Statistical Inference from High Dimensional Data

Dear Colleagues,

Continuous improvement and cost reduction in next-generation sequencing platforms is enabling better understanding of multifactorial and complex pathologies such as cancer. This is the typical problem in which the amount of data matters and where, in addition, the so-called curse of dimensionality occurs (the number of variables is many orders of magnitude greater than the number of cases). In this Special Issue, we welcome contributions that apply different approaches of Statistical Inference or Machine Learning for the characterization of complex pathologies using -omic data. We strongly encourage interdisciplinary works with real data (TCGA, HMP, clinicogenomic data or related datasets) and heterogeneous data integration (clinical, genomic, proteomic, and so on).

This Special Issue solicit submissions in, but not limited to, the following areas:

• Applications based on statistical inference from high dimensional data;
• Dimensionality reduction with imbalanced biological datasets;
• Applications based on feature selection (e.g., text processing, bioinformatics, medical informatics and natural language processing);
• Applications based on Information Theory for data integration (e.g., semantic interoperability, clustering, classification);
• Applications based on feature selection methods using meta-heuristic search methods such as genetic algorithms, particle swarm optimization and so on;
• Applications based on feature extraction (e.g., PCA, LDA);
• Applications based on prior knowledge (e.g., ontologies, pathways).

Volume II: Special Issue "Statistical Inference from High Dimensional Data II"

Dr. Carlos Fernandez-Lozano
Guest Editor

Manuscript Submission Information

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