Relevant bibliographies by topics / Automatic cell types annotation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Automatic cell types annotation'

Author: Grafiati
Published: 25 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Automatic cell types annotation"

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Hia, Nazifa Tasnim, and Sumon Ahmed. "Automatic cell type annotation using supervised classification: A systematic literature review." Systematic Literature Review and Meta-Analysis Journal 3, no. 3 (2022): 99–108. http://dx.doi.org/10.54480/slrm.v3i3.45.

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Single-cell sequencing gives us the opportunity to analyze cells on an individual level rather than at a population level. There are different types of sequencing based on the stage and portion of the cell from where the data are collected. Among those Single Cell RNA seq is most widely used and most application of cell type annotation has been on Single-cell RNA seq data. Tools have been developed for automatic cell type annotation as manual annotation of cell type is time-consuming and partially subjective. There are mainly three strategies to associate cell type with gene expression profile
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Xu, Yang, Simon J. Baumgart, Christian M. Stegmann, and Sikander Hayat. "MACA: marker-based automatic cell-type annotation for single-cell expression data." Bioinformatics 38, no. 6 (2021): 1756–60. http://dx.doi.org/10.1093/bioinformatics/btab840.

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Abstract Summary Accurately identifying cell types is a critical step in single-cell sequencing analyses. Here, we present marker-based automatic cell-type annotation (MACA), a new tool for annotating single-cell transcriptomics datasets. We developed MACA by testing four cell-type scoring methods with two public cell-marker databases as reference in six single-cell studies. MACA compares favorably to four existing marker-based cell-type annotation methods in terms of accuracy and speed. We show that MACA can annotate a large single-nuclei RNA-seq study in minutes on human hearts with ∼290K ce
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Shao, Xin, Jie Liao, Xiaoyan Lu, Rui Xue, Ni Ai, and Xiaohui Fan. "scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data." iScience 23, no. 3 (2020): 100882. http://dx.doi.org/10.1016/j.isci.2020.100882.

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Tang, Yachen, Xuefeng Li, and Mingguang Shi. "LIDER: cell embedding based deep neural network classifier for supervised cell type identification." PeerJ 11 (August 16, 2023): e15862. http://dx.doi.org/10.7717/peerj.15862.

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Background Automatic cell type identification has been an urgent task for the rapid development of single-cell RNA-seq techniques. Generally, the current approach for cell type identification is to generate cell clusters by unsupervised clustering and later assign labels to each cell cluster with manual annotation. Methods Here, we introduce LIDER (celL embeddIng based Deep nEural netwoRk classifier), a deep supervised learning method that combines cell embedding and deep neural network classifier for automatic cell type identification. Based on a stacked denoising autoencoder with a tailored
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Xiong, Yi-Xuan, Meng-Guo Wang, Luonan Chen, and Xiao-Fei Zhang. "Cell-type annotation with accurate unseen cell-type identification using multiple references." PLOS Computational Biology 19, no. 6 (2023): e1011261. http://dx.doi.org/10.1371/journal.pcbi.1011261.

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The recent advances in single-cell RNA sequencing (scRNA-seq) techniques have stimulated efforts to identify and characterize the cellular composition of complex tissues. With the advent of various sequencing techniques, automated cell-type annotation using a well-annotated scRNA-seq reference becomes popular. But it relies on the diversity of cell types in the reference, which may not capture all the cell types present in the query data of interest. There are generally unseen cell types in the query data of interest because most data atlases are obtained for different purposes and techniques.
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Liu, Huaitian, Alexandra Harris, Brittany Jenkins-Lord, et al. "Abstract LB240: Cell type annotation using singleR with custom reference for single-nucleus multiome data derived from frozen human breast tumors." Cancer Research 84, no. 7_Supplement (2024): LB240. http://dx.doi.org/10.1158/1538-7445.am2024-lb240.

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Abstract Single-nucleus joint ATAC- and RNA-sequencing (snMultiome) can be used to identify functionally divergent cell subpopulations based on their transcriptomic and epigenetic profiles within complex samples. Accurate cell type annotation is critical to successful snMultiome data analysis. Several computational methods have been developed for automatic annotation. Traditional cell type annotation methods initially cluster cells using unsupervised learning methods based on the gene expression profiles, then label the clusters using aggregated cluster-level expression profiles and marker gen
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Zhang, Yuping, Gabriel Cruz, Hanbyul Cho, et al. "Abstract 1063: CellMap: a comprehensive human single cell gene expression reference for automated cell annotation and cancer cell-of-origin analysis." Cancer Research 85, no. 8_Supplement_1 (2025): 1063. https://doi.org/10.1158/1538-7445.am2025-1063.

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Abstract In single-cell transcriptomic analysis, accurate cell type annotation forms the essential basis for all downstream analysis and data interpretation. While time consuming manual annotation requires extensive prior knowledge, available automated cell annotation tools lack a unified, curated human cell reference to ensure successful identification of all cell types present in any given dataset. To fill in the gap we create “CellMap” a comprehensive single cell gene expression reference database of known cell types across most human tissues by compiling, curating and integrating single ce
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Doddahonnaiah, Deeksha, Patrick J. Lenehan, Travis K. Hughes, et al. "A Literature-Derived Knowledge Graph Augments the Interpretation of Single Cell RNA-seq Datasets." Genes 12, no. 6 (2021): 898. http://dx.doi.org/10.3390/genes12060898.

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Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have advanced rapidly in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedic
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Pham, Son, Tri Le, Tan Phan, et al. "484 Bioturing browser: interactively explore public single cell sequencing data." Journal for ImmunoTherapy of Cancer 8, Suppl 3 (2020): A520. http://dx.doi.org/10.1136/jitc-2020-sitc2020.0484.

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BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual dat
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Mao, Shunfu, Yue Zhang, Georg Seelig, and Sreeram Kannan. "CellMeSH: probabilistic cell-type identification using indexed literature." Bioinformatics 38, no. 5 (2021): 1393–402. http://dx.doi.org/10.1093/bioinformatics/btab834.

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Abstract Motivation Single-cell RNA sequencing (scRNA-seq) is widely used for analyzing gene expression in multi-cellular systems and provides unprecedented access to cellular heterogeneity. scRNA-seq experiments aim to identify and quantify all cell types present in a sample. Measured single-cell transcriptomes are grouped by similarity and the resulting clusters are mapped to cell types based on cluster-specific gene expression patterns. While the process of generating clusters has become largely automated, annotation remains a laborious ad hoc effort that requires expert biological knowledg
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Dissertations / Theses on the topic "Automatic cell types annotation"

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Raoux, Corentin. "Review and Analysis of single-cell RNA sequencing cell-type identification and annotation tools." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297852.

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Single-cell RNA-sequencing makes possible to study the gene expression at the level of individual cells. However, one of the main challenges of the single-cell RNA-sequencing analysis today, is the identification and annotation of cell types. The current method consists in manually checking the expression of genes using top differentially expressed genes and comparing them with related cell-type markers available in scientific publications. It is therefore time-consuming and labour intensive. Nevertheless, in the last two years,numerous automatic cell-type identification and annotation tools w
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Wedin, Mattias, and Isak Bengtsson. "A Comparative Study on Machine Learning Models for Automatic Classification of Cell Types from Digitally Reconstructed Neurons." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301744.

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For the last decade, the use of machine learning in neuroscientific research has become a popular topic. For instance, image recognition has been used together with machine learning to detect and also help improve the diagnostics of diseases. This study compares the accuracy of a Convolutional Neural Network (CNN), a support vector classifier and a random forest classifier to investigate which are better suited for classification of cell types based on digitally reconstructed images from mice. All models were trained on both a larger unbalanced dataset containing 49 different cell types and a
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Neves, João. "Automatic annotation of cellular data." Master's thesis, 2013. http://hdl.handle.net/10400.6/3696.

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Life scientists often need to count cells in microscopy images, which is very tedious and a time consuming task. Henceforth, automatic approaches can be a solution to this problem. Several works have been devised for this issue, but the majority of these approaches degrade their performance in case of cell overlapping. In this dissertation we propose a method to determine the position of macrophages and parasites in uorescence images of Leishmania-infected macrophages. The proposed strategy is mainly based on blob detection, clustering and separation using concave regions of the cells' contou
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Books on the topic "Automatic cell types annotation"

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Lüdeling, Anke, Julia Ritz, Manfred Stede, and Amir Zeldes. Corpus Linguistics and Information Structure Research. Edited by Caroline Féry and Shinichiro Ishihara. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199642670.013.013.

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This chapter describes the contributions that Corpus Linguistics (the study of linguistic phenomena by means of systematically exploiting collections of naturally-occurring linguistic data) can make to IS research. It discusses issues of designing a corpus that can serve as a basis for qualitative or quantitative studies, and then turns to the central issue of data annotation: what corpora are available that have been annotated with IS-related annotations, and how can such annotations be evaluated? In case a corpus does not have direct IS annotation, can other types of annotations, especially
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Book chapters on the topic "Automatic cell types annotation"

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Abida, Rabeb, and Anthony Cleve. "Improving the Usability of Tabular Data Through Data Annotation, Repair and Augmentation." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17030-0_6.

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AbstractIn recent years, a rapidly increasing amount of information has been made publicly available in tabular form on the Web. Many of these data are not usable due to their poor quality (e.g., misspelled or missing values, missing or incomplete metadata, and missing meaningful columns). Solutions have been proposed in the literature to address these data quality issues, but there is still a lack of all-in-one approaches that can fully solve them. Therefore, users need to use several methods to solve these data quality issues. In this paper, we present an all-in-one and automatic approach ca
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Storås, Andrea M., Michael A. Riegler, Trine B. Haugen, et al. "Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17030-0_9.

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AbstractThe in vitro fertilization procedure called intracytoplasmic sperm injection can be used to help fertilize an egg by injecting a single sperm cell directly into the cytoplasm of the egg. In order to evaluate, refine and improve the method in the fertility clinic, the procedure is usually observed at the clinic. Alternatively, a video of the procedure can be examined and labeled in a time-consuming process. To reduce the time required for the assessment, we propose an unsupervised method that automatically clusters video frames of the intracytoplasmic sperm injection procedure. Deep fea
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Busse, Beatrix. "Toward Developing a Procedure for Automatically Identifying Speech, Writing, and Thought Presentation." In Speech, Writing, and Thought Presentation in 19th-Century Narrative Fiction. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190212360.003.0006.

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The sixth chapter illustrates how the automatic annotation of the different modes of speech, writing, and thought presentation in 19th-century narrative fiction may be performed on the basis of repetitive lexico-grammatical features and by setting up rules based on the manual annotation of the corpus and facilitating it in larger data sets. The chapter proposes a number of formal diagnostic features for the identification of discourse presentation as well as procedures to help their automatic detection. The procedures described serve as basis for a tool for the automatic identification of disc
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Jan, Rafiya, and Afaq Alam Khan. "Emotion Mining Using Semantic Similarity." In Natural Language Processing. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0951-7.ch053.

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Social networks are considered as the most abundant sources of affective information for sentiment and emotion classification. Emotion classification is the challenging task of classifying emotions into different types. Emotions being universal, the automatic exploration of emotion is considered as a difficult task to perform. A lot of the research is being conducted in the field of automatic emotion detection in textual data streams. However, very little attention is paid towards capturing semantic features of the text. In this article, the authors present the technique of semantic relatednes
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Zhou, Xiangrong, and Hiroshi Fujita. "Automatic Organ Localization on X-Ray CT Images by Using Ensemble-Learning Techniques." In Machine Learning in Computer-Aided Diagnosis. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0059-1.ch019.

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Location of an inner organ in a CT image is the basic information that is required for medical image analysis such as image segmentation, lesion detection, content-based image retrieval, and anatomical annotation. A general approach/scheme for the localization of different inner organs that can be adapted to suit various types of medical image formats is required. However, this is a very challenging problem and can hardly be solved by using traditional image processing techniques. This chapter introduces an ensemble-learning-based approach that can be used to solve organ localization problems.
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Cheng Hung-Yi, Pai Tun-Wen, and Fujita Hamido. "An automatic structural detection system for alpha-solenoid repeats." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-522-7-157.

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Tandem repeat structures are widely distributed among all classes of proteins. Various basic structural units of repetitive nature possess functional diversity and reflect important influences on biological responses for different organisms. One of the most common types of protein repeat structure is the α-solenoid tandem repeat class which possesses low sequence similarity between any two repeat units within a structure. Therefore, a successful segmentation system for identifying each repeat unit cannot be achieved mainly based on sequence comparison approaches. For a comprehensive
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Ni Jian, Delaney Brian, and Florian Radu. "Fast Model Adaptation for Automated Section Classification in Electronic Medical Records." In Studies in Health Technology and Informatics. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-564-7-35.

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Medical information extraction is the automatic extraction of structured information from electronic medical records, where such information can be used for improving healthcare processes and medical decision making. In this paper, we study one important medical information extraction task called section classification. The objective of section classification is to automatically identify sections in a medical document and classify them into one of the pre-defined section types. Training section classification models typically requires large amounts of human labeled training data to achieve hig
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Singhal, Vanika, and Preety Singh. "Selected Shape and Texture Features for Automatic Detection of Acute Lymphoblastic Leukemia." In Biomedical Signal and Image Processing in Patient Care. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2829-6.ch009.

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Acute Lymphoblastic Leukemia is a cancer of blood caused due to increase in number of immature lymphocyte cells. Detection is done manually by skilled pathologists which is time consuming and depends on the skills of the pathologist. The authors propose a methodology for discrimination of a normal lymphocyte cell from a malignant one by processing the blood sample image. Automatic detection process will reduce the diagnosis time and not be limited by human interpretation. The lymphocyte images are classified based on two types of extracted features: shape and texture. To identify prominent sha
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Gustafsson, Mika, and Michael Hörnquist. "Integrating Various Data Sources for Improved Quality in Reverse Engineering of Gene Regulatory Networks." In Handbook of Research on Computational Methodologies in Gene Regulatory Networks. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-685-3.ch020.

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In this chapter we outline a methodology to reverse engineer GRNs from various data sources within an ODE framework. The methodology is generally applicable and is suitable to handle the broad error distribution present in microarrays. The main effort of this chapter is the exploration of a fully data driven approach to the integration problem in a “soft evidence” based way. Integration is here seen as the process of incorporation of uncertain a priori knowledge and is therefore only relied upon if it lowers the prediction error. An efficient implementation is carried out by a linear programmi
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Sujith, K. P., P. Vetrivelan, P. Prakasam, and T. R. Sureshkumar. "Computer-aided Diagnosis Model for White Blood Cell Leukemia and Myeloma Classification using Deep Convolutional Neural Network." In Advanced Computing Solutions for Healthcare. BENTHAM SCIENCE PUBLISHERS, 2025. https://doi.org/10.2174/9789815274134125010018.

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Diagnosing white blood cell (leukocyte) diseases (Leukemia and Myeloma) is a thought-provoking task in the body. The abnormal growth of the leukocytes leads to an unbalanced immune system. Therefore, the automatic detection and classification of leukocytes will be the best aiding tool for the physician. This research work proposes a Computer-aided Diagnosis (CAD) model using the Deep Convolutional Neural Network (DCNN) to classify the white blood cell Acute Myeloid Leukemia (AML), Acute lymphoblastic leukemia (ALL), Myeloma, and its sub-types. The Gaussian distribution and k-means clustering s
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Conference papers on the topic "Automatic cell types annotation"

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Zhai, Yuyao, Liang Chen, and Minghua Deng. "Distribution-Independent Cell Type Identification for Single-Cell RNA-seq Data." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/679.

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Automatic cell type annotation aims to transfer the label knowledge from label-abundant reference data to label-scarce target data, which makes encouraging progress in single-cell RNA-seq data analysis. While previous works have focused on classifying close-set cells and detecting open-set cells during testing, it is still essential to be able to classify unknown cell types as human beings. Additionally, few efforts have been devoted to addressing the challenge of common long-tail dilemma in cell type annotation data. Therefore, in this paper, we propose an innovative distribution-independent
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Bryant, Christopher, Mariano Felice, and Ted Briscoe. "Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction." In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/p17-1074.

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Zhai, Yuyao, Liang Chen, and Minghua Deng. "Realistic Cell Type Annotation and Discovery for Single-cell RNA-seq Data." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/552.

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The rapid development of single-cell RNA sequencing (scRNA-seq) technologies allows us to explore tissue heterogeneity at the cellular level. Cell type annotation plays an essential role in the substantial downstream analysis of scRNA-seq data. Existing methods usually classify the novel cell types in target data as an “unassigned” group and rarely discover the fine-grained cell type structure among them. Besides, these methods carry risks, such as susceptibility to batch effect between reference and target data, thus further compromising of inherent discrimination of target data. Considering
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Chowdhury, Aritra, Sujoy K. Biswas, and Simone Bianco. "Active deep learning reduces annotation burden in automatic cell segmentation." In Digital and Computational Pathology, edited by John E. Tomaszewski and Aaron D. Ward. SPIE, 2021. http://dx.doi.org/10.1117/12.2579537.

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Luo, Yumeng, Yuming Zhai, and Ying Qin. "FRETA-D: A Toolkit of Automatic Annotation of Grammatical and Phonetic Error Types in French Dictations." In 2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS). IEEE, 2022. http://dx.doi.org/10.1109/ccis57298.2022.10016326.

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Pochernina, Olga Leonidovna, Alexander Vladimirovich Khvostikov, and Andrey Serdjevich Krylov. "Semi-automatic Algorithm for Lumen Segmentation in Histological Images." In 32nd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2022. http://dx.doi.org/10.20948/graphicon-2022-648-656.

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In this paper we focus on a problem of lumen segmentation in histological images. A large number of annotated images are necessary for the development of diagnostic algorithms that can help to detect changes, such as lumen serration, indicating really serious health problems like cancer. We propose a semi-automatic interactive segmentation algorithm to accelerate the process of manual image annotation. The core of our annotation approach is a classical graph-cut algorithm that uses manually selected parameters. The user annotates an image with two types of scribbles corresponding to the gland
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Lin, Lu, Wen Xue, Xindian Wei, et al. "SCTrans: Multi-scale scRNA-seq Sub-vector Completion Transformer for Gene-selective Cell Type Annotation." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/658.

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Cell type annotation is pivotal to single-cell RNA sequencing data (scRNA-seq)-based biological and medical analysis, e.g., identifying biomarkers, exploring cellular heterogeneity, and understanding disease mechanisms. The previous annotation methods typically learn a nonlinear mapping to infer cell type from gene expression vectors, and thus fall short in discovering and associating salient genes with specific cell types. To address this issue, we propose a multi-scale scRNA-seq Sub-vector Completion Transformer, and our model is referred to as SCTrans. Considering that the expressiveness of
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Caines, Andrew, Helen Yannakoudakis, Helen Allen, Pascual Pérez-Paredes, Bill Byrne, and Paula Buttery. "The Teacher-Student Chatroom Corpus version 2: more lessons, new annotation, automatic detection of sequence shifts." In 11th Workshop on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL 2022). Linköping University Electronic Press, 2022. http://dx.doi.org/10.3384/ecp190003.

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The first version of the Teacher-Student Chatroom Corpus (TSCC) was released in 2020 and contained 102 chatroom dialogues between 2 teachers and 8 learners of English, amounting to 13.5K conversational turns and 133K word tokens. In this second version of the corpus, we release an additional 158 chatroom dialogues, amounting to an extra 27.9K conversational turns and 230K word tokens. In total there are now 260 chatroom lessons, 41.4K conversational turns and 363K word tokens, involving 2 teachers and 13 students with seven different first languages. The content of the lessons was, as before,
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Dyachkov, V. V., I. A. Khomchenkova, P. S. Pleshak, and N. M. Stoynova. "ANNOTATING AND EXPLORING CODE-SWITCHING IN FOUR CORPORA OF MINORITY LANGUAGES OF RUSSIA." In International Conference on Computational Linguistics and Intellectual Technologies "Dialogue". Russian State University for the Humanities, 2020. http://dx.doi.org/10.28995/2075-7182-2020-19-228-240.

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This paper describes code-switching with Russian in four spoken corpora of minority languages of Russia: two Uralic ones (Hill Mari and Moksha) and two Tungusic ones (Nanai and Ulch). All narrators are bilinguals, fluent both in the indigenous language (IL) and in Russian; all the corpora are comparable in size and genres (small field collections of spontaneous oral texts, produced under the instruction to speak IL); the languages are comparable in structural (dis)similarity with Russian. The only difference concerns language dominance and the degree of language shift across the communities. T
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Tawil, Ibrahim H., Albashir K. Elfaqih, Bsebsu Farag Muftah, and Ezuldeen B. Abraheem. "Performance Comparison Study of Solid Oxide Fuel Cell With Direct Oxidation in Three Fuel Types." In 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA). IEEE, 2022. http://dx.doi.org/10.1109/mi-sta54861.2022.9837702.

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Reports on the topic "Automatic cell types annotation"

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Xu, Chao, Walter Forkel, Stefan Borgwardt, Franz Baader, and Beihai Zhou. Automatic Translation of Clinical Trial Eligibility Criteria into Formal Queries. Technische Universität Dresden, 2019. http://dx.doi.org/10.25368/2023.224.

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Selecting patients for clinical trials is very labor-intensive. Our goal is to develop an automated system that can support doctors in this task. This paper describes a major step towards such a system: the automatic translation of clinical trial eligibility criteria from natural language into formal, logic-based queries. First, we develop a semantic annotation process that can capture many types of clinical trial criteria. Then, we map the annotated criteria to the formal query language. We have built a prototype system based on state-of-the-art NLP tools such as Word2Vec, Stanford NLP tools,
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