06323cam a2200637Ii 4500 ocn918135780 OCoLC 20190328114812.0 m o d cr cnu|||unuuu 150813s2015 mau ob 001 0 eng d N$T eng rda pn N$T EBLCP N$T YDXCP OPELS IDEBK UMI OCLCF STF DEBBG B24X7 COO OCLCO CDX D6H AU@ SCB Z5A VT2 VGM LIV OCLCQ U3W CEF OCLCQ WYU 919515530 924055503 956678784 988654386 1008960047 1066407451 9780128026465 (electronic bk.) 0128026464 (electronic bk.) 0128025085 9780128025086 9780128025086 (OCoLC)918135780 (OCoLC)919515530 (OCoLC)924055503 (OCoLC)956678784 (OCoLC)988654386 (OCoLC)1008960047 (OCoLC)1066407451 QH324.2 .T73 2015eb SCI 007000 bisacsh 572.80285 23 Emerging trends in computational biology, bioinformatics, and systems biology : algorithms and software tools / [electronic resource] edited by Quoc Nam Tran, Hamid Arabnia. Waltham, MA : Elsevier : Morgan Kaufman, 2015 �2015 1 online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier Emerging trends in computer science & applied computing Vendor-supplied metadata. Includes bibliographical references and index. This book discusses the latest developments in all aspects of computational biology, bioinformatics, and systems biology and the application of data-analytics and algorithms, mathematical modeling, and simulation techniques. It addresses the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological and behavioral systems; presents a systematic approach for storing, retrieving, organizing and analyzing biological data using software tools with applications; provides a systems biology perspective including general guidelines and techniques for obtaining, integrating and analyzing complex data sets from multiple experimental sources using computational tools and software. -- Edited summary from book. Front Cover; Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology: Algorithms and Software Tools; Copyright; Contents; Contributors; Preface; Acknowledgments; Introduction; Chapter 1: Supervised Learning with the Artificial Neural Networks Algorithm for Modeling Immune Cell Differentiation; 1. Introduction; 1.A. Immune cell differentiation and modeling; 1.B. MSM and model reduction; 1.C. ANN algorithm and its applications; 2. Related work; 3. Modeling immune cell differentiation; 3.1. T cell differentiation process as a use case. 3.2. Data for training and testing models3.3. ANN model; 3.4. Comparative analysis with the LR model and SVM; 3.5. Capability of ANN model to analyze data with noise; 4. Discussion; 5. Conclusion; References; References; Chapter 2: Accelerating Techniques for Particle Filter Implementations on FPGA; 1. Introduction; 2. PF and SLAM algorithms; 2.1. Particle filtering; 2.2. Application of PF to SLAM; 3. Computational bottleneck identification and hardware/software partitioning; 4. PF acceleration techniques; 4.1. CORDIC acceleration technique; 4.2. Ziggurat acceleration technique. 5. Hardware implementation6. Hardware/software Architecture; 7. Results and discussion; 8. Conclusions; References; Chapter 3: Biological Study on Pulsatile Flow of Herschel-Bulkley Fluid in Tapered Blood Vessels; 1. Introduction; 2. Formulation of the problem; 3. Solution; 4. Discussion; 5. Conclusion; References; Chapter 4: Hierarchical k-Means: A Hybrid Clustering Algorithm and Its Application to Study Gene Expression in Lung Adeno ... ; 1. Introduction; 2. Methods; 3. Data Set; 4. Results and Discussion; 5. Conclusions; References; Supplementary Materials. Chapter 5: Molecular Classification of N-Aryloxazolidinone-5-carboxamides as Human Immunodeficiency Virus Protease Inhibitors1. Introduction; 2. Computational method; 3. Classification algorithm; 4. Information entropy; 5. The EC of entropy production; 6. Learning procedure; 7. Calculation results and discussion; 8. Conclusions; Acknowledgment; References; Chapter 6: Review of Recent Protein-Protein Interaction Techniques; 1. Introduction; 2. Technical challenges and open issues; 3. Performance measures; 4. Computational approaches; 4.1. Sequence-based approaches. 4.1.1. Statistical sequence-based approaches4.1.1.1. Mirror tree method; 4.1.1.2. PIPE; 4.1.1.3. CD; 4.1.2. ML sequence-based approaches; 4.1.2.1. Auto covariance; 4.1.2.2. Pairwise similarity; 4.1.2.3. AA composition; 4.1.2.4. AA Triad; 4.1.2.5. UNISPPI; 4.1.2.6. ETB-Viterbi; 4.2. Structure-based approaches; 4.2.1. Template structure-based approaches; 4.2.1.1. PRISM; 4.2.1.2. PrePPI; 4.2.2. Statistical structure-based approaches; 4.2.2.1. PID matrix score; 4.2.2.2. PreSPI; 4.2.2.3. DCC; 4.2.2.4. MEGADOCK; 4.2.2.5. Meta approach; 4.2.3. ML structure-based approaches; 4.2.3.1. Random Forest. Computational biology. Bioinformatics. Systems biology. SCIENCE Life Sciences Biochemistry. bisacsh Bioinformatics. fast (OCoLC)fst00832181 Computational biology. fast (OCoLC)fst00871990 Systems biology. fast (OCoLC)fst01745552 Biology - General. hilcc Biology. hilcc Health & Biological Sciences. hilcc Electronic books. Electronic book. Tran, Quoc-Nam, editor. Arabnia, Hamid, editor. Print version: Tran, Quoc-Nam. Emerging trends in computational biology, bioinformatics, and systems biology. Amsterdam : Morgan Kaufmann, 2015 9780128025086 (OCoLC)915354962 Emerging trends in computer science & applied computing. ScienceDirect http://www.sciencedirect.com/science/book/9780128025086 247130 247130