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It works on Linux, Windows, macOS, and is available in Python, [8] R, [9] and models built using CatBoost can be used for predictions in C++, Java, [10] C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub. [6] InfoWorld magazine awarded the library "The best machine learning tools" in 2017.
Medical open network for AI (MONAI) is an open-source, community-supported framework for Deep learning (DL) in healthcare imaging. MONAI provides a collection of domain-optimized implementations of various DL algorithms and utilities specifically designed for medical imaging tasks.
DECIPHER is a web-based resource and database of genomic variation data from analysis of patient DNA. [ 1 ] [ 2 ] [ 3 ] It documents submicroscopic chromosome abnormalities ( microdeletions and duplications ) and pathogenic sequence variants (single nucleotide variants - SNVs, Insertions, Deletions, InDels), from over 25000 patients and maps ...
DECIPHER is a software that can be used to decipher and manage biological sequences efficiently using the programming language R. Features ...
Schematic overview of the modular structure underlying procedures for gene set enrichment analysis. Gene set enrichment analysis (GSEA) (also called functional enrichment analysis or pathway enrichment analysis) is a method to identify classes of genes or proteins that are over-represented in a large set of genes or proteins, and may have an association with different phenotypes (e.g ...
Name Description Knots [Note 1]Links References trRosettaRNA: trRosettaRNA is an algorithm for automated prediction of RNA 3D structure. It builds the RNA structure by Rosetta energy minimization, with deep learning restraints from a transformer network (RNAformer). trRosettaRNA has been validated in blind tests, including CASP15 and RNA-Puzzles, which suggests that the automated predictions ...
Bowtie is a software package commonly used for sequence alignment and sequence analysis in bioinformatics. [3] The source code for the package is distributed freely and compiled binaries are available for Linux, macOS and Windows platforms.
These algorithms typically do not work well for larger read sets, as they do not easily reach a global optimum in the assembly, and do not perform well on read sets that contain repeat regions. [1] Early de novo sequence assemblers, such as SEQAID [ 2 ] (1984) and CAP [ 3 ] (1992), used greedy algorithms, such as overlap-layout-consensus (OLC ...