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Base-pair resolution detection of transcription factor binding site by deep deconvolutional network

Bioinformatics, 2018-10, Vol.34 (20), p.3446-3453

2018

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Accurate LC peak boundary detection for ¹⁶O/¹⁸O labeled LC-MS data

PloS one, 2013, Vol.8 (10), p.e72951-e72951

2013

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Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks

Proteome science, 2011-10, Vol.9 Suppl 1 (S1), p.S9-S9, Article S9

2011

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A Robust and Efficient Feature Selection Algorithm for Microarray Data

Molecular informatics, 2017-04, Vol.36 (4), p.np-n/a

2017

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MZDASoft: a software architecture that enables large-scale comparison of protein expression levels over multiple samples based on liquid chromatography/tandem mass spectrometry

Rapid communications in mass spectrometry, 2015-10, Vol.29 (19), p.1841-1848

2015

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Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model

EURASIP journal on advances in signal processing, 2010-01, Vol.2010 (1), Article 538919

2010

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Predicting Sites of Epitranscriptome Modifications Using Unsupervised Representation Learning Based on Generative Adversarial Networks

Frontiers in physics, 2020-06, Vol.8

2020

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Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model

EURASIP journal on advances in signal processing, 2010-01, Vol.2010 (1), p.538919-538919

2010

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MZDASoft-A Software Architecture that Enables Large Scale Comparison of Protein Expression Levels over Multiple Samples Based on LC-MS/MS

Rapid communications in mass spectrometry, 2015-10, Vol.29 (19), p.1841-1848

2015

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A deep learning model for predicting transcription factor binding location at single nucleotide resolution

2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2017, p.57-60

2017

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Adaptive blind multiuser detection over flat fast fading channels using particle filtering

EURASIP journal on wireless communications and networking, 2005-04, Vol.2005 (2), p.130-140, Article 960165

2005

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Accurate LC Peak Boundary Detection for .sup.16O/.sup.18O Labeled LC-MS Data

PloS one, 2013-10, Vol.8 (10), p.e72951

2013

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Early disease correlated protein detection using early response index (ERI)

2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2016, p.569-572

2016

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IEEE_Xplore

Prediction of fatigue-related driver performance from EEG data by deep Riemannian model

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017, Vol.2017, p.4167-4170

2017

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Driver's fatigue prediction by deep covariance learning from EEG

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, p.240-245

2017

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Accurate LC Peak Boundary Detection for 16 O/ 18 O Labeled LC-MS Data

PloS one, 2013-10, Vol.8 (10)

2013

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Adaptive Blind Multiuser Detection over Flat Fast Fading Channels Using Particle Filtering

EURASIP journal on wireless communications and networking, 2005-04, Vol.2005 (2), p.960165-960165

2005

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Improved Quantification of Labeled LC-MS

2011 IEEE International Conference on Bioinformatics and Biomedicine, 2011, p.299-303

2011

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IEEE_Xplore

An SILAC quantification algorithm for LC-FTMS

Proceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), 2012, p.199-202

2012

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IEEE_Xplore

Uncovering transcriptional regulatory networks by sparse Bayesian factor model

IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, 2010, p.1785-1788

2010

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IEEE_Xplore

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Base-pair resolution detection of transcription factor binding site by deep deconvolutional network

Accurate LC peak boundary detection for ¹⁶O/¹⁸O labeled LC-MS data

Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks

A Robust and Efficient Feature Selection Algorithm for Microarray Data

MZDASoft: a software architecture that enables large-scale comparison of protein expression levels over multiple samples based on liquid chromatography/tandem mass spectrometry

Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model

Predicting Sites of Epitranscriptome Modifications Using Unsupervised Representation Learning Based on Generative Adversarial Networks

Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model

MZDASoft-A Software Architecture that Enables Large Scale Comparison of Protein Expression Levels over Multiple Samples Based on LC-MS/MS

A deep learning model for predicting transcription factor binding location at single nucleotide resolution

Adaptive blind multiuser detection over flat fast fading channels using particle filtering

Accurate LC Peak Boundary Detection for .sup.16O/.sup.18O Labeled LC-MS Data

Early disease correlated protein detection using early response index (ERI)

Prediction of fatigue-related driver performance from EEG data by deep Riemannian model

Driver's fatigue prediction by deep covariance learning from EEG

Accurate LC Peak Boundary Detection for 16 O/ 18 O Labeled LC-MS Data

Adaptive Blind Multiuser Detection over Flat Fast Fading Channels Using Particle Filtering

Improved Quantification of Labeled LC-MS

An SILAC quantification algorithm for LC-FTMS

Uncovering transcriptional regulatory networks by sparse Bayesian factor model

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