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15 results for “LDA”
Dataset for Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients
<p>This dataset contains all the spectra used in the paper "Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients", plus the R code to import the TXT (ASCII) files into a dataset, preprocess data, set-up and cross validate the PCA-LDA model and generate the figures shown in the paper.</p> <p>Data are available in 2 different formats: </p> <p>- 1 compressed archive ("dataset.zip") containing all the 144 TXT files (1 file = 1 spectrum) </p> <p>- 1 single CSV file (“dataset.csv”) with all the 144 spectra in the form of a table. The data are structured as follow, with each row being 1 spectrum, preceded by metadata: "acquisition_date", "substrate_batch", "class", "sample_code".</p> <p>The code for R is available as a single file "Rcode.R".</p> <p> </p>
Preprocessed Tweets for LDA with defined Topics
<p>This file contains preprocessed tweets from the #BTW17 Twitter Dataset (Kratzke, 2017). With this tweets, the <code>btw17_twitter_lda.ipynb</code> file from https://github.com/matteomeier/topics-tweets-querysuggestions-btw17 can be run to recreate the results of the analysis for the Bachelor Thesis <strong>Topic-Analyse politischer Tweets und Suchvorschläge zur Bundestagswahl 2017.</strong></p>
FUN-LDA scores for human genome assembly GRCh37
<p>FUN-LDA is based on a Latent Dirichlet Allocation (LDA) model for predicting functional effects of non-coding genetic variants in a cell type and tissue-specific way by integrating diverse epigenetic annotations for specific cell types and tissues from large-scale genomics projects such as ENCODE and Roadmap Epigenomics. Using this unsupervised approach, we predict tissue-specific functional effects for every position in the human genome for 127 tissues and cell types in ENCODE and Roadmap Epigenomics. This <a href="https://docs.google.com/spreadsheet/ccc?key=0Am6FxqAtrFDwdHU1UC13ZUxKYy1XVEJPUzV6MEtQOXc&usp=sharing">online spreadsheet</a> includes the information about the 127 Roadmap tissues in detail.</p> <p><strong>Format</strong></p> <p>The FUN-LDA scores are stored in the UCSC Genome Browser bigWig Track Format. </p> <p>To extract FUN-LDA scores, the bigWigAverageOverBed utility is required. It can be downloaded from the Genome Browser website at <a href="https://hgdownload.cse.ucsc.edu/admin/exe">https://hgdownload.cse.ucsc.edu/admin/exe</a>.</p> <p>User should prepare a UCSC Genome Browser bed file, a tab-separated four-column file. The first column is the chromosome; the second is the zero-based coordinate of the position of interest; the third is that zero-based coordinate plus one; and the fourth is a unique identifier for the position. The command below is an example of extracting FUN-LDA scores in tissue E007 for the positions defined in a bed file, "input.bed".</p> <pre><code class="language-bash">bigWigAverageOverBed E007.valley9.c89.bigwig input.bed output.tab</code></pre> <p>It produces a six-column file, output.tab. The first column includes the unique position id defined in the input bed file. The last column, average over the covered bases, is the score for this position.</p> <p><strong>Reference</strong></p> <p>Daniel Backenroth, Zihuai He, Krzysztof Kiryluk, Valentina Boeva, Lynn Pethukova, Ekta Khurana, Angela Christiano, Joseph Buxbaum, Iuliana Ionita-Laza. FUN-LDA: A latent Dirichlet allocation model for predicting tissue-specific functional effects of noncoding variation: Methods and applications. American Journal of Human Genetics, 2018.<br> </p> <p> </p>
Study of Esomeprazole 20 mg or 40 mg vs Placebo Effectiveness on the Occurrence of Peptic Ulcers in Subjects on Low Dose Acetylsalicylic Acid (LDA)
ClinicalTrials.gov study NCT00441727. IPD Sharing: Not stated. Countries: 20. Publications: 2.
Oskars Kalpaks, LDA tematu modelis (6)
<p>Datu kopa izveidota no laikraksta Latvijas Kareivis (1925 - 1940) datiem, atlasot rakstus, kas satur vārdu "kalpaks". </p> <p>Dati morfoloģiski marķēti ar NLP-PIPE (Znotiņš, Cīrule 2018).</p> <p>LDA tematu modelis izveidots ar Gensim (Řehůřek, Sojka 2010).</p> <p> </p>
One million articles from five post socialist countries with extracted features: sentiment, basic emotions, LDA topics and presence of influential domestic politicians
<p>This is a replication data for my paper under blind review.<br> <br> This paper develops a new prediction model for media content presence on a website. It analyses a new corpus of one million articles from five countries: Poland, Russia, Belarus, Kazakhstan and Ukraine, in two languages, Polish and Russian. These articles were scraped daily from seventeen websites in 2017-2020 period. The research applies a wide range of natural language processing methods to automatically derive several properties of each article: its topic, sentiment, basic emotions, mentions of influential domestic politicians. The articles’ embeddings and their cosine similarity are used to calculate the news context, such as how an article differs from the daily issue main themes. These features are used to estimate a logistic regression assessing the likelihood that the same or slightly modified, as measured by cosine similarity, article will remain on the main web page the next day. The key, and somewhat unexpected result is that articles with negative sentiment polarity are less likely to be published for more than one day. This result holds for all countries analyzed. It means that the negative news bias documented in the literature is partly offset by their shorter life cycle.<br> <br> Data is in the Python pickle format. Should be read into Python using the pickle.load() function. Each element (row) is the data frames or list represents one news article. Each file has the same format. Loading a pickle file returns a list of four elements:<br> 1. A dummy variable equal to 1 when the article was published the next day, with the text being identical<br> 2. A dummy variable equal to 1 when the article was published the next day, but we allow for small text modifications (cosine similarity > 0.99)<br> 3. Dataframe with extracted features, described below.<br> 4. List with texts of articles in Polish or Russian<br> <br> Ad 3. The columns of the dataframe are as follows (we refer to row number i in description):<br> - pandas index (may appear once or twice in the datafame)<br> - maxcosine: maximum cosine similarity between art i and all articles published next day<br> - cosine_diff: cosine similarity between article i and the elementwise average of embeddings of all articles in the current issue. Measure how similar is the article i to the core narrative of the current issue<br> - cosine_std: std. dev. of cosine similarity measures between all pairs of articles in the current issue. Measures how focused or dispersed is the current issue news coverage<br> - thirteen LDA topic groups: politics, legislation and legal affairs (POL); economy, finance, various sectors of the economy (ECO); military, war, protests, crime, security threats (MIL); international affairs, specific issues concerning foreign countries (INT); technology (TECH); family issues, culture, sport, education (FAM); regional issues and housing (REG); health issues and the Covid-19 pandemic (HEA); media (MED); accidents (ACC); religion (REL); the Soviet Union (USSR); and articles for which no topic could be determined (MISC).<br> - rsent.c: relative sentiment that is dictionary based sentiment of articles i minus the average sentiment of the newspaper. This approach eliminates newspaper or country idiosyncratic sentiment factors. c stands for Covid, the sentiment lexicon was augmented with Covid related terms<br> - dip_*: Variable measuring if influential domestic politicians are mentioned in article i, * represent a country acronym. If N is equal to the number of occurrences of the names of influential domestic politicians in the article i, dip_* = 0 if N=0, dip_* = 1+ log(N) if N>0.<br> - three or four names of news portals from which the data was scraped.<br> - names of six basic emotions and the article i emotion scores calculated using zero-shot learning and the large version of the XLM (Conneau et al., 2019) model from the huggingface transformers library available at https://huggingface.co/vicgalle/xlm-roberta-large-xnli-anli<br> Names of the politicians used to calculate dip variables<br> Russia<br> "putin" "medvedev" "vaino" "shoigu" "bortnikov" "lavrov" "mishustin" "kirienko" "sechin"<br> Ukraine<br> "zelensky" "shmygal" "akhmetov" "avakov" "ermak" "poroshenko" "medvedchuk" "groisman"<br> Kazakhstan<br> "sagyntaev" "mamin" "tokayev" "nnazarbayev" "dnazarbayeva" "kulibayev" "masimov"<br> Belarus<br> "alukashenko" "vakulchik" "vlukashenko" "kobyakov" "makei" "myasnikovich" "rumas" "golovchenko"<br> Poland<br> "kaczynski" "duda" "morawiecki" "ziobro"<br> Data coverage<br> Country, news portal, numbr of articles<br> Russia iz.ru 43,782<br> Russia kommersant.ru 46,070<br> Russia novayagazeta.ru 29,357<br> Russia vedomosti.ru 27,797<br> Kazakhstan informburo.kz 29,375<br> Kazakhstan nur.kz 67,350<br> Kazakhstan tengrinews.kz 44,285<br> Kazakhstan zakon.kz 109,442<br> Belarus bdg.by 33,447<br> Belarus belgazeta.by 21,995<br> Belarus sb.by 83,685<br> Ukraine kp.ua 194,792<br> Ukraine segodnya.ua 45,835<br> Ukraine vesti.ua 90,559<br> Poland gazeta.pl 53,321<br> Poland rp.pl 49,587<br> Poland wpolityce.pl 76,625<br> <br> In the provided dataframes the number of observations is smaller, because the issues for which there was no next day issue, were removed.<br> <br> Data was scraped daily between 2017 or 2018 (depending on the country) and January 2021.</p>
LDA and LMWH vs LDA Alone in High-risk Patients for Preeclampsia Prevention
ClinicalTrials.gov study NCT07361679. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
LDA_NEURAL_NETWORK_PATIENT_DATA
Open the record for dataset details and reuse information.
Fig. 50. LDA allocation results for A. guineensis and A in A revision of the Afrotropical species of the Dorylinae ant genus Aenictus (Hymenoptera: Formicidae) based on the worker caste
Fig. 50. LDA allocation results for A. guineensis and A. rotundatus.
Dual diagnosis: a LDA study of the topic evolution in the period 1967-2023. Dataset
<p>Dataset for the work "<span>Dual diagnosis: a LDA study of the topic evolution in the period 1967-2023"</span></p>
Frame Analysis of Chinese COVID-19 Press Conference Texts during 2020-2023: A LDA Topic Modelling Approach
Open the record for dataset details and reuse information.
Identification of robust cellular programs using reproducible LDA that impact sex-specific disease progression in different genotypes of a mouse model of AD
GEO Series GSE255965. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Nexium Capsules LDA Specific Clinical Experience Investigation
ClinicalTrials.gov study NCT01729182. IPD Sharing: Not stated. Countries: 1. Publications: 0.
An Evaluation of the Optimal Dose of Tofacitinib Needed to Achieve Low Disease Activity (LDA) or Clinical Remission in Patients With Active Rheumatoid Arthritis (RA) as Measured From a Clinical and St
ClinicalTrials.gov study NCT02566967. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study Comparing a Fixed-Dose Capsule of Esomeprazole 40mg and LDA 325mg With Free Combination
ClinicalTrials.gov study NCT00688428. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.