  With different genomes available, unsupervised learning algorithms are
essential in learning genome-wide biological insights. Especially, the
functional characterization of different genomes is essential for us to
understand lives. In this book chapter, we review the state-of-the-art
unsupervised learning algorithms for genome informatics from DNA to MicroRNA.
  DNA (DeoxyriboNucleic Acid) is the basic component of genomes. A significant
fraction of DNA regions (transcription factor binding sites) are bound by
proteins (transcription factors) to regulate gene expression at different
development stages in different tissues. To fully understand genetics, it is
necessary of us to apply unsupervised learning algorithms to learn and infer
those DNA regions. Here we review several unsupervised learning methods for
deciphering the genome-wide patterns of those DNA regions.
  MicroRNA (miRNA), a class of small endogenous non-coding RNA (RiboNucleic
acid) species, regulate gene expression post-transcriptionally by forming
imperfect base-pair with the target sites primarily at the 3$'$ untranslated
regions of the messenger RNAs. Since the 1993 discovery of the first miRNA
\emph{let-7} in worms, a vast amount of studies have been dedicated to
functionally characterizing the functional impacts of miRNA in a network
context to understand complex diseases such as cancer. Here we review several
representative unsupervised learning frameworks on inferring miRNA regulatory
network by exploiting the static sequence-based information pertinent to the
prior knowledge of miRNA targeting and the dynamic information of miRNA
activities implicated by the recently available large data compendia, which
interrogate genome-wide expression profiles of miRNAs and/or mRNAs across
various cell conditions.
