Exploring Text Mining for Recent Consumer and Sensory Studies about Alternative Proteins.
Ontology highlight
ABSTRACT:
SUBMITTER: Chen Z
PROVIDER: S-EPMC8620912 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The current dataset is obtained by text mining of ?-hydroxybutyrate (BHB) supplement products' consumer online reviews. The text data of 71 BHB products' consumer reviews were extracted with the aid of the Web Scraper Chrome extension. Then, a lexicon-based sentiment analysis approach was developed to classify the sentiment or polarity of BHB products' consumer reviews. Both word-level and sentence-level sentiment analyses were conducted to score the analyzed text snippets. In terms of word-level sentiment analysis, word clouds of selected BHB products' reviews were generated to give direct observation, and the statistics of high-frequent sentiment words were listed for comparison. In terms of sentence-level sentiment analysis, two factors such as flavor and package were taken into consideration to map the products' polarity distributions. Besides, the complex analysis provides us with the basic statistics of the analyzed BHB customer reviews data.
| S-EPMC7083782 | biostudies-literature
Project description:Moonlighting proteins is an emerging concept for considering protein functions, which indicate proteins with two or more independent and distinct functions. An increasing number of moonlighting proteins have been reported in the past years; however, a systematic study of the topic has been hindered because the secondary functions of proteins are usually found serendipitously by experiments. Toward systematic identification and study of moonlighting proteins, computational methods for identifying moonlighting proteins from several different information sources, database entries, literature, and large-scale omics data have been developed. In this study, an overview for finding moonlighting proteins is discussed. Then, the literature-mining method, DextMP, is applied to find new moonlighting proteins in three genomes, Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster. Potential moonlighting proteins identified by DextMP are further examined by a two-step manual literature checking procedure, which finally yielded 13 new moonlighting proteins. Identified moonlighting proteins are categorized into two classes based on the clarity of the distinctness of two functions of the proteins. A few cases of the identified moonlighting proteins are described in detail. Further direction for improving the DextMP algorithm is also discussed.
| S-EPMC6404977 | biostudies-literature
Project description:We present Dragon TF Association Miner (DTFAM), a system for text-mining of PubMed documents for potential functional association of transcription factors (TFs) with terms from Gene Ontology (GO) and with diseases. DTFAM has been trained and tested in the selection of relevant documents on a manually curated dataset containing >3000 PubMed abstracts relevant to transcription control. On our test data the system achieves sensitivity of 80% with specificity of 82%. DTFAM provides comprehensive tabular and graphical reports linking terms to relevant sets of documents. These documents are color-coded for easier inspection. DTFAM complements the existing biological resources by collecting, assessing, extracting and presenting associations that can reveal some of the not so easily observable connections among the entities found which could explain the functions of TFs and help decipher parts of gene transcriptional regulatory networks. DTFAM summarizes information from a large volume of documents saving time and making analysis simpler for individual users. DTFAM is freely available for academic and non-profit users at http://research.i2r.a-star.edu.sg/DRAGON/TFAM/.
| S-EPMC441622 | biostudies-literature
Project description:Single-cultivar juices may be a valuable way to introduce different versions of a product to the market and obtain price discrimination. To communicate a product's value, complex characteristics incorporated by each cultivar must be identified. New sensory methods rely on the assessor's ability to recall attributes; however, the use of objective vocabularies may improve the sensory profiling. This work aimed to profile monovarietal apple juices by using projective mapping (PM) combined with ultra-flash profiling (UFP) supported by a sensory wheel built with a text-mining tool. Samples were also analyzed for physicochemical parameters to provide more information to the assessment. The assessor coordinates from PM were used in multiple factor analysis with confidence ellipses to assess differences among samples. A goodness-of-fit test was applied to select the most meaningful descriptors generated through the UFP test by calculating the expected frequency of choosing a descriptor from the sensory wheel and comparing it with the observed values. The methodology provided a more accurate sensory profile compared to previous research on fresh apples and juices. Elstar, Jonagold, and Pinova were considered as sweet juices, and Gravensteiner was described as sour and astringent, with green-apple notes. Rubinette was described as having a strong taste and cloudy aspect.
| S-EPMC6963934 | biostudies-literature
Project description:BACKGROUND: Decoding transcriptional regulatory networks and the genomic cis-regulatory logic implemented in their control nodes is a fundamental challenge in genome biology. High-throughput computational and experimental analyses of regulatory networks and sequences rely heavily on positive control data from prior small-scale experiments, but the vast majority of previously discovered regulatory data remains locked in the biomedical literature. RESULTS: We develop text-mining strategies to identify relevant publications and extract sequence information to assist the regulatory annotation process. Using a vector space model to identify Medline abstracts from papers likely to have high cis-regulatory content, we demonstrate that document relevance ranking can assist the curation of transcriptional regulatory networks and estimate that, minimally, 30,000 papers harbor unannotated cis-regulatory data. In addition, we show that DNA sequences can be extracted from primary text with high cis-regulatory content and mapped to genome sequences as a means of identifying the location, organism and target gene information that is critical to the cis-regulatory annotation process. CONCLUSION: Our results demonstrate that text-mining technologies can be successfully integrated with genome annotation systems, thereby increasing the availability of annotated cis-regulatory data needed to catalyze advances in the field of gene regulation.
| S-EPMC2374703 | biostudies-other
Project description:A projected global population growth by 2050 and climate change crises have led to increasing demand in edible protein sources; thus, scientific research and food industries are searching for alternatives. In this study, we investigated the incorporation of plant- and insect-based protein sources in wheat-based formulations. The Alveographic properties of dough and the effects on bread physicochemical and sensory characteristics were analysed. Including pea protein or insect powder improved the nutritional value, increasing protein content, but influenced the dough and bread properties. Pea protein significantly increased the dough extensibility (L), tenacity (P), and their ratio (P/L) in dough with insect blends and the control. Bread texture properties were significantly affected by the addition of pea and insect flour. Higher amounts of pea protein incorporation increased hardness values and showed a mean cell area lower than the control bread. Crust colour analysis showed significant differences concerning the control bread, while crumb colour was affected by the flour colour. Word association analysis showed insect bread was associated with an emotional dimension, wheat bread was linked with "tradition", and pea bread was associated with "fruit and vegetable".
| S-EPMC7404644 | biostudies-literature
Project description:INTRODUCTION:Inhibitory sensory gating of the P50 cerebral evoked potential to paired auditory stimuli (S1, S2) is a widely used paradigm for the study of schizophrenia and related conditions. Its use to measure genetic, treatment, and developmental effects requires a metric with more stable properties than the simple ratio of the paired responses. METHODS:This study assessed the ratio P50S2μV/P50S1μV and P50S2μV co-varied for P50S1μV in all 27 independent published studies that compared schizophrenia patients with healthy controls from 2000 to 2019. The largest study from each research group was selected. The Colorado research group's studies were excluded to eliminate bias from the first report of the phenomenon. RESULTS:Across the 27 studies encompassing 1179 schizophrenia patients and 1091 healthy controls, both P50S2μV co-varied for P50S1μV and P50S2μV/P50S1μV significantly separated the patients from the controls (both P < 0.0001). Effect size for P50S2μV co-varied for P50S1μV is d' = 1.23. The normal distribution of P50S2μV co-varied for P50S1μV detected influences of maternal inflammation and effects on behavior in a recent developmental study, an emerging use for the P50 inhibitory gating measure. P50S2μV/P50S1μV was not normally distributed. Results from two multi-site NIMH genetics collaborations also support the use of P50S2μV as a biomarker. CONCLUSION:Both methods detect an abnormality of cerebral inhibition in schizophrenia with high significance across multiple independent laboratories. The normal distribution of P50S2μV co-varied for P50S1μV makes it more suitable for studies of genetic, treatment, and other influences on the development and expression of inhibitory deficits in schizophrenia.
| S-EPMC7299819 | biostudies-literature
Project description:BackgroundCOVID-19 is still rampant all over the world. Until now, the COVID-19 vaccine is the most promising measure to subdue contagion and achieve herd immunity. However, public vaccination intention is suboptimal. A clear division lies between medical professionals and laypeople. While most professionals eagerly promote the vaccination campaign, some laypeople exude suspicion, hesitancy, and even opposition toward COVID-19 vaccines.ObjectiveThis study aims to employ a text mining approach to examine expression differences and thematic disparities between the professionals and laypeople within the COVID-19 vaccine context.MethodsWe collected 3196 answers under 65 filtered questions concerning the COVID-19 vaccine from the China-based question and answer forum Zhihu. The questions were classified into 5 categories depending on their contents and description: adverse reactions, vaccination, vaccine effectiveness, social implications of vaccine, and vaccine development. Respondents were also manually coded into two groups: professional and laypeople. Automated text analysis was performed to calculate fundamental expression characteristics of the 2 groups, including answer length, attitude distribution, and high-frequency words. Furthermore, structural topic modeling (STM), as a cutting-edge branch in the topic modeling family, was used to extract topics under each question category, and thematic disparities were evaluated between the 2 groups.ResultsLaypeople are more prevailing in the COVID-19 vaccine-related discussion. Regarding differences in expression characteristics, the professionals posted longer answers and showed a conservative stance toward vaccine effectiveness than did laypeople. Laypeople mentioned countries more frequently, while professionals were inclined to raise medical jargon. STM discloses prominent topics under each question category. Statistical analysis revealed that laypeople preferred the "safety of Chinese-made vaccine" topic and other vaccine-related issues in other countries. However, the professionals paid more attention to medical principles and professional standards underlying the COVID-19 vaccine. With respect to topics associated with the social implications of vaccines, the 2 groups showed no significant difference.ConclusionsOur findings indicate that laypeople and professionals share some common grounds but also hold divergent focuses toward the COVID-19 vaccine issue. These incongruities can be summarized as "qualitatively different" in perspective rather than "quantitatively different" in scientific knowledge. Among those questions closely associated with medical expertise, the "qualitatively different" characteristic is quite conspicuous. This study boosts the current understanding of how the public perceives the COVID-19 vaccine, in a more nuanced way. Web-based question and answer forums are a bonanza for examining perception discrepancies among various identities. STM further exhibits unique strengths over the traditional topic modeling method in statistically testing the topic preference of diverse groups. Public health practitioners should be keenly aware of the cognitive differences between professionals and laypeople, and pay special attention to the topics with significant inconsistency across groups to build consensus and promote vaccination effectively.
| S-EPMC8404777 | biostudies-literature
Project description:The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply proceed to the solution based on similarity to the existing, previously determined structures. A similar major paradigm shift is emerging due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction. Automated text mining has been widely used in recreating protein interaction networks, as well as in detecting small ligand binding sites on protein structures. Combining and expanding these two well-developed areas of research, we applied the text mining to structural modeling of protein-protein complexes (protein docking). Protein docking can be significantly improved when constraints on the docking mode are available. We developed a procedure that retrieves published abstracts on a specific protein-protein interaction and extracts information relevant to docking. The procedure was assessed on protein complexes from Dockground (http://dockground.compbio.ku.edu). The results show that correct information on binding residues can be extracted for about half of the complexes. The amount of irrelevant information was reduced by conceptual analysis of a subset of the retrieved abstracts, based on the bag-of-words (features) approach. Support Vector Machine models were trained and validated on the subset. The remaining abstracts were filtered by the best-performing models, which decreased the irrelevant information for ~ 25% complexes in the dataset. The extracted constraints were incorporated in the docking protocol and tested on the Dockground unbound benchmark set, significantly increasing the docking success rate.
| S-EPMC4674139 | biostudies-literature
Project description:Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies.
| S-EPMC5975701 | biostudies-literature