We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
Background: The investigation of plant genome structure and evolution requires comprehensive characterization of repetitive sequences that make up the majority of higher plant nuc...
Background: Clustering methods are widely used on gene expression data to categorize genes with similar expression profiles. Finding an appropriate (dis)similarity measure is crit...
Kyungpil Kim, Shibo Zhang, Keni Jiang, Li Cai, In-...
Background: An adequate and expressive ontological representation of biological organisms and their parts requires formal reasoning mechanisms for their relations of physical aggr...
Background: We present a methodology for high-throughput design of oligonucleotide fingerprints for microarray-based pathogen diagnostic assays. The oligonucleotide fingerprints, ...
Ravi Vijaya Satya, Nela Zavaljevski, Kamal Kumar, ...