Literature DB >> 15073010

Data mining in bioinformatics using Weka.

Eibe Frank1, Mark Hall, Len Trigg, Geoffrey Holmes, Ian H Witten.   

Abstract

UNLABELLED: The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it. AVAILABILITY: http://www.cs.waikato.ac.nz/ml/weka.

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Year:  2004        PMID: 15073010     DOI: 10.1093/bioinformatics/bth261

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  292 in total

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Journal:  J Ind Microbiol Biotechnol       Date:  2011-10-26       Impact factor: 3.346

2.  Lancet: a high precision medication event extraction system for clinical text.

Authors:  Zuofeng Li; Feifan Liu; Lamont Antieau; Yonggang Cao; Hong Yu
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3.  Highly reproducible label free quantitative proteomic analysis of RNA polymerase complexes.

Authors:  Amber L Mosley; Mihaela E Sardiu; Samantha G Pattenden; Jerry L Workman; Laurence Florens; Michael P Washburn
Journal:  Mol Cell Proteomics       Date:  2010-11-03       Impact factor: 5.911

4.  Intravascular optical coherence tomography detection of atherosclerosis and inflammation in murine aorta.

Authors:  Satoko Tahara; Toshifumi Morooka; Zhao Wang; Hiram G Bezerra; Andrew M Rollins; Daniel I Simon; Marco A Costa
Journal:  Arterioscler Thromb Vasc Biol       Date:  2012-02-02       Impact factor: 8.311

5.  Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting.

Authors:  David K Crockett; Perry G Ridge; Andrew R Wilson; Elaine Lyon; Marc S Williams; Scott P Narus; Julio C Facelli; Joyce A Mitchell
Journal:  Genome Med       Date:  2012-05-28       Impact factor: 11.117

6.  NeuroD1 reprograms chromatin and transcription factor landscapes to induce the neuronal program.

Authors:  Abhijeet Pataskar; Johannes Jung; Pawel Smialowski; Florian Noack; Federico Calegari; Tobias Straub; Vijay K Tiwari
Journal:  EMBO J       Date:  2015-10-29       Impact factor: 11.598

7.  Data-driven advice for applying machine learning to bioinformatics problems.

Authors:  Randal S Olson; William La Cava; Zairah Mustahsan; Akshay Varik; Jason H Moore
Journal:  Pac Symp Biocomput       Date:  2018

8.  Identification of non-coding RNAs with a new composite feature in the Hybrid Random Forest Ensemble algorithm.

Authors:  Supatcha Lertampaiporn; Chinae Thammarongtham; Chakarida Nukoolkit; Boonserm Kaewkamnerdpong; Marasri Ruengjitchatchawalya
Journal:  Nucleic Acids Res       Date:  2014-04-25       Impact factor: 16.971

9.  Species translatable blood gene signature as a marker of exposure to smoking: computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge.

Authors:  Ömer Sinan Saraç; Rahul Kumar; Sandeep Kumar Dhanda; Ali Tuğrul Balcı; İsmail Bilgen; Roberto Romero; Adi L Tarca
Journal:  Comput Toxicol       Date:  2017-04-28

10.  Elevated TNFR1 and serotonin in bone metastasis are correlated with poor survival following bone metastasis diagnosis for both carcinoma and sarcoma primary tumors.

Authors:  Antonella Chiechi; Chiara Novello; Giovanna Magagnoli; Emanuel F Petricoin; Jianghong Deng; Maria S Benassi; Piero Picci; Iosif Vaisman; Virginia Espina; Lance A Liotta
Journal:  Clin Cancer Res       Date:  2013-03-14       Impact factor: 12.531

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