Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes.
BACKGROUND
Nonlinear small datasets, which are characterized by low numbers of samples and very high numbers of measures, occur frequently in computational biology, and pose problems in their investigation. Unsupervised hybrid-two-phase (H2P) procedures-specifically dimension reduction (DR), coupled with clustering-provide valuable assistance, not only for unsupervised data classification, but also for visualization of the patterns hidden in high-dimensional feature space.


METHODS
'Minimum Curvilinearity' (MC) is a principle that-for small datasets-suggests the approximation of curvilinear sample distances in the feature space by pair-wise distances over their minimum spanning tree (MST), and thus avoids the introduction of any tuning parameter. MC is used to design two novel forms of nonlinear machine learning (NML): Minimum Curvilinear embedding (MCE) for DR, and Minimum Curvilinear affinity propagation (MCAP) for clustering.


RESULTS
Compared with several other unsupervised and supervised algorithms, MCE and MCAP, whether individually or combined in H2P, overcome the limits of classical approaches. High performance was attained in the visualization and classification of: (i) pain patients (proteomic measurements) in peripheral neuropathy; (ii) human organ tissues (genomic transcription factor measurements) on the basis of their embryological origin.


CONCLUSIONS
MC provides a valuable framework to estimate nonlinear distances in small datasets. Its extension to large datasets is prefigured for novel NMLs. Classification of neuropathic pain by proteomic profiles offers new insights for future molecular and systems biology characterization of pain. Improvements in tissue embryological classification refine results obtained in an earlier study, and suggest a possible reinterpretation of skin attribution as mesodermal.


BACKGROUND
https://sites.google.com/site/carlovittoriocannistraci/home.
Cannistraci
Carlo Vittorio
CV
Computational Bioscience Research Center, Division of Chemical and Life Sciences and Engineering, King Abdullah University for Science and Technology (KAUST), Jeddah, Kingdom of Saudi Arabia. kalokagathos.agon@gmail.com
Ravasi
Timothy
T
Montevecchi
Franco Maria
FM
Ideker
Trey
T
Alessio
Massimo
M
D000465:Algorithms; D001185:Artificial Intelligence; D002460:Cell Line; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D004622:Embryo, Mammalian / Q000166:cytology; D006801:Humans; D010146:Pain / Q000145:classification*; D010523:Peripheral Nervous System Diseases / Q000134:cerebrospinal fluid / Q000503:physiopathology*; D014157:Transcription Factors / Q000235:genetics
D014157:Transcription Factors
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