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A Brief Bioinformatics Tutorial
This is about how to use a computer to find what is known about a gene of interest and also how to get new insights about it. The tutorial is divided in three main parts: In the Sequence part, you will see how to look efficiently for a particular protein sequence, how to blast it against the ...Tags: Bioinformatics, Computational Biology, Tutorial, Learn, Online, Basics, Learning
3686 days ago
HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning
HECIL—Hybrid Error Correction with Iterative Learning—a hybrid error correction framework that determines a correction policy for erroneous long reads, based on optimal combinations of decision weights obtained from short read alignments. HECIL’s core algorithm by introdu...Tags: HECIL, Hybrid, Error, Correction, Algorithm, Long, Reads, Iterative, Learning, longreads
2000 days ago
- Module 1: Statistics for genomics (2-8 August 2021) A simple intro to statistical distributions hypothesis testing linear models. reading: http://compgenomr.github.io/book/stats.html slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/compgen2021_stats.pdf exer...
Tags: Machine, Learning, Genomics, Reseearch, AI
1018 days ago
A guide to machine learning for biologists
Because of the increasing size and inherent complexity of biological data, there has been an increase in the application of machine learning in biology to create useful and predictive models of the underlying biological processes. All machine learning techniques fit models to data; nevertheless, ...Tags: Machine, Learning, Learn, Biology, Computer, Guide
908 days ago
Janggu - Deep learning for Genomics
Janggu is a python package that facilitates deep learning in the context of genomics. The package is freely available under a GPL-3.0 license. Detail tutorial at https://janggu.readthedocs.io/en/latest/ USE cases https://github.com/wkopp/janggu_usecasesTags: Janggu, Deep, learning, Genomics
1188 days ago
GLEAN: an unsupervised learning system to integrate disparate sources of gene structure evidence
GLEAN is an unsupervised learning system to integrate disparate sources of gene structure evidence (gene model predictions, EST/protein genomic sequence alignments, SAGE/peptide tags, etc) to produce a consensus gene prediction, without prior training.Tags: GLEAN, unsupervised, learning, integrate, gene, structure, evidence, prediction
2213 days ago
- this is a fourth blog post in the Machine learning in Perl series, focusing on the AI::MXNet, a Perl interface to Apache MXNet, a modern and powerful machine learning library. If you're interested in refreshing your memory or just new to the series, please check previous entries over here:&...
Tags: Machine, learning, Perl
1589 days ago
Machine learning training and courses in bioinformatics !
Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. In this class, we will learn basics about probabilistic models and machine learning techniques. We will focus on ...Tags: Machine, learning, Bioinformatics, course, tutorial
1636 days ago
modelStudio: a package automates the explanation of machine learning predictive models
The modelStudio package automates the explanation of machine learning predictive models. This package generates advanced interactive and animated model explanations in the form of a serverless HTML site. It combines R with D3.js to produce plots and descriptions for...Tags: modelStudio, package, automates, explanation, machine, learning, predictive, models
1614 days ago
A guide to machine learning for biologists
We aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss...Tags: guide, machine, learning, biologists
1012 days ago
