Tristan/synth-data-target-string-test dataset hosted on Hugging Face and contributed by the HF Datasets community

Additional file 9. STRING db Analysis of Intra-individual Positional Gene Rankings In 12 Archived Control Samples Based On Range/Median, Range/Q3, Kurtosis and Q4/Q(2 + 3) Slope Calculations.

Introduction

This dataset card contains typed solutions and relevant notes to a multitude of problems in string theory that I authored during my study. More notes and solutions are set to be typed as times allows. Find the solutions and notes available in the Files and Versions section under the file name Reynolds_string_solutions.pdf. The PDF will not render in the UI if using Safari - you will have to download the file to view it. However, Google Chrome (and likely other browsers)… See the full description on the dataset page: https://huggingface.co/datasets/MarioBarbeque/StringTheorySolutions.

Functional enrichment in each module, analyzed with STRING 9.0.

Network analysis is a novel method to understand the complex pathogenesis of inflammation-driven atherosclerosis. Using this approach, we attempted to identify key inflammatory genes and their core transcriptional regulators in coronary artery disease (CAD). Initially, we obtained 124 candidate genes associated with inflammation and CAD using Polysearch and CADgene database for which protein-protein interaction network was generated using STRING 9.0 (Search Tool for the Retrieval of Interacting Genes) and visualized using Cytoscape v 2.8.3. Based on betweenness centrality (BC) and node degree as key topological parameters, we identified interleukin-6 (IL-6), vascular endothelial growth factor A (VEGFA), interleukin-1 beta (IL-1B), tumor necrosis factor (TNF) and prostaglandin-endoperoxide synthase 2 (PTGS2) as hub nodes. The backbone network constructed with these five hub genes showed 111 nodes connected via 348 edges, with IL-6 having the largest degree and highest BC. Nuclear factor kappa B1 (NFKB1), signal transducer and activator of transcription 3 (STAT3) and JUN were identified as the three core transcription factors from the regulatory network derived using MatInspector. For the purpose of validation of the hub genes, 97 test networks were constructed, which revealed the accuracy of the backbone network to be 0.7763 while the frequency of the hub nodes remained largely unaltered. Pathway enrichment analysis with ClueGO, KEGG and REACTOME showed significant enrichment of six validated CAD pathways - smooth muscle cell proliferation, acute-phase response, calcidiol 1-monooxygenase activity, toll-like receptor signaling, NOD-like receptor signaling and adipocytokine signaling pathways. Experimental verification of the above findings in 64 cases and 64 controls showed increased expression of the five candidate genes and the three transcription factors in the cases relative to the controls (p

Question Paper Solutions of chapter String Matching Problem of Design Analysis of Algorithms, 4th Semester , Information Technology

Question Paper Solutions of chapter String Matching Problem of IT501 - Design and Analysis of Algorithm, 5th Semester , Information Technology

The beginnings of the measures describe a simple cadenza, which is resolved with a pure C major triad in the basic position with a doubled octave.

for enrichment in gene ontology biological processes. Glucose metabolism was found to be the most significant biological process, and is also highlighted in the STRING network map (Fig. 5).

PDF QA Annotated Dataset

This dataset contains 324 examples where each row corresponds to an annotated question related to a PDF document. It includes metadata, annotation labels, and a list of 9 rendered images (first pages of the referenced PDF).

  Dataset Structure

Each row contains:

Column Type Description

session_id string Unique session ID

timestamp string Timestamp of annotation

paper_id string Identifier of the paper

source string Source of question… See the full description on the dataset page: https://huggingface.co/datasets/anonymousatom/paper_voting_data.

Theoretically predicted versus experimentally measured pitch deviation for a tremolo arm pull up by θ = 0.085 radians assuming dimensions and variables taken from Table 1 with D’Addario EXL120 “Nickel Wound” Super Light 9-42 strings (round wound with nickel plated steel).

Subtitle

"Synthetic protein dataset with sequences, physical properties, and functional classification for machine learning tasks."

Description

Introduction

This synthetic dataset was created to explore and develop machine learning models in bioinformatics. It contains 20,000 synthetic proteins, each with an amino acid sequence, calculated physicochemical properties, and a functional classification.

Columns Included

• ID_Protein: Unique identifier for each protein.
• Sequence: String of amino acids.
• Molecular_Weight: Molecular weight calculated from the sequence.
• Isoelectric_Point: Estimated isoelectric point based on the sequence composition.
• Hydrophobicity: Average hydrophobicity calculated from the sequence.
• Total_Charge: Sum of the charges of the amino acids in the sequence.
• Polar_Proportion: Percentage of polar amino acids in the sequence.
• Nonpolar_Proportion: Percentage of nonpolar amino acids in the sequence.
• Sequence_Length: Total number of amino acids in the sequence.
• Class: The functional class of the protein, one of five categories: Enzyme, Transport, Structural, Receptor, Other.

Inspiration and Sources

While this is a simulated dataset, it was inspired by patterns observed in real protein datasets, such as: - UniProt: A comprehensive database of protein sequences and annotations. - Kyte-Doolittle Scale: Calculations of hydrophobicity. - Biopython: A tool for analyzing biological sequences.

Proposed Uses

This dataset is ideal for: - Training classification models for proteins. - Exploratory analysis of physicochemical properties of proteins. - Building machine learning pipelines in bioinformatics.

How This Dataset Was Created

1. Sequence Generation: Amino acid chains were randomly generated with lengths between 50 and 300 residues.
2. Property Calculation: Physicochemical properties were calculated using the Biopython library.
3. Class Assignment: Classes were randomly assigned for classification purposes.

Limitations

• The sequences and properties do not represent real proteins but follow patterns observed in natural proteins.
• The functional classes are simulated and do not correspond to actual biological characteristics.

Data Split

The dataset is divided into two subsets: - Training: 16,000 samples (proteinas_train.csv). - Testing: 4,000 samples (proteinas_test.csv).

Acknowledgment

This dataset was inspired by real bioinformatics challenges and designed to help researchers and developers explore machine learning applications in protein analysis.

Il était prévu que d’ici 2022, le marché mondial des onduleurs string représenterait 4,9 milliards de dollars. Sur la période projetée (2023 - 2030), le marché mondial des onduleurs string devrait passer de 5,0 milliards de dollars en 2023 à 9,7 milliards de dollars d'ici 2030, ce qui représente un TCAC (taux de croissance annuel composé) de 9,30 %. Taille du marché, croissance, part

Note: The abbreviations of the protein names used in the protein interaction map are shown in Fig 7b, as assigned by STRING v12.0. Abbreviation: OS, species; OX, taxonomic identifier; GN, gene name. For proteins for which STRING did not provide abbreviations, “NA” has been indicated. a Human Counterpart UniProtKB Accession Numbers. b Abbreviations assigned by STRING. (XLSX)

BackgroundIdiopathic pulmonary fibrosis (IPF) and pulmonary sarcoidosis are typical interstitial lung diseases with unknown etiology that cause lethal lung damages. There are notable differences between these two pulmonary disorders, although they do share some similarities. Gene expression profiles have been reported independently, but differences on the transcriptional level between these two entities have not been investigated.Methods/ResultsAll expression data of lung tissue samples for IPF and sarcoidosis were from published datasets in the Gene Expression Omnibus (GEO) repository. After cross platform normalization, the merged sample data were grouped together and were subjected to statistical analysis for finding discriminate genes. Gene enrichments with their corresponding functions were analyzed by the online analysis engine “Database for Annotation, Visualization and Integrated Discovery” (DAVID) 6.7, and genes interactions and functional networks were further analyzed by STRING 9.0 and Cytoscape 3.0.0 Beta1. One hundred and thirty signature genes could potentially differentiate one disease state from another. Compared with normal lung tissue, tissue affected by IPF and sarcoidosis displayed similar signatures that concentrated on proliferation and differentiation. Distinctly expressed genes that could distinguish IPF from sarcoidosis are more enriched in processes of cilium biogenesis or degradation and regulating T cell activations. Key discriminative network modules involve aspects of bone morphogenetic protein receptor two (BMPR2) related and v-myb myeloblastosis viral oncogene (MYB) related proliferation.ConclusionsThis study is the first attempt to examine the transcriptional regulation of IPF and sarcoidosis across different studies based on different working platforms. Groups of significant genes were found to clearly distinguish one condition from the other. While IPF and sarcoidosis share notable similarities in cell proliferation, differentiation and migration, remarkable differences between the diseases were found at the transcription level, suggesting that the two diseases are regulated by overlapping yet distinctive transcriptional networks.

Protein-Protein Interaction Gene Sets and KEGG Pathway Results Post-STRING Clustering.

Cross-linking mass spectrometry (XL-MS) is a powerful tool for elucidating protein structures and protein–protein interactions (PPIs) at the global scale. However, sensitive XL-MS analysis of mass-limited samples remains challenging, due to serious sample loss during sample preparation of the low-abundance cross-linked peptides. Herein, an optimized miniaturized filter-aided sample preparation (O-MICROFASP) method was presented for sensitive XL-MS analysis of microscale samples. By systematically investigating and optimizing crucial experimental factors, this approach dramatically improves the XL identification of low and submicrogram samples. Compared with the conventional FASP method, more than 7.4 times cross-linked peptides were identified from single-shot analysis of 1 μg DSS cross-linked HeLa cell lysates (440 vs 59). The number of cross-linked peptides identified from 0.5 μg HeLa cell lysates was increased by 58% when further reducing the surface area of the filter to 0.058 mm2 in the microreactor. To deepen the identification coverage of XL-proteome, five different types of cross-linkers were used and each μg of cross-linked HeLa cell lysates was processed by O-MICROFASP integrated with tip-based strong cation exchange (SCX) fractionation. Up to 2741 unique cross-linked peptides were identified from the 5 μg HeLa cell lysates, representing 2579 unique K–K linkages on 1092 proteins. About 96% of intraprotein cross-links were within the maximal distance restraints of 26 Å, and 75% of the identified PPIs reported by the STRING database were with high confidence (scores ≥0.9), confirming the high validity of the identified cross-links for protein structural mapping and PPI analysis. This study demonstrates that O-MICROFASP is a universal and efficient method for proteome-wide XL-MS analysis of microscale samples with high sensitivity and reliability.

List of STRING interactions shown in tabular format.

There is still large controversy about whether abstract knowledge of physical problems is uniquely human. We presented 9 capuchin monkeys, 6 bonobos, 6 chimpanzees and 48 children with two versions of a broken-string problem. In the standard condition, participants had to choose between an intact and a broken string as means to a reward. In the critical condition, the functional parts of the strings were covered up and replaced by perceptually similar, but non-functional cues. Apes, monkeys and young children performed significantly better in the standard condition in which the cues played a functional role, indicating knowledge of the functional properties involved. Moreover, a control experiment with chimpanzees and young children ruled out that this difference in performance could be accounted for by differences of perceptual feedback in the two conditions. We suggest that, similar to humans, nonhuman primates partly rely on abstract concepts in physical problem-solving.

Including the following: (1) Enrichment ratios under different thresholds in the second step (Figure S1); (2) literature evaluation for 10 predicted inflammatory genes (Table S1); (3) literature evaluation for 32 predicted regulators of inflammation (Table S2); (4) wikipathway enrichment analysis of molecular signatures via TSNBA (Table S3); (5) TF binding sites analysis of 32 predicted regulators of inflammation (Table S4); (6) transcription factor-inflammatory gene relationships from TFactS for 5 transcription factors, namely DLX5, MSX1, NR5A1, PRRX1, and RXRA (Table S5); (7) protein-protein interactions collected from STRING database for 9 transcription factors that are confirmed by literature to be inflammatory regulators (Table S6); (8) investigation for diffusion parameter α of network propagation (Table S7); (9) methods comparison for the first step of TSNBA (Table S8); (10) methods comparison for TSNBA (Table S9); (11) union sets of signatures from different methods. (XLSX)