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Materiale didattico
Slide utilizzate durante il corso
Le lezioni sono in gran parte in formato pdf. Le letture consigliate
sono in pdf o in postscript.
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Introduction to genome biology
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Introduction to DNA microarray
technologies
Letture:
- Eisen, M. and Brown, P., DNA arrays for analysis of gene expression,
Methods Enzymol., vol. 303, pp. 179-205, 1999.
- Gavin MacBeath Protein
microarrays and proteomics, Nature Genetics vol.32, pp. 526-532, 2002.
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Pre-processing in DNA-microarray experiments:
cDNA microarray
oligonucleotide
microarray
Letture:
- Y.H.Yang, S.Dudoit, P.O. Brown, and T. Speed. Normalization
for cDNA microarray data. In M. L. Bittner, Y. Chen, A.N. DOrsel and
E.R. Dougherty (eds), Microarrays: Optical Technologies ans Informatics, Vol.
4266 of Proceedings of SPIE, 2001 [ UCB
Tech. Report]
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Statistics
for functional bioinformatics
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Computational problems in
functional genomics
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Single gene analysis of differential
expression
Letture:
- Y. H. Yang, S. Dudoit, T. P. Speed, and M. J. Callow
(2002). Statistical
methods for identifying differentially expressed genes in replicated cDNA
microarray experiments. Statistica Sinica, Vol. 12, No. 1,
pp. 111--139. [UCB Tech report
#578]
- W. Pan, A
comparative review of statistical methods for discovering differentially
expressed genes in replicated microarray experiments, Bioinformatics,
18 (4), pp. 546-554, 2002.
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Introduction to clustering methods
for gene expression data analysis
Letture:
- Jain A. Murty M. And Flynn P. Data clustering: a review,
ACM Computing Surveys. (5)31, p. 264-323, 1999.
- Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M,
Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander
ES. Molecular
classification of cancer: class discovery and class prediction by gene expression
monitoring. Science 286(5439):531-7, 1999.
- Ben-Dor, A. and Shamir, R. and Yakhini, Z., Clustering
gene expression patterns, Journal of Computational Biology, (3)6, p.281--297,
1999.
- P.Tamayo, D.Slonim, J.Mesirov,
Q.Zhu, S.Kitareewan, E.Dmitrovsky, E.S.Lander and T.R.Golub, Interpreting
patterns of gene expression with self-organizing maps: Methods and application
to hematopoietic differentiation", Proc.Natl.Acad.Sci.USA,
96, 6, pp.2907-2912, 1999.
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Similarity measures and standardization
for gene expression data clustering
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Hierarchical clustering
for gene expression data analysis
Letture:
- Ash A. Alizadeh
et al. Distinct
Types of Diffuse Large B-Cell Lymphoma Identified by Gene Expression Profiling, Nature vol. 403: no. 6769, pp. 503-511,
2000.
- M.B.Eisen, P.T.Spellman,
P.O.Brown and D.Botstein, Cluster analysis
and display of genome-wide expression patterns, Proc. Natl. Acad.Sci.USA,
95, 25, pp.14863-14868, 1998.
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Clustering algorithms
based on cost function minimization: K-means, Fuzzy and possibilistic c-mean
Letture:
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Introduzione alla
classificazione funzionale di tessuti e geni con metodi di apprendimento automatico
Letture:
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Single layer and Multi-Layer Perceptrons (MLPs)
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L'
algoritmo di backpropagation, Radial Basis Function Network ed alberi di
decisione (non obbligatorio per l' esame)
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An
introduction to Support Vector Machines
(SVMs)
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Supervised
gene expression data analysis using SVMs and MLPs
Letture:
- M. Brown et al., Knowledge-base
analysis of microarray gene expression data by using Support Vector
Machines, PNAS 97(1) pp. 262-267, 2000
- Furey, T.S. Cristianini, N. Duffy, N. Bednarski, D. Schummer,
M. and Haussler, D., Support vector machine
classification and validation of cancer tissue samples using microarray expression
data, Bioinformatics, 16(10) pp. 906-914, 2000.
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Ensemble
methods in bioinformatics (non obbligatorio per l' esame)
Letture:
- G. Valentini, M. Muselli and F. Ruffino, Bagged
Ensembles of SVMs for Gene Expression Data Analysis, IJCNN2003,
The IEEE-INNS-ENNS International Joint Conference on Neural Networks, Portland,
USA, IEEE, 2003.
- G. Valentini, Gene
expression data analysis of human lymphoma using Support Vector Machines and
Output Coding ensembles. Artificial Intelligence in Medicine
26(3) pp 283-306, 2002. © Elsevier, science
direct link
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Parte del materiale didattico e' tratto da diversi corsi svolti
in Europa e negli Stati Uniti.
Un particolare ringraziamento e' rivolto a Sandrine Dudoit (Universita' di
Berkeley) per avere reso disponibili lezioni e slide dei suoi corsi.
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