An Hidden Markov Model-Based Transcription Factor Mining Method
A text mining algorithm named HMM-TFM (Hidden Markov Model based transcription factor name mining) is presented. The proposed algorithm does not need a dictionary of transcription factor names. A small verb set is defined to filter sentences. Transcription factor names are mined according to the part of speech tagged by hidden Markov model. Experimental results show that the recall and precision of HMM-TFM achieve 74.2% and 77.9%, respectively.
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Document Type: Research Article
Publication date: 01 February 2013
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- Bionanoscience attempts to harness various functions of biological macromolecules and integrate them with engineering for technological applications. It is based on a bottom-up approach and encompasses structural biology, biomacromolecular engineering, material science, and engineering, extending the horizon of material science. The journal aims at publication of (i) Letters (ii) Reviews (3) Concepts (4) Rapid communications (5) Research papers (6) Book reviews (7) Conference announcements in the interface between chemistry, physics, biology, material science, and technology. The use of biological macromolecules as sensors, biomaterials, information storage devices, biomolecular arrays, molecular machines is significantly increasing. The traditional disciplines of chemistry, physics, and biology are overlapping and coalescing with nanoscale science and technology. Currently research in this area is scattered in different journals and this journal seeks to bring them under a single umbrella to ensure highest quality peer-reviewed research for rapid dissemination in areas that are in the forefront of science and technology which is witnessing phenomenal and accelerated growth.
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