scholarly journals PubData: search engine for bioinformatics databases worldwide

2016 ◽  
Author(s):  
Bohdan B. Khomtchouk ◽  
Kasra A. Vand ◽  
Thor Wahlestedt ◽  
Kelly Khomtchouk ◽  
Mohammed K. Sayed ◽  
...  
Keyword(s):  
Search Engine ◽  
Language Processing ◽  
Biomedical Literature ◽  
Biomedical Data ◽  
File Transfer ◽  
Link Type ◽  
Bioinformatics Databases ◽  
Cross Platform ◽  
File Retrieval ◽  
User Friendly

AbstractWe propose a search engine and file retrieval system for all bioinformatics databases worldwide. PubData searches biomedical data in a user-friendly fashion similar to how PubMed searches biomedical literature. PubData is built on novel network programming, natural language processing, and artificial intelligence algorithms that can patch into the file transfer protocol servers of any user-specified bioinformatics database, query its contents, retrieve files for download, and adapt to the user’s search preferences.PubData is hosted as a user-friendly, cross-platform graphical user interface program developed using PyQt: http://www.pubdata.bio. The methods are implemented in Python, and are available as part of the PubData project at: https://github.com/Bohdan-Khomtchouk/PubData.

scholarly journals Pydigree: a python library for manipulation and forward-time simulation and of genetic datasets

2017 ◽  
Author(s):  
James E. Hicks
Keyword(s):  
Population Genetics ◽  
Data Structures ◽  
Genetic Epidemiology ◽  
Genetic Data ◽  
Link Type ◽  
File Formats ◽  
Time Simulation ◽  
Cross Platform ◽  
User Friendly ◽  
Python Package

AbstractThe development of software for working with data from population genetics or genetic epidemiology often requires substantial time spent implementing common procedures. Pydigree is a cross-platform Python 3 library that contains efficient, user friendly implementations for many of these common functions, and support for input from common file formats. Developers can combine the functions and data structures to rapidly implement programs handling genetic data. Pydigree presents a useful environment for development of applications for genetic data or rapid prototyping before reimplementation in a higher-performance language.Pydigree is freely available under an open source license. Stable sources can be found in the Python Package Index at https://pypi.python.org/pypi/pydigree/, and development sources can be downloaded at https://github.com/jameshicks/pydigree/

Natural Language Processing–Assisted Literature Retrieval and Analysis for Combination Therapy in Cancer

2022 ◽  
Author(s):  
Jia Zeng ◽  
Christian X. Cruz-Pico ◽  
Turçin Saridogan ◽  
Md Abu Shufean ◽  
Michael Kahle ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Combination Therapy ◽  
Natural Language ◽  
Search Engine ◽  
Language Processing ◽  
Biomedical Literature ◽  
Antitumor Efficacy ◽  
Domain Specific ◽  
Validation Experiment

PURPOSE Despite advances in molecular therapeutics, few anticancer agents achieve durable responses. Rational combinations using two or more anticancer drugs have the potential to achieve a synergistic effect and overcome drug resistance, enhancing antitumor efficacy. A publicly accessible biomedical literature search engine dedicated to this domain will facilitate knowledge discovery and reduce manual search and review. METHODS We developed RetriLite, an information retrieval and extraction framework that leverages natural language processing and domain-specific knowledgebase to computationally identify highly relevant papers and extract key information. The modular architecture enables RetriLite to benefit from synergizing information retrieval and natural language processing techniques while remaining flexible to customization. We customized the application and created an informatics pipeline that strategically identifies papers that describe efficacy of using combination therapies in clinical or preclinical studies. RESULTS In a small pilot study, RetriLite achieved an F 1 score of 0.93. A more extensive validation experiment was conducted to determine agents that have enhanced antitumor efficacy in vitro or in vivo with poly (ADP-ribose) polymerase inhibitors: 95.9% of the papers determined to be relevant by our application were true positive and the application's feature of distinguishing a clinical paper from a preclinical paper achieved an accuracy of 97.6%. Interobserver assessment was conducted, which resulted in a 100% concordance. The data derived from the informatics pipeline have also been made accessible to the public via a dedicated online search engine with an intuitive user interface. CONCLUSION RetriLite is a framework that can be applied to establish domain-specific information retrieval and extraction systems. The extensive and high-quality metadata tags along with keyword highlighting facilitate information seekers to more effectively and efficiently discover knowledge in the combination therapy domain.

scholarly journals DataMed – an open source discovery index for finding biomedical datasets

2018 ◽  
Vol 25 (3) ◽  
pp. 300-308 ◽  
Author(s):  
Xiaoling Chen ◽  
Anupama E Gururaj ◽  
Burak Ozyurt ◽  
Ruiling Liu ◽  
Ergin Soysal ◽  
...  
Keyword(s):  
Open Source ◽  
Search Engine ◽  
Language Processing ◽  
Data Reuse ◽  
Biomedical Domain ◽  
Biomedical Data ◽  
Data Discovery ◽  
Healthcare Data ◽  
Core Elements ◽  
Data Ingestion

Abstract Objective Finding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data. Here we describe the development of an open source biomedical data discovery system called DataMed, with the goal of promoting the building of additional data indexes in the biomedical domain. Materials and Methods DataMed, which can efficiently index and search diverse types of biomedical datasets across repositories, is developed through the National Institutes of Health–funded biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium. It consists of 2 main components: (1) a data ingestion pipeline that collects and transforms original metadata information to a unified metadata model, called DatA Tag Suite (DATS), and (2) a search engine that finds relevant datasets based on user-entered queries. In addition to describing its architecture and techniques, we evaluated individual components within DataMed, including the accuracy of the ingestion pipeline, the prevalence of the DATS model across repositories, and the overall performance of the dataset retrieval engine. Results and Conclusion Our manual review shows that the ingestion pipeline could achieve an accuracy of 90% and core elements of DATS had varied frequency across repositories. On a manually curated benchmark dataset, the DataMed search engine achieved an inferred average precision of 0.2033 and a precision at 10 (P@10, the number of relevant results in the top 10 search results) of 0.6022, by implementing advanced natural language processing and terminology services. Currently, we have made the DataMed system publically available as an open source package for the biomedical community.

scholarly journals Developing a More Accurate Biomedical Literature Retrieval Method using Deep Learning and Citations in PubMed Central Full-text Articles

2021 ◽  
Author(s):  
Chun-chao Lo ◽  
Shubo Tian ◽  
Yuchuan Tao ◽  
Jie Hao ◽  
Jinfeng Zhang
Keyword(s):  
Deep Learning ◽  
Search Engine ◽  
Language Processing ◽  
Full Text ◽  
Question Answering ◽  
Biomedical Literature ◽  
Specific Information ◽  
Retrieval Method ◽  
Pubmed Central ◽  
Learning Techniques

Most queries submitted to a literature search engine can be more precisely written as sentences to give the search engine more specific information. Sentence queries should be more effective, in principle, than short queries with small numbers of keywords. Querying with full sentences is also a key step in question-answering and citation recommendation systems. Despite the considerable progress in natural language processing (NLP) in recent years, using sentence queries on current search engines does not yield satisfactory results. In this study, we developed a deep learning-based method for sentence queries, called DeepSenSe, using citation data available in full-text articles obtained from PubMed Central (PMC). A large amount of labeled data was generated from millions of matched citing sentences and cited articles, making it possible to train quality predictive models using modern deep learning techniques. A two-stage approach was designed: in the first stage we used a modified BM25 algorithm to obtain the top 1000 relevant articles; the second stage involved re-ranking the relevant articles using DeepSenSe. We tested our method using a large number of sentences extracted from real scientific articles in PMC. Our method performed substantially better than PubMed and Google Scholar for sentence queries.

scholarly journals CovidNLP: A Web Application for Distilling Systemic Implications of COVID-19 Pandemic with Natural Language Processing

2020 ◽  
Author(s):  
Raghav Awasthi ◽  
Ridam Pal ◽  
Pradeep Singh ◽  
Aditya Nagori ◽  
Suryatej Reddy ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Web Application ◽  
Data Availability ◽  
Research Articles ◽  
Global Literature ◽  
Link Type ◽  
Novel Coronavirus ◽  
User Friendly

AbstractThe flood of conflicting COVID-19 research has revealed that COVID-19 continues to be an enigma. Although more than 14,000 research articles on COVID-19 have been published with the disease taking a pandemic proportion, clinicians and researchers are struggling to distill knowledge for furthering clinical management and research. In this study, we address this gap for a targeted user group, i.e. clinicians, researchers, and policymakers by applying natural language processing to develop a CovidNLP dashboard in order to speed up knowledge discovery. The WHO has created a repository of about more than 5000 peer-reviewed and curated research articles on varied aspects including epidemiology, clinical features, diagnosis, treatment, social factors, and economics. We summarised all the articles in the WHO Database through an extractive summarizer followed by an exploration of the feature space using word embeddings which were then used to visualize the summarized associations of COVID-19 as found in the text. Clinicians, researchers, and policymakers will not only discover the direct effects of COVID-19 but also the systematic implications such as the anticipated rise in TB and cancer mortality due to the non-availability of drugs during the export lockdown as highlighted by our models. These demonstrate the utility of mining massive literature with natural language processing for rapid distillation and knowledge updates. This can help the users understand, synthesize, and take pre-emptive action with the available peer-reviewed evidence on COVID-19. Our models will be continuously updated with new literature and we have made our resource CovidNLP publicly available in a user-friendly fashion at http://covidnlp.tavlab.iiitd.edu.in/.Data Availability StatementAll the data used in this study are publicly available from the WHO Covid-19 Global Literature on coronavirus disease maintained at https://search.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/. Our analysis and the interactive resource CovidNLP is publicly available in a user friendly fashion at http://covidnlp.tavlab.iiitd.edu.in

scholarly journals Biologically-oriented mud volcano database: muddy_db

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12463
Author(s):  
Alexei Remizovschi ◽  
Rahela Carpa
Keyword(s):  
Language Processing ◽  
R Package ◽  
Mud Volcanoes ◽  
Processing Pipeline ◽  
Link Type ◽  
Naturally Occurring ◽  
R Shiny ◽  
Microbial Taxonomy ◽  
Web App ◽  
User Friendly

Mud volcanoes (MVs) are naturally occurring hydrocarbon hotbeds with continuous methane discharge, contributing to global warming. They host microbial communities adapted to hydrocarbon oxidation. Given their research value, MVs still represent a niche topic in microbiology and are neglected by hydrocarbon-oriented research. All the data regarding MVs is sporadic and decentralized. To mitigate this problem, we built a custom Natural Language Processing pipeline (muddy_mine), and collected all the available MV data from open-access articles. Based on this data, we built the muddy_db database. The muddy_db represents the first biologically oriented database rendered as a user-friendly web app. This database includes all the relevant MV data, ranging from microbial taxonomy to hydrocarbon occurrence and geology. The muddy_mine and muddy_db tools are licensed under the GPLv3. muddy_db R Shiny web app: https://muddy-db.shinyapps.io/muddy_db/ muddy_db R package: https://github.com/TracyRage/muddy_db muddy_mine Conda package: https://github.com/TracyRage/muddy_mine.

scholarly journals Biochat: a database for natural language processing of Gene Expression Omnibus data

2018 ◽  
Author(s):  
Bohdan B. Khomtchouk ◽  
Vsevolod Dyomkin ◽  
Kasra A. Vand ◽  
Themistocles Assimes ◽  
Or Gozani
Keyword(s):  
Gene Expression ◽  
Language Processing ◽  
Web Application ◽  
Gene Expression Omnibus ◽  
Omics Data ◽  
Textual Information ◽  
Link Type ◽  
Different Types ◽  
User Friendly ◽  
Omics Data Integration

AbstractA biological dataset’s metadata profile (e.g., study description, organism name, sequencing type, etc.) typically contains terse but descriptive textual information that can be used to link it with other similar biological datasets for the purpose of integrating omics data of different types to inform hypotheses and biological questions. Here we present Biochat, a database containing a multi-omics data integration support system to aid in cross-linking Gene Expression Omnibus (GEO) records to each other by metadata similarity through a user-friendly web application. Biochat is publicly available at: http://www.biochat.ai. Biochat source code is hosted at: https://github.com/Bohdan-Khomtchouk/Bio-chat.Database URLhttps://github.com/Bohdan-Khomtchouk/Bio-chat

Pedigree and Pedigree Import Wizard

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 552g-553
Author(s):  
Shahrokh Khandizadeh
Keyword(s):  
Additional Data ◽  
File Format ◽  
Fruit Crops ◽  
Operating Environment ◽  
Agronomic Characteristics ◽  
Link Type ◽  
Plant Characteristics ◽  
User Friendly

Pedigree for Windows is a user-friendly program that allows the user to trace agronomic characteristics, draw pedigrees, and view images of several fruit crops, including more than 1400 apple, 800 strawberry, 800 almond, 100 blackberry, 80 blueberry, 790 pear, 200 raspberry examples. Pedigree Import Wizard®© for Windows is an add-on software for users who are interested in importing their research or breeding data records of fruit, flower, and plant characteristics and any related images into Pedigree for Windows. Pedigree for Windows and Pedigree Import Wizard have been designed so that a user familiar with the Windows operating environment should have little need to refer to the documentation provided with the program. Pedigree Import Wizard uses a comma-separated value (csv) file format under the MS Excel environment. This option allows the user to add or import additional data to the existing database that are already stored in other software such as Lotus, Excel, Access, QuattroPro, WordPerfect, and MS Word tables, etc., as long as they work under the Windows environment. A free demo version of Pedigree and Pedigree Import Wizard for Windows is available from http://www.pgris.com.

scholarly journals PathScore: a web tool for identifying altered pathways in cancer data

2016 ◽  
Author(s):  
Stephen G. Gaffney ◽  
Jeffrey P. Townsend
Keyword(s):  
Web Application ◽  
Somatic Mutations ◽  
Supplementary Information ◽  
Web Tool ◽  
Cancer Data ◽  
Link Type ◽  
Novel Approach ◽  
Supplementary Material ◽  
User Friendly ◽  
Pathway Effect

ABSTRACTSummaryPathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects.Availability and ImplementationWeb application available at pathscore.publichealth.yale.edu. Site implemented in Python and MySQL, with all major browsers supported. Source code available at github.com/sggaffney/pathscore with a GPLv3 [email protected] InformationAdditional documentation can be found at http://pathscore.publichealth.yale.edu/faq.

scholarly journals COVID-19 preVIEW: Semantic Search to Explore COVID-19 Research Preprints

2021 ◽  
Author(s):  
Lisa Langnickel ◽  
Roman Baum ◽  
Johannes Darms ◽  
Sumit Madan ◽  
Juliane Fluck
Keyword(s):  
Search Engine ◽  
Access Point ◽  
Semantic Search ◽  
Web Interface ◽  
Disease Trajectory ◽  
Human Genes ◽  
Central Access ◽  
Semantic Information Retrieval ◽  
User Friendly ◽  
Semantic Search Engine

During the current COVID-19 pandemic, the rapid availability of profound information is crucial in order to derive information about diagnosis, disease trajectory, treatment or to adapt the rules of conduct in public. The increased importance of preprints for COVID-19 research initiated the design of the preprint search engine preVIEW. Conceptually, it is a lightweight semantic search engine focusing on easy inclusion of specialized COVID-19 textual collections and provides a user friendly web interface for semantic information retrieval. In order to support semantic search functionality, we integrated a text mining workflow for indexing with relevant terminologies. Currently, diseases, human genes and SARS-CoV-2 proteins are annotated, and more will be added in future. The system integrates collections from several different preprint servers that are used in the biomedical domain to publish non-peer-reviewed work, thereby enabling one central access point for the users. In addition, our service offers facet searching, export functionality and an API access. COVID-19 preVIEW is publicly available at https://preview.zbmed.de.

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