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Journal articles on the topic 'Clinical annotations'

Author: Grafiati

Published: 16 November 2024

Last updated: 31 July 2025

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1

Yost, Shawn, Márton Münz, Shazia Mahamdallie, Anthony Renwick, Elise Ruark, and Nazneen Rahman. "Clinical Annotation Reference Templates: a resource for consistent variant annotation." Wellcome Open Research 3 (November 14, 2018): 146. http://dx.doi.org/10.12688/wellcomeopenres.14924.1.

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Annotating the impact of a variant on a gene is a vital component of genetic medicine and genetic research. Different gene annotations for the same genomic variant are possible, because different structures and sequences for the same gene are available. The clinical community typically use RefSeq NMs to annotate gene variation, which do not always match the reference genome. The scientific community typically use Ensembl ENSTs to annotate gene variation. These match the reference genome, but often do not match the equivalent NM. Often the transcripts used to annotate gene variation are not pro

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Anderson, Matthew, Salman Sadiq, Muzammil Nahaboo Solim, et al. "Biomedical Data Annotation: An OCT Imaging Case Study." Journal of Ophthalmology 2023 (August 22, 2023): 1–9. http://dx.doi.org/10.1155/2023/5747010.

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In ophthalmology, optical coherence tomography (OCT) is a widely used imaging modality, allowing visualisation of the structures of the eye with objective and quantitative cross-sectional three-dimensional (3D) volumetric scans. Due to the quantity of data generated from OCT scans and the time taken for an ophthalmologist to inspect for various disease pathology features, automated image analysis in the form of deep neural networks has seen success for the classification and segmentation of OCT layers and quantification of features. However, existing high-performance deep learning approaches r

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Cronkite, David, Bradley Malin, John Aberdeen, Lynette Hirschman, and David Carrell. "Is the Juice Worth the Squeeze? Costs and Benefits of Multiple Human Annotators for Clinical Text De-identification." Methods of Information in Medicine 55, no. 04 (2016): 356–64. http://dx.doi.org/10.3414/me15-01-0122.

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SummaryBackground: Clinical text contains valuable information but must be de-identified before it can be used for secondary purposes. Accurate annotation of personally identifiable information (PII) is essential to the development of automated de-identification systems and to manual redaction of PII. Yet the accuracy of annotations may vary considerably across individual annotators and annotation is costly. As such, the marginal benefit of incorporating additional annotators has not been well characterized.Objectives: This study models the costs and benefits of incorporating increasing number

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Park, Jimyung, Seng Chan You, Eugene Jeong, et al. "A Framework (SOCRATex) for Hierarchical Annotation of Unstructured Electronic Health Records and Integration Into a Standardized Medical Database: Development and Usability Study." JMIR Medical Informatics 9, no. 3 (2021): e23983. http://dx.doi.org/10.2196/23983.

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Background Although electronic health records (EHRs) have been widely used in secondary assessments, clinical documents are relatively less utilized owing to the lack of standardized clinical text frameworks across different institutions. Objective This study aimed to develop a framework for processing unstructured clinical documents of EHRs and integration with standardized structured data. Methods We developed a framework known as Staged Optimization of Curation, Regularization, and Annotation of clinical text (SOCRATex). SOCRATex has the following four aspects: (1) extracting clinical notes

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Yssel, Anna E. J., Shu-Min Kao, Yves Van de Peer, and Lieven Sterck. "ORCAE-AOCC: A Centralized Portal for the Annotation of African Orphan Crop Genomes." Genes 10, no. 12 (2019): 950. http://dx.doi.org/10.3390/genes10120950.

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ORCAE (Online Resource for Community Annotation of Eukaryotes) is a public genome annotation curation resource. ORCAE-AOCC is a branch that is dedicated to the genomes published as part of the African Orphan Crops Consortium (AOCC). The motivation behind the development of the ORCAE platform was to create a knowledge-based website where the research-community can make contributions to improve genome annotations. All changes to any given gene-model or gene description are stored, and the entire annotation history can be retrieved. Genomes can either be set to “public” or “restricted” mode; anon

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Keegan, Niamh M., Samantha E. Vasselman, Ethan Barnett, et al. "Clinical annotations for prostate cancer research: Defining data elements, creating a reproducible analytical pipeline, and assessing data quality." Journal of Clinical Oncology 40, no. 6_suppl (2022): 64. http://dx.doi.org/10.1200/jco.2022.40.6_suppl.064.

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64 Background: Routine clinical data from the electronic medical record are indispensable for retrospective and prospective observational studies and clinical trials. Their reproducibility is often not assessed. We sought to develop a prostate cancer-specific database with a defined source hierarchy for clinical annotations and to evaluate data reproducibility. Methods: At a comprehensive cancer center, we designed and implemented a clinical database for men with prostate cancer and clinical-grade paired tumor–normal sequencing for whom we performed team-based retrospective clinical data annot

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Moore, Jill E., Xiao-Ou Zhang, Shaimae I. Elhajjajy, et al. "Integration of high-resolution promoter profiling assays reveals novel, cell type–specific transcription start sites across 115 human cell and tissue types." Genome Research 32, no. 2 (2021): 389–402. http://dx.doi.org/10.1101/gr.275723.121.

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Accurate transcription start site (TSS) annotations are essential for understanding transcriptional regulation and its role in human disease. Gene collections such as GENCODE contain annotations for tens of thousands of TSSs, but not all of these annotations are experimentally validated nor do they contain information on cell type–specific usage. Therefore, we sought to generate a collection of experimentally validated TSSs by integrating RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression (RAMPAGE) data from 115 cell and tissue types, which resulted in a collection of

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de Bruijn, Ino, Xiang Li, Onur Sumer, et al. "Abstract 1156: Genome Nexus: A comprehensive resource for the annotation and interpretation of genomic variants in cancer." Cancer Research 82, no. 12_Supplement (2022): 1156. http://dx.doi.org/10.1158/1538-7445.am2022-1156.

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Abstract Interpreting genomic variants in tumor samples presents a challenge in research and the clinical setting. A major barrier is that information about variants is fragmented across disparate databases, and aggregating information from these requires building extensive infrastructure. To this end, we have developed Genome Nexus, a one stop shop for variant annotation, equipped with a powerful API for bulk annotation of variants and a user friendly interface for cancer researchers. Genome Nexus is available at https://www.genomenexus.org. It a) aggregates variant information from a large n

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Queirós, Pedro, Polina Novikova, Paul Wilmes, and Patrick May. "Unification of functional annotation descriptions using text mining." Biological Chemistry 402, no. 8 (2021): 983–90. http://dx.doi.org/10.1515/hsz-2021-0125.

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Abstract A common approach to genome annotation involves the use of homology-based tools for the prediction of the functional role of proteins. The quality of functional annotations is dependent on the reference data used, as such, choosing the appropriate sources is crucial. Unfortunately, no single reference data source can be universally considered the gold standard, thus using multiple references could potentially increase annotation quality and coverage. However, this comes with challenges, particularly due to the introduction of redundant and exclusive annotations. Through text mining it

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Bax, Martin, Hilary Hart, and Sue Jenkins. "Annotations." Developmental Medicine & Child Neurology 23, no. 1 (2008): 92–95. http://dx.doi.org/10.1111/j.1469-8749.1981.tb08450.x.

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Gedo, John E. "Annotations on Artemisia." Psychoanalytic Review 100, no. 5 (2013): 717–40. http://dx.doi.org/10.1521/prev.2013.100.5.717.

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Hinge, Kerry, Aditya Ghose, and Andrew Miller. "A Framework for Detecting Interactions Between Co-Incident Clinical Processes." International Journal of E-Health and Medical Communications 1, no. 2 (2010): 24–35. http://dx.doi.org/10.4018/jehmc.2010040103.

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The detection of treatment conflicts between multiple treatment protocols that are co-incident is a difficult and open problem that is particularly exacerbated regarding the treatment of multiple medical conditions co-occurring in aged patients. For example, a clinical protocol for prostate cancer treatment requires the administration of androgen-suppressing medication, which may negatively interact with another, co-incident protocol if the same patient were being treated for renal disease via haemodialysis, where androgen-enhancers are frequently administered. These treatment conflicts are su

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Quick, Corbin, Xiaoquan Wen, Gonçalo Abecasis, Michael Boehnke, and Hyun Min Kang. "Integrating comprehensive functional annotations to boost power and accuracy in gene-based association analysis." PLOS Genetics 16, no. 12 (2020): e1009060. http://dx.doi.org/10.1371/journal.pgen.1009060.

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Gene-based association tests aggregate genotypes across multiple variants for each gene, providing an interpretable gene-level analysis framework for genome-wide association studies (GWAS). Early gene-based test applications often focused on rare coding variants; a more recent wave of gene-based methods, e.g. TWAS, use eQTLs to interrogate regulatory associations. Regulatory variants are expected to be particularly valuable for gene-based analysis, since most GWAS associations to date are non-coding. However, identifying causal genes from regulatory associations remains challenging and content

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Lin, Jia-Wen, Feng Lu, Tai-Chen Lai, et al. "Meibomian glands segmentation in infrared images with limited annotation." International Journal of Ophthalmology 17, no. 3 (2024): 401–7. http://dx.doi.org/10.18240/ijo.2024.03.01.

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AIM: To investigate a pioneering framework for the segmentation of meibomian glands (MGs), using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis. METHODS: Totally 203 infrared meibomian images from 138 patients with dry eye disease, accompanied by corresponding annotations, were gathered for the study. A rectified scribble-supervised gland segmentation (RSSGS) model, incorporating temporal ensemble prediction, uncertainty estimation, and a transformation equivariance constraint, was introduced to address constraints imposed by lim

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Mei, Hao, Lianna Li, Fan Jiang, et al. "snpGeneSets: An R Package for Genome-Wide Study Annotation." G3 Genes|Genomes|Genetics 6, no. 12 (2016): 4087–95. http://dx.doi.org/10.1534/g3.116.034694.

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Abstract Genome-wide studies (GWS) of SNP associations and differential gene expressions have generated abundant results; next-generation sequencing technology has further boosted the number of variants and genes identified. Effective interpretation requires massive annotation and downstream analysis of these genome-wide results, a computationally challenging task. We developed the snpGeneSets package to simplify annotation and analysis of GWS results. Our package integrates local copies of knowledge bases for SNPs, genes, and gene sets, and implements wrapper functions in the R language to en

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Fan, Jung-wei, Jianrong Li, and Yves A. Lussier. "Semantic Modeling for Exposomics with Exploratory Evaluation in Clinical Context." Journal of Healthcare Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/3818302.

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Exposome is a critical dimension in the precision medicine paradigm. Effective representation of exposomics knowledge is instrumental to melding nongenetic factors into data analytics for clinical research. There is still limited work in (1) modeling exposome entities and relations with proper integration to mainstream ontologies and (2) systematically studying their presence in clinical context. Through selected ontological relations, we developed a template-driven approach to identifying exposome concepts from the Unified Medical Language System (UMLS). The derived concepts were evaluated in

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Zhang, Jichang, Yuanjie Zheng, and Yunfeng Shi. "A Soft Label Method for Medical Image Segmentation with Multirater Annotations." Computational Intelligence and Neuroscience 2023 (February 18, 2023): 1–11. http://dx.doi.org/10.1155/2023/1883597.

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In medical image analysis, collecting multiple annotations from different clinical raters is a typical practice to mitigate possible diagnostic errors. For such multirater labels’ learning problems, in addition to majority voting, it is a common practice to use soft labels in the form of full-probability distributions obtained by averaging raters as ground truth to train the model, which benefits from uncertainty contained in soft labels. However, the potential information contained in soft labels is rarely studied, which may be the key to improving the performance of medical image segmentatio

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Johnson, Amber, Yekaterina B. Khotskaya, Lauren Brusco, et al. "Clinical Use of Precision Oncology Decision Support." JCO Precision Oncology, no. 1 (November 2017): 1–12. http://dx.doi.org/10.1200/po.17.00036.

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Purpose Precision oncology is hindered by the lack of decision support for determining the functional and therapeutic significance of genomic alterations in tumors and relevant clinically available options. To bridge this knowledge gap, we established a Precision Oncology Decision Support team that provides annotations at the alteration level and subsequently determined whether clinical decision making was influenced. Methods Genomic alterations were annotated to determine actionability on the basis of a variant’s known or potential functional and/or therapeutic significance. The medical recor

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Luo, Yuan, and Peter Szolovits. "Efficient Queries of Stand-off Annotations for Natural Language Processing on Electronic Medical Records." Biomedical Informatics Insights 8 (January 2016): BII.S38916. http://dx.doi.org/10.4137/bii.s38916.

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In natural language processing, stand-off annotation uses the starting and ending positions of an annotation to anchor it to the text and stores the annotation content separately from the text. We address the fundamental problem of efficiently storing stand-off annotations when applying natural language processing on narrative clinical notes in electronic medical records (EMRs) and efficiently retrieving such annotations that satisfy position constraints. Efficient storage and retrieval of stand-off annotations can facilitate tasks such as mapping unstructured text to electronic medical record

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Sánchez-Salvador, Alejandro, Sandra González-de la Fuente, Begoña Aguado, Phillip A. Yates, and Jose M. Requena. "Refinement of Leishmania donovani Genome Annotations in the Light of Ribosome-Protected mRNAs Fragments (Ribo-Seq Data)." Genes 14, no. 8 (2023): 1637. http://dx.doi.org/10.3390/genes14081637.

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Advances in next-generation sequencing methodologies have facilitated the assembly of an ever-increasing number of genomes. Gene annotations are typically conducted via specialized software, but the most accurate results require additional manual curation that incorporates insights derived from functional and bioinformatic analyses (e.g., transcriptomics, proteomics, and phylogenetics). In this study, we improved the annotation of the Leishmania donovani (strain HU3) genome using publicly available data from the deep sequencing of ribosome-protected mRNA fragments (Ribo-Seq). As a result of th

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Schiavone, Alice, Lea Marie Pehrson, Silvia Ingala, et al. "Effective Machine Learning Techniques for Non-English Radiology Report Classification: A Danish Case Study." AI 6, no. 2 (2025): 37. https://doi.org/10.3390/ai6020037.

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Background: Machine learning methods for clinical assistance require a large number of annotations from trained experts to achieve optimal performance. Previous work in natural language processing has shown that it is possible to automatically extract annotations from the free-text reports associated with chest X-rays. Methods: This study investigated techniques to extract 49 labels in a hierarchical tree structure from chest X-ray reports written in Danish. The labels were extracted from approximately 550,000 reports by performing multi-class, multi-label classification using a method based o

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Kleinert, Philip, and Martin Kircher. "A framework to score the effects of structural variants in health and disease." Genome Research 32, no. 4 (2022): 766–77. http://dx.doi.org/10.1101/gr.275995.121.

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Although technological advances improved the identification of structural variants (SVs) in the human genome, their interpretation remains challenging. Several methods utilize individual mechanistic principles like the deletion of coding sequence or 3D genome architecture disruptions. However, a comprehensive tool using the broad spectrum of available annotations is missing. Here, we describe CADD-SV, a method to retrieve and integrate a wide set of annotations to predict the effects of SVs. Previously, supervised learning approaches were limited due to a small number and biased set of annotat

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Lin, Tai-Pei, Chiou-Ying Yang, Ko-Jiunn Liu, Meng-Yuan Huang, and Yen-Lin Chen. "Immunohistochemical Stain-Aided Annotation Accelerates Machine Learning and Deep Learning Model Development in the Pathologic Diagnosis of Nasopharyngeal Carcinoma." Diagnostics 13, no. 24 (2023): 3685. http://dx.doi.org/10.3390/diagnostics13243685.

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Nasopharyngeal carcinoma (NPC) is an epithelial cancer originating in the nasopharynx epithelium. Nevertheless, annotating pathology slides remains a bottleneck in the development of AI-driven pathology models and applications. In the present study, we aim to demonstrate the feasibility of using immunohistochemistry (IHC) for annotation by non-pathologists and to develop an efficient model for distinguishing NPC without the time-consuming involvement of pathologists. For this study, we gathered NPC slides from 251 different patients, comprising hematoxylin and eosin (H&E) slides, pan-cytok

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Jo, Eunkyung, Rachael Zehrung, Katherine Genuario, Alexandra Papoutsaki, and Daniel A. Epstein. "Exploring Patient-Generated Annotations to Digital Clinical Symptom Measures for Patient-Centered Communication." Proceedings of the ACM on Human-Computer Interaction 8, CSCW2 (2024): 1–26. http://dx.doi.org/10.1145/3686997.

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Patients' self-reports are crucial for effective care management of clinical conditions involving subjective symptoms. While patients often value the ability to bring in different forms of self-report data to convey their lived experiences, they often struggle to make their data practically usable in clinical settings. To better center patient needs in communicating illness experiences in clinical contexts, we explore the idea of patient annotations to digital clinical self-report measures, specifically in the context of discontinuing antidepressants. Through interviews with 20 patients with A

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Jaravine, Victor, James Balmford, Patrick Metzger, Melanie Boerries, Harald Binder, and Martin Boeker. "Annotation of Human Exome Gene Variants with Consensus Pathogenicity." Genes 11, no. 9 (2020): 1076. http://dx.doi.org/10.3390/genes11091076.

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A novel approach is developed to address the challenge of annotating with phenotypic effects those exome variants for which relevant empirical data are lacking or minimal. The predictive annotation method is implemented as a stacked ensemble of supervised base-learners, including distributed random forest and gradient boosting machines. Ensemble models were trained and cross-validated on evidence-based categorical variant effect annotations from the ClinVar database, and were applied to 84 million non-synonymous single nucleotide variants (SNVs). The consensus model combined 39 functional muta

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Zhang, Chao, Zhongwei Chen, Miming Zhang, and Shulei Jia. "KEGG_Extractor: An Effective Extraction Tool for KEGG Orthologs." Genes 14, no. 2 (2023): 386. http://dx.doi.org/10.3390/genes14020386.

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The KEGG Orthology (KO) database is a widely used molecular function reference database which can be used to conduct functional annotation of most microorganisms. At present, there are many KEGG tools based on the KO entries for annotating functional orthologs. However, determining how to efficiently extract and sort the annotation results of KEGG still hinders the subsequent genome analysis. There is a lack of effective measures used to quickly extract and classify the gene sequences and species information of the KEGG annotations. Here, we present a supporting tool: KEGG_Extractor for specie

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Lee, Kye Hwa, Hyunsung Lee, Jin-Hyeok Park, Yi-Jun Kim, and Youngho Lee. "ANNO: A General Annotation Tool for Bilingual Clinical Note Information Extraction." Healthcare Informatics Research 28, no. 1 (2022): 89–94. http://dx.doi.org/10.4258/hir.2022.28.1.89.

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Objectives: This study was conducted to develop a generalizable annotation tool for bilingual complex clinical text annotation, which led to the design and development of a clinical text annotation tool, ANNO.Methods: We designed ANNO to enable human annotators to support the annotation of information in clinical documents efficiently and accurately. First, annotations for different classes (word or phrase types) can be tagged according to the type of word using the dictionary function. In addition, it is possible to evaluate and reconcile differences by comparing annotation results between hu

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Zhao, Zipei, Fengqian Pang, Yaou Liu, Zhiwen Liu, and Chuyang Ye. "Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations." Machine Learning for Biomedical Imaging 1, December 2022 (2023): 1–30. http://dx.doi.org/10.59275/j.melba.2022-8g31.

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Cell detection in histopathology images is of great interest to clinical practice and research, and convolutional neural networks (CNNs) have achieved remarkable cell detection results. Typically, to train CNN-based cell detection models, every positive instance in the training images needs to be annotated, and instances that are not labeled as positive are considered negative samples. However, manual cell annotation is complicated due to the large number and diversity of cells, and it can be difficult to ensure the annotation of every positive instance. In many cases, only incomplete annotati

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Chai, Yuan, Vincent Maes, A. Mounir Boudali, Brooke Rackel, and William L. Walter. "Inadequate Annotation and Its Impact on Pelvic Tilt Measurement in Clinical Practice." Journal of Clinical Medicine 13, no. 5 (2024): 1394. http://dx.doi.org/10.3390/jcm13051394.

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Background: Accurate pre-surgical templating of the pelvic tilt (PT) angle is essential for hip and spine surgeries, yet the reliability of PT annotations is often compromised by human error, inherent subjectivity, and variations in radiographic quality. This study aims to identify challenges leading to inadequate annotations at a landmark dimension and evaluating their impact on PT. Methods: We retrospectively collected 115 consecutive sagittal radiographs for the measurement of PT based on two definitions: the anterior pelvic plane and a line connecting the femoral head’s centre to the sacra

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Taleb, Aiham, Csaba Rohrer, Benjamin Bergner, et al. "Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification." Diagnostics 12, no. 5 (2022): 1237. http://dx.doi.org/10.3390/diagnostics12051237.

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High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data. In this work, we propose employing self-supervised methods. To that end, we trained with three self-supervised algorithms on a large corpus of unlabeled dental images, which contained 38K bitewing radiographs (BWRs). We then applied the learned neural network representations on tooth-level dental caries classification, for which we utilized labels extracted from electronic health records (EHRs). Finally

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Li, Dana, Lea Marie Pehrson, Rasmus Bonnevie, et al. "Performance and Agreement When Annotating Chest X-ray Text Reports—A Preliminary Step in the Development of a Deep Learning-Based Prioritization and Detection System." Diagnostics 13, no. 6 (2023): 1070. http://dx.doi.org/10.3390/diagnostics13061070.

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A chest X-ray report is a communicative tool and can be used as data for developing artificial intelligence-based decision support systems. For both, consistent understanding and labeling is important. Our aim was to investigate how readers would comprehend and annotate 200 chest X-ray reports. Reports written between 1 January 2015 and 11 March 2022 were selected based on search words. Annotators included three board-certified radiologists, two trained radiologists (physicians), two radiographers (radiological technicians), a non-radiological physician, and a medical student. Consensus labels

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Reynolds, Regina H., John Hardy, Mina Ryten, and Sarah A. Gagliano Taliun. "Informing disease modelling with brain-relevant functional genomic annotations." Brain 142, no. 12 (2019): 3694–712. http://dx.doi.org/10.1093/brain/awz295.

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How can we best translate the success of genome-wide association studies for neurological and neuropsychiatric diseases into therapeutic targets? Reynolds et al. critically assess existing brain-relevant functional genomic annotations and the tools available for integrating such annotations with summary-level genetic association data.

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Bunnell, Arianna, Kailee Hung, John A. Shepherd, and Peter Sadowski. "BUSClean: Open-source software for breast ultrasound image pre-processing and knowledge extraction for medical AI." PLOS ONE 19, no. 12 (2024): e0315434. https://doi.org/10.1371/journal.pone.0315434.

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Development of artificial intelligence (AI) for medical imaging demands curation and cleaning of large-scale clinical datasets comprising hundreds of thousands of images. Some modalities, such as mammography, contain highly standardized imaging. In contrast, breast ultrasound imaging (BUS) can contain many irregularities not indicated by scan metadata, such as enhanced scan modes, sonographer annotations, or additional views. We present an open-source software solution for automatically processing clinical BUS datasets. The algorithm performs BUS scan filtering (flagging of invalid and non-B-m

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Ghiasvand, Omid, and Rohit J. Kate. "Learning for clinical named entity recognition without manual annotations." Informatics in Medicine Unlocked 13 (2018): 122–27. http://dx.doi.org/10.1016/j.imu.2018.10.011.

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Pehrson, Lea Marie, Dana Li, Alyas Mayar, et al. "Clinicians’ Agreement on Extrapulmonary Radiographic Findings in Chest X-Rays Using a Diagnostic Labelling Scheme." Diagnostics 15, no. 7 (2025): 902. https://doi.org/10.3390/diagnostics15070902.

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Objective: Reliable reading and annotation of chest X-ray (CXR) images are essential for both clinical decision-making and AI model development. While most of the literature emphasizes pulmonary findings, this study evaluates the consistency and reliability of annotations for extrapulmonary findings, using a labelling scheme. Methods: Six clinicians with varying experience levels (novice, intermediate, and experienced) annotated 100 CXR images using a diagnostic labelling scheme, in two rounds, separated by a three-week washout period. Annotation consistency was assessed using Randolph’s free-

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Rahm, Erhard, Toralf Kirsten, and Jörg Lange. "The GeWare data warehouse platform for the analysis of molecular-biological and clinical data." Journal of Integrative Bioinformatics 4, no. 1 (2007): 1–11. http://dx.doi.org/10.1515/jib-2007-47.

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Abstract We introduce the GeWare data warehouse platform for the integrated analysis of clinical information, microarray data and annotations within large biomedical research studies. Clinical data is obtained from a commercial study management system while publicly available data is integrated using a mediator approach. The platform utilizes a generic approach to manage different types of annotations. We outline the overall architecture of the platform, its implementation as well as the main processing and analysis workflows.

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Cary, Michael, Katie Podshivalova, and Cynthia Kenyon. "Application of Transcriptional Gene Modules to Analysis of Caenorhabditis elegans’ Gene Expression Data." G3: Genes|Genomes|Genetics 10, no. 10 (2020): 3623–38. http://dx.doi.org/10.1534/g3.120.401270.

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Identification of co-expressed sets of genes (gene modules) is used widely for grouping functionally related genes during transcriptomic data analysis. An organism-wide atlas of high-quality gene modules would provide a powerful tool for unbiased detection of biological signals from gene expression data. Here, using a method based on independent component analysis we call DEXICA, we have defined and optimized 209 modules that broadly represent transcriptional wiring of the key experimental organism C. elegans. These modules represent responses to changes in the environment (e.g., starvation, e

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Philipp, Markus, Anna Alperovich, Alexander Lisogorov, et al. "Annotation-efficient learning of surgical instrument activity in neurosurgery." Current Directions in Biomedical Engineering 8, no. 1 (2022): 30–33. http://dx.doi.org/10.1515/cdbme-2022-0008.

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Abstract Machine learning-based solutions rely heavily on the quality and quantity of the training data. In the medical domain, the main challenge is to acquire rich and diverse annotated datasets for training. We propose to decrease the annotation efforts and further diversify the dataset by introducing an annotation-efficient learning workflow. Instead of costly pixel-level annotation, we require only image-level labels as the remainder is covered by simulation. Thus, we obtain a large-scale dataset with realistic images and accurate ground truth annotations. We use this dataset for the inst

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Vieira, Alexandre R. "Multiple annotations forGCPII in the htgs database." American Journal of Medical Genetics 123A, no. 3 (2003): 316. http://dx.doi.org/10.1002/ajmg.a.20337.

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Thakur, Siddhesh, Shahriar Faghani, Mana Moassefi, et al. "TMIC-60. BRATS-PATH: ASSESSING HETEROGENEOUS HISTOPATHOLOGIC REGIONS IN GLIOBLASTOMA." Neuro-Oncology 26, Supplement_8 (2024): viii312. http://dx.doi.org/10.1093/neuonc/noae165.1238.

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Abstract Glioblastoma, the most common malignant primary adult brain tumor, poses significant diagnostic and treatment challenges due to its heterogeneous molecular and micro-environmental profiles. To this end, we organize the BraTS-Path challenge to provide a public benchmarking environment and a comprehensive dataset to develop and validate AI models for identifying distinct histopathologic glioblastoma sub-regions in H&E-stained digitized tissue sections. We identified 188 multi-institutional diagnostic slides of glioblastoma (IDH-wt, Gr.4) cases, from the TCGA-GBM and TCGA-LGG data co

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Schilling, Marcel P., Niket Ahuja, Luca Rettenberger, Tim Scherr, and Markus Reischl. "Impact of Annotation Noise on Histopathology Nucleus Segmentation." Current Directions in Biomedical Engineering 8, no. 2 (2022): 197–200. http://dx.doi.org/10.1515/cdbme-2022-1051.

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Abstract Deep learning is often used for automated diagnosis support in biomedical image processing scenarios. Annotated datasets are essential for the supervised training of deep neural networks. The problem of consistent and noise-free annotation remains for experts such as pathologists. The variability within an annotator (intra) and the variability between annotators (inter) are current challenges. In clinical practice or biology, instance segmentation is a common task, but a comprehensive and quantitative study regarding the impact of noisy annotations lacks. In this paper, we present a c

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Di Bartolomeo, Mattia, Arrigo Pellacani, Federico Bolelli, et al. "Inferior Alveolar Canal Automatic Detection with Deep Learning CNNs on CBCTs: Development of a Novel Model and Release of Open-Source Dataset and Algorithm." Applied Sciences 13, no. 5 (2023): 3271. http://dx.doi.org/10.3390/app13053271.

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Introduction: The need of accurate three-dimensional data of anatomical structures is increasing in the surgical field. The development of convolutional neural networks (CNNs) has been helping to fill this gap by trying to provide efficient tools to clinicians. Nonetheless, the lack of a fully accessible datasets and open-source algorithms is slowing the improvements in this field. In this paper, we focus on the fully automatic segmentation of the Inferior Alveolar Canal (IAC), which is of immense interest in the dental and maxillo-facial surgeries. Conventionally, only a bidimensional annotat

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Magnini, Bernardo, Saeed Farzi, Pietro Ferrazzi, et al. "A cost-effective approach to counterbalance the scarcity of medical datasets." Frontiers in Disaster and Emergency Medicine 3 (May 9, 2025). https://doi.org/10.3389/femer.2025.1558200.

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This paper presents an innovative methodology for addressing the critical issue of data scarcity in clinical research, specifically within emergency departments. Inspired by the recent advancements in the generative abilities of Large Language Models (LLMs), we devised an automated approach based on LLMs to extend an existing publicly available English dataset to new languages. We constructed a pipeline of multiple automated components which first converts an existing annotated dataset from its complex standard format to a simpler inline annotated format, then generates inline annotations in t

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Mendieta, John Pablo, Alexandre P. Marand, William A. Ricci, Xuan Zhang, and Robert J. Schmitz. "Leveraging histone modifications to improve genome annotations." G3 Genes|Genomes|Genetics, July 27, 2021. http://dx.doi.org/10.1093/g3journal/jkab263.

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Abstract Accurate genome annotations are essential to modern biology; however, they remain challenging to produce. Variation in gene structure and expression across species, as well as within an organism, make correctly annotating genes arduous; an issue exacerbated by pitfalls in current in silico methods. These issues necessitate complementary approaches to add additional confidence and rectify potential misannotations. Integration of epigenomic data into genome annotation is one such approach. In this study, we utilized sets of histone modification data, which are precisely distributed at e

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Kimmel, Jacob C., and David R. Kelley. "Semisupervised adversarial neural networks for single-cell classification." Genome Research, February 24, 2021. http://dx.doi.org/10.1101/gr.268581.120.

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Annotating cell identities is a common bottleneck in the analysis of single-cell genomics experiments. Here, we present scNym, a semisupervised, adversarial neural network that learns to transfer cell identity annotations from one experiment to another. scNym takes advantage of information in both labeled data sets and new, unlabeled data sets to learn rich representations of cell identity that enable effective annotation transfer. We show that scNym effectively transfers annotations across experiments despite biological and technical differences, achieving performance superior to existing met

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Dobbie, Samuel, Huw Strafford, W. Owen Pickrell, et al. "Markup: A Web-Based Clinical Annotation Tool with Enhanced Ontology Mapping." International Journal of Population Data Science 5, no. 5 (2020). http://dx.doi.org/10.23889/ijpds.v5i5.1634.

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IntroductionUnstructured free-text clinical notes often contain valuable information relating to patient symptoms, prescriptions and diagnoses. These can assist with better care for patients and novel healthcare research if transformed into accessible, structured clinical text. In particular, Natural Language Processing (NLP) algorithms can produce such structured outputs, but require gold standard data to train and validate their accuracy. While existing tools such as Brat and Webanno provide interfaces to manually annotate text, there is a lack of capability to efficiently annotate complex c

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Kromp, Florian, Raphael Wagner, Basak Balaban, et al. "An annotated human blastocyst dataset to benchmark deep learning architectures for in vitro fertilization." Scientific Data 10, no. 1 (2023). http://dx.doi.org/10.1038/s41597-023-02182-3.

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AbstractMedical Assisted Reproduction proved its efficacy to treat the vast majority forms of infertility. One of the key procedures in this treatment is the selection and transfer of the embryo with the highest developmental potential. To assess this potential, clinical embryologists routinely work with static images (morphological assessment) or short video sequences (time-lapse annotation). Recently, Artificial Intelligence models were utilized to support the embryo selection procedure. Even though they have proven their great potential in different in vitro fertilization settings, there is

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Seinen, Tom M., Jan A. Kors, Erik M. van Mulligen, and Peter R. Rijnbeek. "Annotation-preserving machine translation of English corpora to validate Dutch clinical concept extraction tools." Journal of the American Medical Informatics Association, June 27, 2024. http://dx.doi.org/10.1093/jamia/ocae159.

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Abstract Objective To explore the feasibility of validating Dutch concept extraction tools using annotated corpora translated from English, focusing on preserving annotations during translation and addressing the scarcity of non-English annotated clinical corpora. Materials and Methods Three annotated corpora were standardized and translated from English to Dutch using 2 machine translation services, Google Translate and OpenAI GPT-4, with annotations preserved through a proposed method of embedding annotations in the text before translation. The performance of 2 concept extraction tools, MedS

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Foroozandeh Shahraki, Mehdi, Marjan Farahbod, and Maxwell W. Libbrecht. "Robust chromatin state annotation." Genome Research, March 21, 2024, gr.278343.123. http://dx.doi.org/10.1101/gr.278343.123.

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With the goal of mapping genomic activity, international projects have recently measured epigenetic activity in hundreds of cell and tissue types. Chromatin state annotations produced by segmentation and genome annotation (SAGA) methods have emerged as the predominant way to summarize these epigenomic data sets in order to annotate the genome. These chromatin state annotations are essential for many genomic tasks, including identifying active regulatory elements and interpreting disease-associated genetic variation. However, despite the widespread applications of SAGA methods, no principled ap

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Mehta, Sunali, Deborah Wright, Michael A. Black, et al. "Impact of clinical data veracity on cancer genomic research." JNCI Cancer Spectrum, October 18, 2022. http://dx.doi.org/10.1093/jncics/pkac070.

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Abstract Genomic analysis of tumours is transforming our understanding of cancer. However, while a great deal of attention is paid to the accuracy of the cancer genomic data itself, less attention has been paid to the accuracy of the associated clinical information that renders the genomic data useful for research. In this Brief Communication, we suggest that omissions and errors in clinical annotations have a major impact on the interpretation of cancer genomic data. We describe our discovery of annotation omissions and errors when reviewing an already carefully annotated colorectal cancer ge

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