Full-length transcriptome assembly from RNA-Seq data without a reference genome.
- M. Grabherr, B. Haas, A. Regev
- BiologyNature Biotechnology
- 1 July 2011
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
- D. Koller, N. Friedman
- Computer Science
- 31 July 2009
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
- B. Haas, A. Papanicolaou, A. Regev
- BiologyNature Protocols
- 1 August 2013
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
- N. Friedman, D. Geiger, M. Goldszmidt
- Computer ScienceMachine-mediated learning
- 1 November 1997
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
- M. Grabherr, B. Haas, A. Regev
- BiologyNature Biotechnology
- 29 April 2011
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.
Using Bayesian networks to analyze expression data
- N. Friedman, M. Linial, I. Nachman, D. Pe’er
- Computer ScienceAnnual International Conference on Research in…
- 8 April 2000
This paper proposes a new framework for discovering interactions between genes based on multiple expression measurements, and presents an efficient algorithm capable of learning such networks and statistical method to assess confidence in their features.
Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks
- N. Friedman, D. Koller
- Computer ScienceMachine-mediated learning
- 2004
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
- Craig Boutilier, N. Friedman, M. Goldszmidt, D. Koller
- Computer ScienceConference on Uncertainty in Artificial…
- 1 August 1996
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
- E. Segal, M. Shapira, N. Friedman
- BiologyNature Genetics
- 1 June 2003
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
- D. Koller, N. Friedman
- Computer Science
- 31 August 2009
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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