Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE
By Barrett C. Foat, Alexandre V. Morozov, Harmen J. Bussemaker. Intelligent Systems in Molecular Biology, 2006
308 citations recorded by the source corpus.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/ca347bdf2dd0915e734c993720583b4eda1c08ac
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In Molecular Biology
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Citations in the corpus
- Evaluation of methods for modeling transcription-factor sequence specificity (2013, methodology): “Third, we wanted to incorporate published algorithms that were not represented in the challenge, including three biophysical energy-based algorithms, BEEML-PBM 24, 25 , FeatureREDUCE (TRR and HJB, manuscript in preparation) and MatrixREDUCE 26 , as well as two statistical algorithms, RankMotif++ 27 ”
- How to infer gene networks from expression profiles (2007, methodology): “A description of other methods based on the physical approach and more details on computational aspects can be found in (Beer and Tavazoie, 2004; Tadesse et al , 2004; Faith and Gardner, 2005; Prakash and Tompa, 2005; Ambesi and di Bernardo, 2006; Foat et al , 2006).”
- RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach (2016, background): “For instance, MatrixREDUCE simply fits a statistical mechanical model to infer the sequence-specific binding sites for transcription factors from sequences [10].”
- Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning (2015, methodology): “DeepBind consistently had better overall performance than MartixREDUCE 19 and Ray et al. ’s 9 PWM_align_Z method ( Fig.”
- UniPROBE, update 2015: new tools and content for the online database of protein-binding microarray data on protein–DNA interactions (2014, methodology): “Among those on which we may choose to focus initially are FeatureREDUCE (manuscript in preparation) and MatrixREDUCE (36), which also performed well in the DREAM5 challenge (18).”