Working capital credit evaluation model using Python language applied to public banking: Case study in shrimp project by Bernardo Cordero (11794946)

Functions 2.0 by Jonatan Flyckt (8101010)

Alignment of 15 vRNA8 IAV sequences (<i>Clustal 1</i>.<i>2</i>.<i>4</i> web server). by Atara Neugroschl (16884406)

Subjects:

Distribution of the 494 TTS hits identified in 123 unique transcripts between the MFE and SO structures, which were predicted using minimization algorithms (<i>mfold</i>, <i>RNAstr... by Atara Neugroschl (16884406)

Subjects:

<i>melanogaster</i> unique transcripts with the potential of forming at least one R12 double-stranded region, identified using <i>RNAMotif</i>. by Atara Neugroschl (16884406)

Subjects:

Structure of an intramolecular Y⦁R:Y triple helix formed with an 11-nt long TTS, as determined using X-ray crystallography. by Atara Neugroschl (16884406)

Subjects:

<i>RNAMotif</i> results for IAV TTS-218 prevalence in <i>D</i>. <i>melanogaster</i> and <i>H</i>. <i>sapiens</i> transcriptome. by Atara Neugroschl (16884406)

Subjects:

The percentage of unique transcripts and corresponding genes containing the indicated TTS types, as obtained from the transcriptome (<i>version 6</i>.<i>38</i>) analysis. by Atara Neugroschl (16884406)

Subjects:

Model RNA hairpins illustrating examples of ideal and interrupted 12-bp long TTS. by Atara Neugroschl (16884406)

Subjects:

Results for the survey of <i>D</i>. <i>melanogaster</i> transcriptome (<i>version 6</i>.<i>38</i>) for the indicated purine-rich sequences. by Atara Neugroschl (16884406)

Subjects:

Distribution of the <i>TFOFinder</i> identified TTS sequences. by Atara Neugroschl (16884406)

Subjects:

<i>TFOFinder</i> program flowchart. by Atara Neugroschl (16884406)

Subjects:

Results for the <i>D</i>. <i>melanogaster</i> genome (<i>version 6</i>.<i>48</i>) for R12. by Atara Neugroschl (16884406)

Subjects:

<i>TFOFinder</i> results for vRNA8 IAV Viet Nam strain HM006763. by Atara Neugroschl (16884406)

Subjects:

Code supporting the paper: A Bayesian finite-element trained machine learning approach for predicting post-burn contraction by Ginger Egberts (9404726)

“…<p>This online resource shows two archived folders: <em>Matlab</em> and <em>Python</em>, that contain relevant code for the article: <em>A Bayesian finite-element trained machine learning approach for predicting post-burn contraction</em>. …”

In silico Toxicity Prediction, PA represent the Prediction accuracy, and AS represents the average similarity, toxicity profile (). by Kassim Folorunsho Adebambo (22806479)

Forecasting for second-hand house prices in China using the GA-PSO-BP neural-network model. (Excel Dataset and Python Code) by Ji Huabin (20531408)

The ROC, calibration curve and DCA of the prediction model based on MRI. by Jiangfa Li (18345033)

Subjects:

BOFdat Step 2: Identifying and calculating the stoichiometric coefficients of coenzymes and inorganic ions. by Jean-Christophe Lachance (6619307)

Optimal implementation of matrix operations on the CPU platform (double precision; solid black line = benchmarked bandwidth; lines + symbols = effective bandwidth; black = COPY2D;... by Xuzhen He (14676114)

Subjects: