Feature Selection in the Reconstruction of Complex Network Representations of Spectral Data

PLoS ONE

Volumn 8, Issue 8, 2013, Pages

  Zanin, Massimiliano ^a,b   Menasalvas, Ernestina ^c   Boccaletti, Stefano ^d   Sousa, Pedro ^a  

^a UNIVERSIDADE NOVA DE LISBOA   (Portugal)

^b Innaxis Foundation and Research Institute   (Spain)

^c UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^d CNR   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; DATA ANALYSIS; INFORMATION SCIENCE; MASS SPECTROMETRY; SPECTROSCOPY;

ALGORITHMS; HUMANS; MASS SPECTROMETRY; NEOPLASMS; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; PROTEOME; REPRODUCIBILITY OF RESULTS; ROC CURVE;

EID: 84883198338     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0072045     Document Type: Article

References (39)

1. Hoffmann E (1996) Mass spectrometry. Wiley Online Library.
2. Andersson CO (1958) Mass spectrometric studies on amino acid and peptide derivatives. Acta chem scand 12.
3. Link AJ, Eng J, Schieltz DM, Carmack E, Mize GJ, et al. (1999) Direct analysis of protein complexes using mass spectrometry. Nature biotechnology 17: 676-682.
4. Dettmer K, Aronov PA, Hammock BD, (2007) Mass spectrometry-based metabolomics. Mass spectrometry reviews 26: 51-78.
5. Covey TR, Lee ED, Henion JD, (1986) High-speed liquid chromatography/tandem mass spectrometry for the determination of drugs in biological samples. Analytical Chemistry 58: 2453-2460.
6. Moseley III MA, Sheeley DM, Blackburn RK, Johnson RL, Merrill BM, (1998) Mass spectrometry in drug discovery. Mass Spectrometry of Biological Materials 2: 162.
7. Zanin M, Papo D, Solís JLG, Espinosa JCM, Frausto-Reyes C, et al. (2013) Knowledge discovery in spectral data by means of complex networks. Metabolites 3: 155-167.
8. Newman ME, (2003) The structure and function of complex networks. SIAM review 45: 167-256.
9. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU, (2006) Complex networks: Structure and dynamics. Physics reports 424: 175-308.
10. Anderson PW, (1972) More is different. Science 177: 393-396.
11. Bullmore E, Sporns O, (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience 10: 186-198.
12. Scott J (2012) Social network analysis. SAGE Publications Limited.
13. Zanin M, Lillo F, (2013) Modelling the air transport with complex networks: A short review. The European Physical Journal Special Topics 215: 5-21.
14. Costa LdF, Oliveira Jr ON, Travieso G, Rodrigues FA, Villas Boas PR, et al. (2011) Analyzing and modeling real-world phenomena with complex networks: a survey of applications. Advances in Physics 60: 329-412.
15. Havlin S, Kenett D, Ben-Jacob E, Bunde A, Cohen R, et al. (2012) Challenges in network science: Applications to infrastructures, climate, social systems and economics. The European Physical Journal Special Topics 214: 273-293.
16. Langley P (1996) Elements of machine learning. Morgan Kaufmann.
17. Guyon I, Elisseeff A, (2003) An introduction to variable and feature selection. The Journal of Machine Learning Research 3: 1157-1182.
18. Kira K, Rendell LA (1992) The feature selection problem: Traditional methods and a new algorithm. In: Proceedings of the National Conference on Artificial Intelligence. John Wiley & Sons Ltd, 129-129.
19. Saeys Y, Inza I, Larrañaga P, (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23: 2507-2517.
20. Liu H, Motoda H (2007) Computational methods of feature selection. Chapman and Hall/CRC.
21. Guyon I, Gunn S, Ben-Hur A, Dror G, (2004) Result analysis of the nips 2003 feature selection challenge. Advances in Neural Information Processing Systems 17: 545-552.
22. Issaq HJ, Veenstra TD, Conrads TP, Felschow D, (2002) The seldi-tof ms approach to proteomics: protein profiling and biomarker identification. Biochemical and biophysical research communications 292: 587-592.
23. Petricoin III EF, Ardekani AM, Hitt BA, Levine PJ, Fusaro VA, et al. (2002) Use of proteomic patterns in serum to identify ovarian cancer. The lancet 359: 572-577.
24. Petricoin EF, Ornstein DK, Paweletz CP, Ardekani A, Hackett PS, et al. (2002) Serum proteomic patterns for detection of prostate cancer. Journal of the National Cancer Institute 94: 1576-1578.
25. Adam BL, Qu Y, Davis JW, Ward MD, Clements MA, et al. (2002) Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Research 62: 3609-3614.
26. Griffin J, Williams H, Sang E, Clarke K, Rae C, et al. (2001) Metabolic profiling of genetic disorders: A multitissue (1) H nuclear magnetic resonance spectroscopic and pattern recognition study into dystrophic tissue. Analytical Biochemistry 293: 16-21.
27. Beckonert O, E Bollard M, Ebbels T, Keun HC, Antti H, et al. (2003) Nmr-based metabonomic toxicity classification: hierarchical cluster analysis and k-nearest-neighbour approaches. Analytica Chimica Acta 490: 3-15.
28. Karmeshu J (2003) Entropy measures, maximum entropy principle and emerging applications. Springer-Verlag New York, Inc.
29. Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. In: Machine Learning-International Workshop Then Conference. Morgan Kaufmann Publishers, Inc., 412-420.
30. Fleuret F, (2004) Fast binary feature selection with conditional mutual information. The Journal of Machine Learning Research 5: 1531-1555.
31. Peng H, Long F, Ding C, (2005) Feature selection based on mutual information criteria of maxdependency, max-relevance, and min-redundancy. Pattern Analysis and Machine Intelligence, IEEE Transactions on 27: 1226-1238.
32. Zanin M, Sousa P, Papo D, Bajo R, García-Prieto J, et al. (2012) Optimizing functional network representation of multivariate time series. Scientific reports 2.
33. Hamel LH (2011) Knowledge discovery with support vector machines, volume 3. Wiley-Interscience.
34. Powers D, (2011) Evaluation: From precision, recall and f-measure to roc., informedness, markedness & correlation. Journal of Machine Learning Technologies 2: 37-63.
35. Clark JW (1988) Probabilistic neural networks. Evolution, Learning and Cognition: 129-180.
36. Specht DF, (1990) Probabilistic neural networks. Neural networks 3: 109-118.
37. Zweig MH, Campbell G, (1993) Receiver-operating characteristic (roc) plots: a fundamental evaluation tool in clinical medicine. Clinical chemistry 39: 561-577.
38. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, et al. (2002) Network motifs: simple building blocks of complex networks. Science Signaling 298: 824.
39. Fortunato S, (2010) Community detection in graphs. Physics Reports 486: 75-174.