Dataset Information

CryGetter: a tool to automate retrieval and analysis of Cry protein data.

ABSTRACT: For many years, the use of chemical agents to control crop pests has been degrading the environment, bringing problems to humans and all living things. An alternative to deal with the pests is the use of biopesticides, biological agents capable of controlling these harmful organisms. One kind of biopesticide is Bacillus thuringiensis, a Gram-positive bacterium that synthesizes a protein that, when ingested by the pests, kills them and does not harm other species.Since the economical importance of Bacillus thuringiensis and its proteins significance, this work presents a software tool, called CryGetter, that is capable of retrieving data related to these proteins, store it and present it in a user friendly manner. The tool also aims to align the protein sequences and generate reports containing some statistical data concerning the alignments that were made.CryGetter was created to help researchers of Bacillus thuringiensis and its proteins to speed up their data retrieval and analysis, allowing them to generate more accurate results. In this sense, the tool circumvents the error prone task of manually getting all the necessary data and processing them in various software systems to get the same result as CryGetter gets in a unique semiautomatic environment.

SUBMITTER: Buzatto D

PROVIDER: S-EPMC5004295 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CryGetter: a tool to automate retrieval and analysis of Cry protein data.

Buzatto David D de Castro França Suzelei S Zingaretti Sônia Marli SM

BMC bioinformatics 20160830 1

<h4>Background</h4>For many years, the use of chemical agents to control crop pests has been degrading the environment, bringing problems to humans and all living things. An alternative to deal with the pests is the use of biopesticides, biological agents capable of controlling these harmful organisms. One kind of biopesticide is Bacillus thuringiensis, a Gram-positive bacterium that synthesizes a protein that, when ingested by the pests, kills them and does not harm other species.<h4>Results</h ...[more]

PMID: 27578522

Similar Datasets

Project description:BackgroundEvidence-based medicine requires synthesis of research through rigorous and time-intensive systematic literature reviews (SLRs), with significant resource expenditure for data extraction from scientific publications. Machine learning may enable the timely completion of SLRs and reduce errors by automating data identification and extraction.MethodsWe evaluated the use of machine learning to extract data from publications related to SLRs in oncology (SLR 1) and Fabry disease (SLR 2). SLR 1 predominantly contained interventional studies and SLR 2 observational studies. Predefined key terms and data were manually annotated to train and test bidirectional encoder representations from transformers (BERT) and bidirectional long-short-term memory machine learning models. Using human annotation as a reference, we assessed the ability of the models to identify biomedical terms of interest (entities) and their relations. We also pretrained BERT on a corpus of 100,000 open access clinical publications and/or enhanced context-dependent entity classification with a conditional random field (CRF) model. Performance was measured using the F1 score, a metric that combines precision and recall. We defined successful matches as partial overlap of entities of the same type.ResultsFor entity recognition, the pretrained BERT+CRF model had the best performance, with an F1 score of 73% in SLR 1 and 70% in SLR 2. Entity types identified with the highest accuracy were metrics for progression-free survival (SLR 1, F1 score 88%) or for patient age (SLR 2, F1 score 82%). Treatment arm dosage was identified less successfully (F1 scores 60% [SLR 1] and 49% [SLR 2]). The best-performing model for relation extraction, pretrained BERT relation classification, exhibited F1 scores higher than 90% in cases with at least 80 relation examples for a pair of related entity types.ConclusionsThe performance of BERT is enhanced by pretraining with biomedical literature and by combining with a CRF model. With refinement, machine learning may assist with manual data extraction for SLRs.

Dataset Information

CryGetter: a tool to automate retrieval and analysis of Cry protein data.

Publications

CryGetter: a tool to automate retrieval and analysis of Cry protein data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets