LaTeX template and build code for a Nature style research article submission
Explore Similar Repositories
voiceprint:Turn your own spoken words into a finished article. AI does the research, structure and checking. You supply the sentences, by talking.
Advanced-AI-Prompting-Guidelines-For-Research:A friendly and comprehensive set of guidelines to help researchers craft powerful prompts for AI. These resources cover everything from brainstorming thesis topics to exploring novel article angles—complete with bilingual (English & Persian) instructions!
fulltext-article-downloader:fulltext-article-downloader is a Python package for programmatically downloading the full text of research articles from their DOIs. It chains together publisher APIs, open‑access aggregators, and polite web scraping in a fallback sequence so you can collect large corpora of PDFs or XML for text mining and analysis.
KaryML-Framework:Machine Learning (ML) research within medicine and healthcare represents one of the most challenging domains for both engineers and medical specialists. One of the most desired tasks to be accomplished using ML applications is represented by disease detection. A good example of such a task is the detection of genetic abnormalities like Down syndrome, Klinefelter syndrome or Hemophilia. Usually, clinicians are doing chromosome analysis using the karyotype to detect such disorders. The main contribution of the current article consists of introducing a new approach called KaryML Framework, which is extending our previous research: KarySOM: An Unsupervised Learning based Approach for Human Karyotyping using Self-Organizing Maps . Our major goal is to provide a new method for an automated karyotyping system using unsupervised techniques. Additionally, we provide computational methods for chromosome feature extraction and to develop an intelligent system designed to aid clinicians during the karyotyping process.
single-trait-electra:A complete ELECTRA-based framework for Big Five personality trait recognition, featuring data augmentation, single-trait model training, k-fold cross-validation, grid search optimization, inference tools, and full reproducibility of the associated research article.
// repository documentation
Was this content helpful?
★ 0(0 ratings)
Recent Feedback
Download README
Do you want to download the README.md file for nature-tex?