Literature DB >> 19163538

Intelligible machine learning with malibu.

Robert E Langlois1, Hui Lu.   

Abstract

malibu is an open-source machine learning work-bench developed in C/C++ for high-performance real-world applications, namely bioinformatics and medical informatics. It leverages third-party machine learning implementations for more robust bug-free software. This workbench handles several well-studied supervised machine learning problems including classification, regression, importance-weighted classification and multiple-instance learning. The malibu interface was designed to create reproducible experiments ideally run in a remote and/or command line environment. The software can be found at: http://proteomics.bioengr. uic.edu/malibu/index.html.

Mesh:

Year:  2008        PMID: 19163538     DOI: 10.1109/IEMBS.2008.4650035

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  6 in total

1.  NAPS: a residue-level nucleic acid-binding prediction server.

Authors:  Matthew B Carson; Robert Langlois; Hui Lu
Journal:  Nucleic Acids Res       Date:  2010-05-16       Impact factor: 16.971

2.  Boosting the prediction and understanding of DNA-binding domains from sequence.

Authors:  Robert E Langlois; Hui Lu
Journal:  Nucleic Acids Res       Date:  2010-02-15       Impact factor: 16.971

3.  An improved machine learning protocol for the identification of correct Sequest search results.

Authors:  Morten Källberg; Hui Lu
Journal:  BMC Bioinformatics       Date:  2010-12-07       Impact factor: 3.169

4.  Network-based prediction and knowledge mining of disease genes.

Authors:  Matthew B Carson; Hui Lu
Journal:  BMC Med Genomics       Date:  2015-05-29       Impact factor: 3.063

5.  Classification of Benign and Malignant Thyroid Nodules Using a Combined Clinical Information and Gene Expression Signatures.

Authors:  Bing Zheng; Jun Liu; Jianlei Gu; Jing Du; Lin Wang; Shengli Gu; Juan Cheng; Jun Yang; Hui Lu
Journal:  PLoS One       Date:  2016-10-24       Impact factor: 3.240

6.  DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool.

Authors:  Graham B Motion; Andrew J M Howden; Edgar Huitema; Susan Jones
Journal:  Nucleic Acids Res       Date:  2015-08-24       Impact factor: 16.971

  6 in total

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