Gene discovery for facioscapulohumeral muscular dystrophy by machine learning techniques

Facioscapulohumeral muscular dystrophy (FSHD) is a neuromuscular disorder that shows a preference for the facial, shoulder and upper arm muscles. FSHD affects about one in 20-400,000 people, and no effective therapeutic strategies are known to halt disease progression or reverse muscle weakness or atrophy. Many genes may be incorrectly regulated in affected muscle tissue, but the mechanisms responsible for the progressive muscle weakness remain largely unknown. Although machine learning (ML) has made significant inroads in biomedical disciplines such as cancer research, no reports have yet addressed FSHD analysis using ML techniques. This study explores a specific FSHD data set from a ML perspective. We report results showing a very promising small group of genes that clearly separates FSHD samples from healthy samples. In addition to numerical prediction figures, we show data visualizations and biological evidence illustrating the potential usefulness of these results.Peer ReviewedPostprint (published version

Giovanni Anceschi, Percorsi fluidi orizzontali, 1962.

Storia e analisi dell'opera di Giovanni Anceschi, Percorsi fluidi orizzontali, 1962

Distribution, movements and group size of the bottlenose dolphin (Tursiops truncatus) to the south of San Quintín Bay, Baja California, Mexico

 Twelve boat-based photoidentification surveys were carried out along the coast to the south of San Quintín Bay, in Baja California, Mexico, from July 1999 to June 2000; effort was 276.76 km and 31.7 h at sea. Twenty-two schools were encountered and 12.9 h of total observation were spent with 242 dolphins in these schools. The average school size was 11 (SD = 8) dolphins, although it is possible that groups are actually smaller; nursing groups were significantly larger (P 70%) were sighted one time or stayed for short periods. A total of 220 different dolphins have been identified in the San Quintín area when these data are combined with those gathered by Caldwell (1992) in 1990; these dolphins probably represent a small part of a larger population. More research on the population biology of the bottlenose dolphin in this and adjacent geographic areas is needed to develop better conservation and management strategies for this important natural resource

Getulio Alviani, Superficie a testura vibratile 1.2.3.4.5, 1962.

Storia e analisi dell'opera di Getulio Alviani, Superficie a testura vibratile 1.2.3.4.5, 1962

Gene discovery for facioscapulohumeral muscular dystrophy by machine learning techniques

Facioscapulohumeral muscular dystrophy (FSHD) is a neuromuscular disorder that shows a preference for the facial, shoulder and upper arm muscles. FSHD affects about one in 20-400,000 people, and no effective therapeutic strategies are known to halt disease progression or reverse muscle weakness or atrophy. Many genes may be incorrectly regulated in affected muscle tissue, but the mechanisms responsible for the progressive muscle weakness remain largely unknown. Although machine learning (ML) has made significant inroads in biomedical disciplines such as cancer research, no reports have yet addressed FSHD analysis using ML techniques. This study explores a specific FSHD data set from a ML perspective. We report results showing a very promising small group of genes that clearly separates FSHD samples from healthy samples. In addition to numerical prediction figures, we show data visualizations and biological evidence illustrating the potential usefulness of these results.Peer Reviewe