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Full-length transcriptome assembly from RNA-Seq data without a reference genome.
TLDR
The Trinity method for de novo assembly of full-length transcripts and evaluate it on samples from fission yeast, mouse and whitefly, whose reference genome is not yet available, providing a unified solution for transcriptome reconstruction in any sample.
Probabilistic Graphical Models - Principles and Techniques
TLDR
The framework of probabilistic graphical models, presented in this book, provides a general approach for causal reasoning and decision making under uncertainty, allowing interpretable models to be constructed and then manipulated by reasoning algorithms.
De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis
TLDR
This protocol provides a workflow for genome-independent transcriptome analysis leveraging the Trinity platform and presents Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes.
Bayesian Network Classifiers
TLDR
Tree Augmented Naive Bayes (TAN) is single out, which outperforms naive Bayes, yet at the same time maintains the computational simplicity and robustness that characterize naive Baye.
Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data
Massively parallel sequencing of cDNA has enabled deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a
Using Bayesian Networks to Analyze Expression Data
TLDR
A new framework for discovering interactions between genes based on multiple expression measurements is proposed and a method for recovering gene interactions from microarray data is described using tools for learning Bayesian networks.
Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks
TLDR
This paper shows how to efficiently compute a sum over the exponential number of networks that are consistent with a fixed order over network variables, and uses this result as the basis for an algorithm that approximates the Bayesian posterior of a feature.
Context-Specific Independence in Bayesian Networks
TLDR
This paper proposes a formal notion of context-specific independence (CSI), based on regularities in the conditional probability tables (CPTs) at a node, and proposes a technique, analogous to (and based on) d-separation, for determining when such independence holds in a given network.
Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data
TLDR
The procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'.
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
TLDR
Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.
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