Computational network models for molecular, neuronal and brain data in the presence of long range dependence

Thesis

Standard parametric statistical approaches based on comparison to global activity tend to perform poorly when this activity varies over multiple scales. Such multiscale variation, termed long range dependence, is a well-documented features of many biological and neurological data sets. We provide evidence from the literature as well as from data that demonstrates long range dependence across three contexts in: protein, brain and neuronal data. We propose novel non-parametric statistical ap...

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Authors: Wilsenach, J

• DOI: 10.5287/ora-wxp10a2zj
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: non-parametric methods; hidden markov models; network science; long range dependence
• Subjects: Bioinformatics; Proteomics; Computational neuroscience; Computational biology; Applied statistics; Neuroinformatics; Machine learning