Motivation and Justification of Naturalistic Method for Bioinformatics Research

This paper introduces and proposes naturalistic method as a trends base for the Bioinformatics research. Naturalistic method emphasizes on finding biodata properties by insight in a real data nature to reflect its de facto and to be as far from the Bioinformatics theoretical assumptions as possible. We present and justify motivating factors in this direction such as studies that depend mainly on hypotheses models lead to the derivation of imperfect biological models, availability of huge real data, furthermore new technologies enable sustainable flow of data. This method aims to find better ways for representing biological data and process. This goal could be reached by finding biodata properties and characteristics. On the other hand, discovered properties could be utilized to enhance different algorithm in Bioinformatics.

[1]  W. Wong,et al.  Computational Biology: Toward Deciphering Gene Regulatory Information in Mammalian Genomes , 2006, Biometrics.

[2]  R. Sowdhamini,et al.  STIF: Identification of stress-upregulated transcription factor binding sites in Arabidopsis thaliana , 2008, Bioinformation.

[3]  Charles Anderson,et al.  The end of theory: The data deluge makes the scientific method obsolete , 2008 .

[4]  Baw-Jhiune Liu,et al.  WildSpan: mining structured motifs from protein sequences , 2011, Algorithms for Molecular Biology.

[5]  Fernando Soriano,et al.  Conducting Needs Assessments: A Multidisciplinary Approach , 1995 .

[6]  Juan M. Vaquerizas,et al.  DNA-Binding Specificities of Human Transcription Factors , 2013, Cell.

[7]  James Bailey,et al.  A binary decision diagram based approach for mining frequent subsequences , 2010, Knowledge and Information Systems.

[8]  Fiona Ni Foong How,et al.  Research methodology: an introduction , 2011 .

[9]  J F Allen,et al.  Bioinformatics and discovery: induction beckons again , 2000, BioEssays : news and reviews in molecular, cellular and developmental biology.

[10]  Peter K. Storkerson.  Naturalistic Cognition: A Research Paradigm for Human-Centered Design , 2010 .

[11]  E. Ramaraj,et al.  Comparative Study of Domain Driven Data Mining for It Infrastructure Suport , 2013 .

[12]  Henry C. M. Leung,et al.  DNA Motif Representation with Nucleotide Dependency , 2008, TCBB.

[13]  T. Hubbard,et al.  Large-Scale Gene Regulatory Motif Discovery with NestedMICA , 2011 .

[14]  Gong Chen,et al.  Heterogeneity in DNA Multiple Alignments: Modeling, Inference, and Applications in Motif Finding , 2010, Biometrics.

[15]  Vladimir Pavlovic,et al.  Efficient motif finding algorithms for large-alphabet inputs , 2010, BMC Bioinformatics.

[16]  Pradeep Kumar,et al.  Pattern Discovery Using Sequence Data Mining: Applications and Studies , 2011 .

[17]  Philip Ball,et al.  DNA: Celebrate the unknowns , 2013, Nature.

[18]  Irving Rothchild,et al.  INDUCTION, DEDUCTION, AND THE SCIENTIFIC METHOD AN ECLECTIC OVERVIEW OF THE PRACTICE OF SCIENCE , 2006 .

[19]  Ioannis P. Androulakis,et al.  Recent Advances in the Computational Discovery of Transcription Factor Binding Sites , 2009, Algorithms.

[20]  D. Kell,et al.  Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. , 2004, BioEssays : news and reviews in molecular, cellular and developmental biology.

[21]  Linda Mabry,et al.  RealWorld Evaluation: Working Under Budget, Time, Data, and Political Constraints , 2006 .

[22]  M. Crawford.  The Human Genome Project. , 1990, Human biology.

[23]  Dan Xie,et al.  Variation and Genetic Control of Protein Abundance in Humans , 2013, Nature.

[24]  Yongqiang Zhang,et al.  SMOTIF: efficient structured pattern and profile motif search , 2006, Algorithms for Molecular Biology.

[25]  Siu-Ming Yiu,et al.  Optimal Algorithm for Finding DNA Motifs with Nucleotide Adjacent Dependency , 2008, APBC.

[26]  K. Priya Karunakaran,et al.  Review of Domain Driven Data Mining , 2013 .

[27]  Longbing Cao,et al.  Domain-Driven Data Mining: Challenges and Prospects , 2010, IEEE Transactions on Knowledge and Data Engineering.

[28]  F. Hamprecht.  Introduction to Statistics , 2022 .

[29]  Gaston H. Gonnet,et al.  Scoring functions for transcription factor binding site prediction , 2005, BMC Bioinformatics.

[30]  Maguelonne Teisseire,et al.  Successes and New Directions in Data Mining , 2007 .

[31]  Tharam S. Dillon,et al.  Mining of Data with Complex Structures , 2010, Studies in Computational Intelligence.

[32]  Cinzia Pizzi.  Motif Discovery with Compact Approaches - Design and Applications , 2011 .