Overview of Commonly Used Bioinformatics Methods and Their Applications

Abstract: Bioinformatics, in its broad sense, involves application of computer processes to solve biological problems. A wide range of computational tools are needed to effectively and efficiently process large amounts of data being generated as a result of recent technological innovations in biology and medicine. A number of computational tools have been developed or adapted to deal with the experimental riches of complex and multivariate data and transition from data collection to information or knowledge. These include a wide variety of clustering and classification algorithms, including self‐organized maps (SOM), artificial neural networks (ANN), support vector machines (SVM), fuzzy logic, and even hyphenated techniques as neuro‐fuzzy networks. These bioinformatics tools are being evaluated and applied in various medical areas including early detection, risk assessment, classification, and prognosis of cancer. The goal of these efforts is to develop and identify bioinformatics methods with optimal sensitivity, specificity, and predictive capabilities.

[1]  A Macario,et al.  Fuzzy logic: theory and medical applications. , 1996, Journal of cardiothoracic and vascular anesthesia.

[2]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[3]  Evangelos Triantaphyllou,et al.  Fuzzy logic in computer-aided breast cancer diagnosis: analysis of lobulation , 1997, Artif. Intell. Medicine.

[4]  Friedrich Steimann,et al.  Fuzzy set theory in medicine , 1997, Artif. Intell. Medicine.

[5]  Thomas Löser,et al.  Tumour markers in the diagnosis of bronchial carcinoma: new options using fuzzy logic-based tumour marker profiles , 1998, Journal of Cancer Research and Clinical Oncology.

[6]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[7]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Moshe Sipper,et al.  A fuzzy-genetic approach to breast cancer diagnosis , 1999, Artif. Intell. Medicine.

[9]  Lenz,et al.  Chemical ligands, genomics and drug discovery. , 2000, Drug discovery today.

[10]  Moshe Sipper,et al.  Evolutionary computation in medicine: an overview , 2000, Artif. Intell. Medicine.

[11]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[12]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[13]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[14]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[15]  D Haussler,et al.  Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Ash A. Alizadeh,et al.  'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns , 2000, Genome Biology.

[17]  P. Woolf,et al.  A fuzzy logic approach to analyzing gene expression data. , 2000, Physiological genomics.

[18]  Maysam F. Abbod,et al.  Survey of utilisation of fuzzy technology in Medicine and Healthcare , 2001, Fuzzy Sets Syst..

[19]  J. Dayhoff,et al.  Artificial neural networks , 2001, Cancer.

[20]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[21]  John Quackenbush,et al.  Computational genetics: Computational analysis of microarray data , 2001, Nature Reviews Genetics.

[22]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[23]  M. Ringnér,et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.

[24]  Johann Gasteiger,et al.  Neural networks and genetic algorithms in drug design , 2001 .

[25]  Robert F. Murphy,et al.  A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells , 2001, Bioinform..

[26]  Irwin D. Kuntz,et al.  A genetic algorithm for structure-based de novo design , 2001, J. Comput. Aided Mol. Des..

[27]  Maysam F. Abbod,et al.  A survey of fuzzy logic monitoring and control utilisation in medicine , 2001, Artif. Intell. Medicine.

[28]  Sayan Mukherjee,et al.  Molecular classification of multiple tumor types , 2001, ISMB.

[29]  Carsten O. Peterson,et al.  Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. , 2001, Cancer research.

[30]  W. Evans,et al.  Pharmacogenomics: unlocking the human genome for better drug therapy. , 2001, Annual review of pharmacology and toxicology.

[31]  Brijesh Verma,et al.  A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques , 2001, IEEE Transactions on Information Technology in Biomedicine.

[32]  A Errejon,et al.  Use of artificial neural networks in prostate cancer. , 2001, Molecular urology.

[33]  T. Poggio,et al.  Multiclass cancer diagnosis using tumor gene expression signatures , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[34]  J. E. Dayhoff,et al.  Medical applications of neural networks: measures of certainty and statistical tradeoffs , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[35]  E. Petricoin,et al.  Use of proteomic patterns in serum to identify ovarian cancer , 2002, The Lancet.

[36]  Stephen G Oliver,et al.  Dynamics of Protein Turnover, a Missing Dimension in Proteomics* , 2002, Molecular & Cellular Proteomics.

[37]  G. Li,et al.  An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers , 2002, Bioinform..

[38]  P. Schellhammer,et al.  Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. , 2002, Cancer research.

[39]  Todd,et al.  Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning , 2002, Nature Medicine.

[40]  Noam Harpaz,et al.  Artificial neural networks distinguish among subtypes of neoplastic colorectal lesions. , 2002, Gastroenterology.

[41]  M. Ringnér,et al.  Analyzing array data using supervised methods. , 2002, Pharmacogenomics.

[42]  The use of chemical design tools to transform proteomics data into drug candidates. , 2002, BioTechniques.

[43]  P. Schellhammer,et al.  Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. , 2002, Clinical chemistry.

[44]  Ting-Fung Chan,et al.  Chemical genomics: a systematic approach in biological research and drug discovery. , 2002, Current issues in molecular biology.

[45]  Jing Yin,et al.  Artificial neural networks and gene filtering distinguish between global gene expression profiles of Barrett's esophagus and esophageal cancer. , 2002, Cancer research.

[46]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.