Search Results for "markov prediction"

..., and 20M. It also incorporates utilities for creating synthetic datasets. For example, generate_sequential generates a Markov-chain-derived interaction dataset, where the next item a user chooses is a function of their previous interactions. Recommendations can be seen as a sequence prediction task: given the items a user has interacted with in the past, what will be the next item they will interact with? Spotlight provides a range of models.

MetaErg is a stand-alone and fully automated metagenome and metaproteome annotation pipeline published at: https://www.frontiersin.org/articles/10.3389/fgene.2019.00999/full. If you are using this pipeline for your work, please cite: Dong X and Strous M (2019) An Integrated Pipeline for Annotation and Visualization of Metagenomic Contigs. Front. Genet. 10:999. doi: 10.3389/fgene.2019.00999 The instructions on configuring and running the MetaErg pipeline is available at GitHub...

A new hidden Markov model (HMM)-based ab initio gene prediction tool for finding genes with highly variable GC contents such as the genes with negative GC gradients in grass genomes.

SnowyOwl is a gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions, and to evaluate the resulting models. The pipeline has been validated and streamlined by comparing its predictions to manually curated gene models in three fungal genomes, and its results show substantial increases in sensitivity and selectivity over previous gene predictions. Sensitivity is gained by repeatedly running the HMM gene...

The automated Prokaryotic Annotation Pipeline was developed to generate on demand ORF prediction and functional annotation for prokaryotic genomes. A round of non-coding RNA prediction is done using various tools such as tRNA-scanSE and BLAST. Gene finding is done using a self-training, iterated glimmer3 analysis. The predicted genes are then analyzed for overlaps, and homology based evidence is gathered using a system of hidden markov model search and BLAST. Roles and gene symbols are assigned...

... the best scores in almost all testings. We have also evaluated the practicability of the alignments of GLProbs by applying the tool to three biological applications, namely phylogenetic trees construction, protein secondary structure prediction and the detection of high risk members for cervical cancer in the HPV-E6 family, and the results are very encouraging.

SuperWillow is a Music Generation program. Artists have many influences which they have accumulated over the years by listening to countless pieces of music, this principle is reflected in SuperWillow.

Conrad is both a high performance Conditional Random Field engine which can be applied to a variety of machine learning problems and a specific set of models for gene prediction using semi-Markov CRFs.