Alexander J. Hartemink

发表

Joint classifier and feature optimization for cancer diagnosis using gene expression data

Lawrence Carin, Balaji Krishnapuram, Alexander J. Hartemink, 2003, RECOMB '03.

A generalized model for multi-marker analysis of cell cycle progression in synchrony experiments

Alexander J. Hartemink, Michael B. Mayhew, Joshua W. Robinson, 2011, Bioinform..

Maximum-likelihood estimation of optimal scaling factors for expression array normalization

Tommi S. Jaakkola, David K. Gifford, Alexander J. Hartemink, 2001, SPIE BiOS.

Simulating biological reactions: A modular approach

David K. Gifford, Alexander J. Hartemink, Tarjei S. Mikkelsen, 1999, DNA Based Computers.

Thermodynamic simulation of deoxyoligonucleotide hybridization for DNA computation

David K. Gifford, Alexander J. Hartemink, D. Gifford, 1997, DNA Based Computers.

Bayesian Methods for Elucidating Genetic Regulatory Networks

Tommi S. Jaakkola, Richard A. Young, David K. Gifford, 2002, IEEE Intell. Syst..

Informative Structure Priors: Joint Learning of Dynamic Regulatory Networks from Multiple Types of Data

Alexander J. Hartemink, Allister Bernard, A. Hartemink, 2004, Pacific Symposium on Biocomputing.

Domain-oriented edge-based alignment of protein interaction networks

Xin Guo, Alexander J. Hartemink, A. Hartemink, 2009, Bioinform..

Combining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models

Tommi S. Jaakkola, Richard A. Young, David K. Gifford, 2001, Pacific Symposium on Biocomputing.

Cell-cycle phenotyping with conditional random fields: A case study in Saccharomyces cerevisiae

Alexander J. Hartemink, Michael B. Mayhew, 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

PROTEIN-NUCLEIC ACID INTERACTIONS: INTEGRATING STRUCTURE, SEQUENCE, AND FUNCTION – Session Introduction

Ernest Fraenkel, Martha L. Bulyk, Alexander J. Hartemink, 2007 .

Informative priors based on transcription factor structural class improve de novo motif discovery

Alexander J. Hartemink, Uwe Ohler, Raluca Gordân, 2006, ISMB.

Theoretical and practical advances in genome halving

Peng Yin, Alexander J. Hartemink, P. Yin, 2005, Bioinform..

Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks

Tommi S. Jaakkola, Richard A. Young, David K. Gifford, 2000, Pacific Symposium on Biocomputing.

RoboCOP: Multivariate State Space Model Integrating Epigenomic Accessibility Data to Elucidate Genome-Wide Chromatin Occupancy

Alexander J. Hartemink, Jianling Zhong, Sneha Mitra, 2020, RECOMB.

Intrinsic Disorder Within and Flanking the DNA-Binding Domains of Human Transcription Factors

Xin Guo, Martha L. Bulyk, Alexander J. Hartemink, 2011, Pacific Symposium on Biocomputing.

Learning Protein-DNA Interaction Landscapes by Integrating Experimental Data through Computational Models

Alexander J. Hartemink, Jianling Zhong, Todd Wasson, 2014, RECOMB.

Learning a Hybrid Architecture for Sequence Regression and Annotation

Lawrence Carin, Alexander J. Hartemink, Ricardo Henao, 2015, AAAI.

Using Bayesian Network Inference Algorithms to Recover Molecular Genetic Regulatory Networks

V. Anne Smith, Alexander J. Hartemink, Jing Yu, 2002 .

SLICER: Inferring Branched, Nonlinear Cellular Trajectories from Single Cell RNA-seq Data

Alexander J. Hartemink, Jan F. Prins, Joshua D. Welch, 2016 .

Influence of Network Topology and Data Collection on Network Inference

V. Anne Smith, Alexander J. Hartemink, Erich D. Jarvis, 2003, Pacific Symposium on Biocomputing.

Reconstructing the Topology of Protein Complexes

Alexander J. Hartemink, Allister Bernard, David S. Vaughn, 2007, RECOMB.

A Bayesian approach to joint feature selection and classifier design

Lawrence Carin, Mário A. T. Figueiredo, Balaji Krishnapuram, 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A Nucleosome-Guided Map of Transcription Factor Binding Sites in Yeast

Alexander J. Hartemink, Raluca Gordân, Leelavati Narlikar, 2007, PLoS Comput. Biol..

Advances to Bayesian network inference for generating causal networks from observational biological data

Paul P. Wang, Jing Yu, V. Anne Smith, 2004, Bioinform..

Developing bayesian network inference algorithms to predict causal functional pathways in biological systems

Jing Yu, Paul P. Wang, Paul P. Wang, 2005 .

Joint Classifier and Feature Optimization for Comprehensive Cancer Diagnosis Using Gene Expression Data

Lawrence Carin, Balaji Krishnapuram, Alexander J. Hartemink, 2004, J. Comput. Biol..

Semi-Supervised Classification

Lawrence Carin, Mário A. T. Figueiredo, Balaji Krishnapuram, 2004, Encyclopedia of Database Systems.

Thermodynamic simulation of deoxyoligonucleotide hybridization, polymerization, and ligation

Alexander J. Hartemink, 1997 .

Sequence features of DNA binding sites reveal structural class of associated transcription factor

Alexander J. Hartemink, Leelavati Narlikar, A. Hartemink, 2006, Bioinform..

A Fast, Alignment-Free, Conservation-Based Method for Transcription Factor Binding Site Discovery

Alexander J. Hartemink, Raluca Gordân, Leelavati Narlikar, 2008, RECOMB.

Learning Non-Stationary Dynamic Bayesian Networks

Alexander J. Hartemink, Joshua W. Robinson, A. Hartemink, 2010, J. Mach. Learn. Res..

Stability selection for regression-based models of transcription factor–DNA binding specificity

Barbara E. Engelhardt, Alexander J. Hartemink, Raluca Gordân, 2013, Bioinform..

Computational Inference of Neural Information Flow Networks

Jing Yu, V. Anne Smith, Alexander J. Hartemink, 2006, PLoS Comput. Biol..

Using DNA Duplex Stability Information for Transcription Factor Binding Site Discovery

Alexander J. Hartemink, Raluca Gordân, 2008, Pacific Symposium on Biocomputing.

Systems Biology Feedback (of the Collaborative Kind)

Alexander J. Hartemink, Michael B. Mayhew, Steven B. Haase, 2012 .

Using DNase Digestion Data to Accurately Identify Transcription Factor Binding Sites

Alexander J. Hartemink, Kaixuan Luo, A. Hartemink, 2012, Pacific Symposium on Biocomputing.

Gene expression analysis: Joint feature selection and classifler design

Lawrence Carin, Balaji Krishnapuram, Alexander J. Hartemink, 2004 .

Finding Diagnostic Biomarkers in Proteomic Spectra

Alexander J. Hartemink, Edward F. Patz, Pallavi N. Pratapa, 2006, Pacific Symposium on Biocomputing.

SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data

Li Qian, Li Wang, Junjie Lu, 2016, Genome Biology.

Non-stationary dynamic Bayesian networks

Alexander J. Hartemink, Joshua W. Robinson, A. Hartemink, 2008, NIPS.

Nucleosome Occupancy Information Improves de novo Motif Discovery

Alexander J. Hartemink, Raluca Gordân, Leelavati Narlikar, 2007, RECOMB.

Bayesian Methods for Elucidating Genetic Regulatory Networks

Tommi S. Jaakkola, Richard A. Young, David K. Gifford, 2002, IEEE Intell. Syst..

Principled computational methods for the validation discovery of genetic regulatory networks

Alexander J. Hartemink, A. Hartemink, 2001 .

Learning protein-DNA interaction landscapes by integrating experimental data through computational models

Alexander J. Hartemink, Jianling Zhong, Todd Wasson, 2014, Bioinform..

Distinguishing Direct versus Indirect Transcription Factor-DNA Interactions

Martha L. Bulyk, Alexander J. Hartemink, Raluca Gordân, 2010, RECOMB.

Session Introduction: Joint Learning from Multiple Types of Genomic Data

Eran Segal, Alexander J. Hartemink, E. Segal, 2005, Pacific Symposium on Biocomputing.

Close Encounters of the Collaborative Kind

Xin Guo, Alexander J. Hartemink, Michael B. Mayhew, 2012, Computer.

Anonymous Authentication of Membership in Dynamic Groups

Stuart E. Schechter, Alexander J. Hartemink, Todd Parnell, 1999, Financial Cryptography.

Sparse multinomial logistic regression: fast algorithms and generalization bounds

Lawrence Carin, Mário A. T. Figueiredo, Balaji Krishnapuram, 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Session Introduction

William Stafford Noble, Manolis Kellis, Alexander J. Hartemink, 2003, Pacific Symposium on Biocomputing.

Evaluating functional network inference using simulations of complex biological systems

V. Anne Smith, Alexander J. Hartemink, Erich D. Jarvis, 2002, ISMB.

MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

Alexander J. Hartemink, Joshua D. Welch, J. Prins, 2017, Genome Biology.

Active Learning of Features and Labels

Mário A. T. Figueiredo, Balaji Krishnapuram, Alexander J. Hartemink, 2005 .

Convergent transcriptional specializations in the brains of humans and song-learning birds

Atsushi Iriki, Morgan Wirthlin, Alexander J. Hartemink, 2014, Science.

Global control of cell-cycle transcription by coupled CDK and network oscillators

Joshua E. S. Socolar, Alexander J. Hartemink, Steven B. Haase, 2008, Nature.

Synergistic and tunable human gene activation by combinations of synthetic transcription factors

Farshid Guilak, Alexander J. Hartemink, Charles A. Gersbach, 2013, Nature Methods.

Finding regulatory DNA motifs using alignment-free evolutionary conservation information

Alexander J. Hartemink, Raluca Gordân, Leelavati Narlikar, 2010, Nucleic acids research.

MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

Alexander J. Hartemink, Joshua D. Welch, J. Prins, 2017, Genome Biology.