Abstract | ||
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Due to the current high availability of omics, data-driven biology has greatly expanded, and several papers have reviewed state-of-the-art technologies. Nowadays, two main types of investigation are available for a multi-omics dataset: extraction of relevant features for a meaningful biological interpretation and clustering of the samples. In the latter case, a few reviews refer to some outdated or no longer available methods, whereas others lack the description of relevant clustering metrics to compare the main approaches. This work provides a general overview of the major techniques in this area, divided into four groups: graph, dimensionality reduction, statistical and neural-based. Besides, eight tools have been tested both on a synthetic and a real biological dataset. An extensive performance comparison has been provided using four clustering evaluation scores: Peak Signal-to-Noise Ratio (PSNR), Davies-Bouldin(DB) index, Silhouette value and the harmonic mean of cluster purity and efficiency. The best results were obtained by using the dimensionality reduction, either explicitly or implicitly, as in the neural architecture. |
Year | DOI | Venue |
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2022 | 10.1016/j.neucom.2021.11.094 | Neurocomputing |
Keywords | DocType | Volume |
Cancer analysis,Competitive learning,Data fusion,Multi-omics clustering,Neural networks,NGL-F,Unsupervised learning,Unsupervised clustering | Journal | 488 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
2 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Marta Lovino | 1 | 0 | 0.34 |
Vincenzo Randazzo | 2 | 0 | 0.34 |
Gabriele Ciravegna | 3 | 3 | 3.23 |
Pietro Barbiero | 4 | 0 | 0.68 |
Elisa Ficarra | 5 | 0 | 0.34 |
Giansalvo Cirrincione | 6 | 0 | 0.34 |