SP-BRAIN: scalable and reliable implementations of a supervised relevance-based machine learning algorithm.
SP-BRAIN a supervised machine learning algorithm based on Big Data technologies and applied on the prediticition of DNA splicing sites has been released.
Classification of Large DNA Methylation Datasets for Identifying Cancer Drivers.
BIGBIOCL an algorithm that can apply supervised machine learning methods to datasets with hundreds of thousands of features for the extraction of alternative and equivalent classification models
has been published.
Combining EEG signal processing with supervised methods for Alzheimer's patients classification.
A new procedure for classifying EEG signals has been designed and applyed to real demented and Alzheimer's diseased patients.
A novel method and software for automatically classifying Alzheimer's disease patients by magnetic resonance imaging analysis.
A new paper, method, and software for classifying Magentic Resonance Images has been published in Computer Methods and Programs in Biomedicine.
TCGA2BED: extracting, extending, integrating, and querying The Cancer Genome Atlas.
A software tool to search and retrieve TCGA data and convert them in the structured BED format for their seamless use and integration has been released.
Emanuel Weitschek published several works in international peer reviewed journals, conference proceedings, book chapters, and
technical reports.
You can find his publications below. Take a look also on google scholar
and on pubmed.
International peer reviewed journals
V. Morfino, S. Rampone, E. Weitschek:
SP-BRAIN: scalable and reliable implementations of a supervised relevance-based machine learning algorithm.
Soft Computing, 10.1007/s00500-019-04366-9, 2019. Impact Factor: 2.78
E. Cappelli, G. Felici, E. Weitschek:
Combining DNA methylation and RNA sequencing data of cancer for supervised knowledge extraction.
BioData Mining, 11:22, 2018. Impact Factor: 2.07
E. Weitschek, S. Di Lauro, E. Cappelli, P. Bertolazzi, G. Felici:
CamurWeb: a classification software and a large knowledge base for gene expression data of cancer.
BMC Bioinformatics, 19(S10):354, 2018. Impact Factor: 3.11
G. Fiscon*, E. Weitschek*, A. Cialini, G. Felici, P. Bertolazzi, S. De Salvo, A. Bramanti, P. Bramanti, M.C. De Cola:
Combining EEG signal processing with supervised methods for Alzheimer's patients classification.
BMC Medical Informatics and Decision Making, 18:35, 2018. Impact Factor: 2.36 *(equal contributors)
F. Celli, F. Cumbo, E. Weitschek:
Classification of Large DNA Methylation Datasets for Identifying Cancer Drivers.
Big Data Research, 13:21-28, 2018.
F. Previtali, P. Bertolazzi, G. Felici, E. Weitschek:
A novel method and software for automatically classifying Alzheimer's disease patients by magnetic resonance imaging analysis.
Computer Methods and Programs in Biomedicine, 143:89-95 2017. Impact Factor: 2.5.
F. Cumbo*, G. Fiscon*, S. Ceri, M. Masseroli, E. Weitschek*:
TCGA2BED: extracting, extending, integrating, and querying The Cancer Genome Atlas.
BMC Bioinformatics, 18:6, 2017. Impact Factor: 2.5 *(equal contributors).
G.Fiscon*, E. Weitschek*, E. Cella, A. Lo Presti, M. Giovanetti, M. Babakir-Mina, M. Ciotti, M. Ciccozzi, A. Pierangeli, P. Bertolazzi, G. Felici:
MISSEL: a method to identify a large number of small species-specific genomic subsequences and its application to viruses classification.
BioData Mining, 9:38, 2016. Impact Factor: 1.64 *(equal contributors).
V. Cestarelli*, G. Fiscon*, G. Felici, P. Bertolazzi, E. Weitschek*:
CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules.
Bioinformatics, 32(5): 697-704, 2016. Impact Factor: 5.0 *(equal contributors).
P. Bertolazzi, G. Felici, P. Festa, G. Fiscon, E. Weitschek:
Integer programming models for feature selection: new extensions and a randomized solution algorithm.
European Journal of Operational Research, 250(2):389-399, 2016. Impact Factor: 2.36
K. Wilkins, ..., G. Fiscon, E. Weitschek, M. Ciccozzi, P. Bertolazzi, G. Felici, et. al:
A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification -
Highlights from the 11th ISCB Student Council Symposium 2015.
BMC Bioinformatics, 7(3):203, 2016. Impact Factor: 2.57
D. Santoni, E. Weitschek, G. Felici:
Optimal discretization and selection of features by association rates of joint distributions.
RAIRO Operational Research, 50(2):437-449, 2016. Impact Factor: 0.33
E. Weitschek*, F. Cunial, G. Felici:
LAF: Logic Alignment Free and its application to bacterial genomes classification.
BioData Mining, 8(1):39, 2015. Impact Factor: 2.02.
E. Weitschek, D. Santoni, G. Fiscon, M.C. De Cola, P. Bertolazzi, G. Felici:
Next generation sequencing reads comparison with an alignment-free distance.
BMC Research Notes, 7:869, 2014.
D. Polychronopoulos*, E. Weitschek*, S. Dimitrieva, P. Bucher, G. Felici, Y. Almirantis:
Classification of selectively constrained DNA elements using feature vectors and rule-based classifiers.
Elsevier Genomics, 104(2):79-86, 2014. Impact Factor: 2.8 *(joint first authors).
E. Weitschek*, G. Fiscon*, G. Felici:
Supervised DNA Barcodes species classification: analysis, comparisons, and results.
BioData Mining, 7(1):4, 2014. Impact Factor: 1.54 *(joint first authors).
E. Weitschek, R. van Velzen, G. Felici, P. Bertolazzi:
BLOG 2.0: a software system for character-based species classification with DNA Barcode sequences.
What it does, how to use it.
Molecular ecology resources, 13(6):1043-1046, 2013. Impact Factor: 7.4.
E. Weitschek, A.L. Presti, G. Drovandi, G. Felici, M. Ciccozzi, M. Ciotti, P. Bertolazzi:
Human polyomaviruses identification by logic mining techniques.
Virology journal, 9(1):1-6, 2012 .Impact Factor: 2.04.
R. van Velzen, E. Weitschek, G. Felici, F.T. Bakker:
DNA barcoding of recently diverged species: relative performance of matching methods.
PloS one, 7(1):e30490, 2012. Impact Factor: 3.73.
M.C. De Cola, G. Felici, D. Santoni, E. Weitschek:
Filtering with alignment free distances for high throughput DNA reads assembly.
EMBnet.journal, 18(B):23-25, 2012.
I. Arisi, M. D'Onofrio, R. Brandi, A. Felsani, S. Capsoni, G. Drovandi, G. Felici, E. Weitschek,
P. Bertolazzi, A. Cattaneo:
Gene expression biomarkers in the brain of a mouse model for Alzheimer's disease:
mining of microarray data by logic classification and feature selection.
Journal of Alzheimer's Disease, 24(4):721-738, 2011. Impact Factor: 4.17.
P. Bertolazzi, G. Felici, E. Weitschek:
Learning to classify species with barcodes.
BMC Bioinformatics, 10(Suppl 14), S7, 2009 Impact Factor: 3.02.
V. Morfino, S. Rampone, E. Weitschek:
A Comparison of Apache Spark Supervised Machine Learning Algorithms for DNA Splicing Site Prediction.
Neural Approaches to Dynamics of Signal Exchanges. Smart Innovation, Systems and Technologies, 151, 133-143,
Springer, Singapore, 2020.
E. Cappelli, E. Weitschek, F. Cumbo:
Smart persistence and accessibility of genomic and clinical data.
Database and Expert Systems Applications (DEXA) 2019. Communications in Computer and Information Science, 1062, 8-14.
Springer, Cham, Linz (Austria), August, 2019.
G.Fiscon, E. Weitschek, M.C. De Cola, G. Felici, P. Bertolazzi:
An integrated approach based on EEG signals processing combined with supervised methods to classify Alzheimer's disease patients.
2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 8621473, 2750-2752,
IEEE, Madrid (Spain), December, 2018.
E. Weitschek, F. Cumbo, E. Cappelli, G. Felici, P. Bertolazzi:
Classifying Big DNA Methylation Data: A Gene-Oriented Approach.
International Conference on Database and Expert Systems Applications, 138-149,
Communications in Computer and Information Science, 903:138-149, Regensburg (Germany), August, 2018.
F. Cumbo, E. Weitschek, P. Bertolazzi, G. Felici:
IRIS-TCGA: an information retrieval and integration system for genomic data of cancer.
Lecture Notes in Bioinformatics vol. 10477, Computational Intelligence Methods for Bioinformatics and Biostatistics, Springer, October, 2017.
E. Cappelli, E. Weitschek:
Extending the Genomic Data Model and the Genometric Query Language with Domain Taxonomies.
Lecture Notes in Computer Science vol. 10360, International Conference on Web Engineering, pp. 567-574, Springer, Cham, Rome (Italy), June, 2017.
D. Assante, C. Fornaro, E. Weitschek, M. Castro, et al.:
Smart open online tool for adaptive education on Cloud Computing.
In Global Engineering Education Conference (EDUCON), pp. 1183-1186. IEEE, Athens (Greece), April, 2017.
E. Weitschek, F. Cumbo, E. Cappelli, G. Felici:
Genomic data integration: A case study on next generation sequencing of cancer.
In Database and Expert Systems Applications (DEXA), 27th International Workshop on Biological Knowledge Discovery, IEEE, Porto (Portugal), September, 2016.
F. Cumbo, E. Weitschek, P. Paci, T. Colombo, P. Bertolazzi, G. Felici:
IRIS-TCGA: an information retrieval and integration system for cancer genomic data.
In 13th international meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB), pp.178-183, A Bracciali, D Gilbert, G MacKenzie (Eds.), Stirling (UK), August, 2016.
E. Weitschek, G. Fiscon, V. Cestarelli, P. Bertolazzi, G. Felici:
LAF Barcoding: classifying DNA Barcode multi-locus sequences with feature vectors and supervised approaches.
In Twelfth international meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB), pp.1-6, ISBN: 9788890643798, Naples (Italy), September, 2015
E. Weitschek, G. Fiscon, G. Felici, P. Bertolazzi:
GELA: a software tool for the analysis of gene expression data.
In Database and Expert Systems Applications (DEXA), 26th International Workshop on Biological Knowledge Discovery, pp. 31-35, ISSN: 1529-4188/15, doi: 10.1109/DEXA.2015.26, IEEE, Valencia (Spain), September, 2015.
E. Weitschek, G. Fiscon, P. Bertolazzi, G. Felici:
Classifying DNA barcode multi-locus sequences with feature vectors and supervised approaches.
GENOME, 58(5), pp. 295-295, doi:10.1139/gen-2015-0087, Ottawa (Canada), August, 2015.
G. Fiscon*, E. Weitschek*, G. Felici, P. Bertolazzi, S. de Salvo, P. Bramante, M.C. De Cola:
Alzheimer's disease patients classification through EEG signals processing.
In Proceedings of SSCI 2014 - IEEE Symposium Series on Computational Intelligence and Data Mining, pp. 105-112.
IEEE, 2014.
E. Weitschek, F. Cunial, G. Felici:
Classifying bacterial genomes on k-mer frequencies with compact logic formulas.
In Database and Expert Systems Applications (DEXA), 25th International Workshop on Biological Knowledge Discovery, pp. 69-73.
IEEE, 2014.
E. Weitschek, G. Felici, P. Bertolazzi:
Clinical data mining: problems, pitfalls and solutions.
In Database and Expert Systems Applications (DEXA), 24th International Workshop on Biological Knowledge Discovery, pp. 90-94.
IEEE, 2013.
E.Weitschek, G. Felici, P. Bertolazzi:
Mala: A microarray clustering and classification software.
In Database and Expert Systems Applications (DEXA), 23rd International Workshop on Biological Knowledge Discovery, pp. 201-205.
IEEE, 2012.
G. Felici, E. Weitschek:
Mining logic models in the presence of noisy data.
In International Symposium on Artificial Intelligence and Mathematics, 2012
G. Fiscon, E. Weitschek:
String-Matching and Alignment Algorithms for Finding Motifs in NGS data.
In Algorithms for Next-Generation Sequencing Data (Techniques, Approaches, and Applications), Elloumi M (Ed.),
Mourad Elloumi Eds, Springer, ISBN 978-3-319-59824-6, 2017.
E. Weitschek, G. Fiscon, V. Fustaino, G. Felici, P. Bertolazzi:
Clustering and Classification Techniques for Gene Expression Profiles Pattern Analysis.
In Pattern Recognition in Computational Molecular Biology: Techniques and Approaches,
Mourad Elloumi, Costas S. Iliopoulos, Jason T. L. Wang and Albert Y. Zomaya Editors, Wiley Book Series
on Bioinformatics: Computational Techniques and Engineering, Wiley-Blackwell, New Jersey, USA (Publisher),
ISBN 978-1118893685, 2015.
E. Weitschek, G. Fiscon, V. Fustaino, G. Felici, P. Bertolazzi: Analysis of microarray and RNA-sequencing gene expression profiles through clustering and classification techniques. IASI-CNR, R. 14-11, 2014
G. Fiscon, E. Weitschek, P. Bertolazzi, M.C. De Cola , S. De Salvo S, P. Bramanti , G. Felici: EEG signals analysis to detect Alzheimer's disease patients. IASI-CNR, R. 14-10, 2014
E. Weitschek, I. Arisi, G. Felici, P. Bertolazzi,. Knowledge extraction in clinical data. IASI-CNR, R. 13-20, 2013.
E. Weitschek, G. Felici, P. Bertolazzi: Microarray Logic Analyzer Software. IASI-CNR, R. 13-18, 2013.
E. Weitschek, G. Fiscon, G. Felici: Supervised Learning Meets DNA Barcoding Species Classification.
IASI-CNR, R. 13-16, 2013.
E. Weitschek, D. Polychronopoulos, Y. Almirantis, G. Felici: Conserved non coding elements classification. IASI-CNR, R. 13-15, 2013.
E. Weitschek, D. Santoni, M.C. De Cola, G. Felici: About similarity of DNA reads. IASI-CNR, R. 13-17, 2013.
G. Felici, E. Weitschek: Mining Logic Models in the Presence of Noisy Data. IASI-CNR, R. 11-25, 2011.
E. Weitschek, R. van Velzen, G. Felici: Species classification using DNA Barcode sequences:
A comparative analysis. IASI-CNR, R. 11-07, 2011.
P. Bertolazzi, G.Felici, E. Weitschek, G. Drovandi, A. Lo Presti, M. Ciccozzi, M. Ciotti: Human Polyomaviruses genome analysis by logic mining techniques, IASI-CNR, R. 10-23, 2010.
E. Weitschek: Logic mining techniques for biological data analysis and classification. Roma Tre University, 2013.
Emanuel Weitschek acted as reviewer for several scientific papers in following international peer reviewed journals, books, and conferences: