl glutathione dehydrogenase (GO:0006069), terpene cyclase (EC 4.2.3.-), aminotran_1_2 domain-containing protein (GO:0009058), and other hydrolytic CAZYmes such as the glycosyl hydrolase (GH) family (GH 13, GH 2, GH 31, and GH 81; GO:0005975), cellulase domain-containing protein, chitinases, β-1, 3-glucanases (GO:0004565), glucan endo-1,3-beta-glucanase (GO:0052861), and proteases (GO:0004177). The obtained RNA-seq data were validated by RT-qPCR using 20 randomly chosen genes, showing consistency with the RNA-seq results. The present work is worldwide the first effort to unravel the biocontrol mechanism of C. globosum against B. sorokiniana. It generated a novel dataset for further studies and facilitated improvement of the gene annotation models in the C. globosum draft genome.

Quantitative Methods for Analyzing Intimate Partner Violence in Microblogs: Observational Study

BackgroundSocial media is a rich, virtually untapped source of data on the dynamics of intimate partner violence, one that is both global in scale and intimate in detail.ObjectiveThe aim of this study is to use machine learning and other computational methods to analyze social media data for the reasons victims give for staying in or leaving abusive relationships.MethodsHuman annotation, part-of-speech tagging, and machine learning predictive models, including support vector machines, were used on a Twitter data set of 8767 #WhyIStayed and #WhyILeft tweets each.ResultsOur methods explored whether we can analyze micronarratives that include details about victims, abusers, and other stakeholders, the actions that constitute abuse, and how the stakeholders respond.ConclusionsOur findings are consistent across various machine learning methods, which correspond to observations in the clinical literature, and affirm the relevance of natural language processing and machine learning for exploring issues of societal importance in social media.

OpenProt 2021: deeper functional annotation of the coding potential of eukaryotic genomes.

OpenProt (www.openprot.org) is the first proteogenomic resource supporting a polycistronic annotation model for eukaryotic genomes. It provides a deeper annotation of open reading frames (ORFs) while mining experimental data for supporting evidence using cutting-edge algorithms. This update presents the major improvements since the initial release of OpenProt. All species support recent NCBI RefSeq and Ensembl annotations, with changes in annotations being reported in OpenProt. Using the 131 ribosome profiling datasets re-analysed by OpenProt to date, non-AUG initiation starts are reported alongside a confidence score of the initiating codon. From the 177 mass spectrometry datasets re-analysed by OpenProt to date, the unicity of the detected peptides is controlled at each implementation. Furthermore, to guide the users, detectability statistics and protein relationships (isoforms) are now reported for each protein. Finally, to foster access to deeper ORF annotation independently of one’s bioinformatics skills or computational resources, OpenProt now offers a data analysis platform. Users can submit their dataset for analysis and receive the results from the analysis by OpenProt. All data on OpenProt are freely available and downloadable for each species, the release-based format ensuring a continuous access to the data. Thus, OpenProt enables a more comprehensive annotation of eukaryotic genomes and fosters functional proteomic discoveries.

Project Score database: a resource for investigating cancer cell dependencies and prioritizing therapeutic targets.

CRISPR genetic screens in cancer cell models are a powerful tool to elucidate oncogenic mechanisms and to identify promising therapeutic targets. The Project Score database (https://score.depmap.sanger.ac.uk/) uses genome-wide CRISPR–Cas9 dropout screening data in hundreds of highly annotated cancer cell models to identify genes required for cell fitness and prioritize novel oncology targets. The Project Score database currently allows users to investigate the fitness effect of 18 009 genes tested across 323 cancer cell models. Through interactive interfaces, users can investigate data by selecting a specific gene, cancer cell model or tissue type, as well as browsing all gene fitness scores. Additionally, users can identify and rank candidate drug targets based on an established oncology target prioritization pipeline, incorporating genetic biomarkers and clinical datasets for each target, and including suitability for drug development based on pharmaceutical tractability. Data are freely available and downloadable. To enhance analyses, links to other key resources including Open Targets, COSMIC, the Cell Model Passports, UniProt and the Genomics of Drug Sensitivity in Cancer are provided. The Project Score database is a valuable new tool for investigating genetic dependencies in cancer cells and the identification of candidate oncology targets.

Dual Triggered Correspondence Topic (DTCT)model for MeSH annotation

Accurate Medical Subject Headings (MeSH)annotation is an important issue for researchers in terms of effective information retrieval and knowledge discovery in the biomedical literature. We have developed a powerful dual triggered correspondence topic (DTCT)model for MeSH annotated articles. In our model, two types of data are assumed to be generated by the same latent topic factors and words in abstracts and titles serve as descriptions of the other type, MeSH terms. Our model allows the generation of MeSHs in abstracts to be triggered either by general document topics or by document-specific "special" word distributions in a probabilistic manner, allowing for a trade-off between the benefits of topic-based abstraction and specific word matching. In order to relax the topic influences of non-topical words or domain-frequent words in text description, we integrated the discriminative feature of Okapi BM25 into word sampling probability. This allows the model to choose keywords, which stand out from others, in order to generate MeSH terms. We further incorporate prior knowledge about relations between word and MeSH in DTCT with phi-coefficient to improve topic coherence. We demonstrated the model's usefulness in automatic MeSH annotation. Our model obtained 0.62 F-score 150,00 MEDLINE test set and showed a strength in recall rate. Specially, it yielded competitive performances in an integrated probabilistic environment without additional post-processing for filtering MeSHs.

How to Illuminate the Dark Proteome Using the Multi-omic OpenProt Resource.

Ten of thousands of open reading frames (ORFs) are hidden within genomes. These alternative ORFs, or small ORFs, have eluded annotations because they are either small or within unsuspected locations. They are found in untranslated regions or overlap a known coding sequence in messenger RNA and anywhere in a "non-coding" RNA. Serendipitous discoveries have highlighted these ORFs' importance in biological functions and pathways. With their discovery came the need for deeper ORF annotation and large-scale mining of public repositories to gather supporting experimental evidence. OpenProt, accessible at https://openprot.org/, is the first proteogenomic resource enforcing a polycistronic model of annotation across an exhaustive transcriptome for 10 species. Moreover, OpenProt reports experimental evidence cumulated across a re-analysis of 114 mass spectrometry and 87 ribosome profiling datasets. The multi-omics OpenProt resource also includes the identification of predicted functional domains and evaluation of conservation for all predicted ORFs. The OpenProt web server provides two query interfaces and one genome browser. The query interfaces allow for exploration of the coding potential of genes or transcripts of interest as well as custom downloads of all information contained in OpenProt. © 2020 The Authors. Basic Protocol 1: Using the Search interface Basic Protocol 2: Using the Downloads interface.