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1. |
Record Nr. |
UNISALENTO991001316899707536 |
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Autore |
Graffi, Sandro |
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Titolo |
Schrodinger operators : lectures given at the 2nd 1984 session of the Centro internationale [sic] matematico estivo (C.I.M.E.) held at Como, Italy, Aug. 26-Sept. 4, 1984 / ed. S. Graffi |
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Pubbl/distr/stampa |
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Berlin ; New York : Springer-Verlag, 1985 |
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ISBN |
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Descrizione fisica |
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Collana |
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Lecture notes in mathematics, 0075-8434 ; 1159 |
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Classificazione |
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AMS 35J |
AMS 35P |
AMS 35Q |
AMS 35R |
AMS 35S |
AMS 47B |
AMS 47E05 |
AMS 47F05 |
AMS 58G |
AMS 81-06 |
AMS 81C (1985) |
AMS 81D (1985) |
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Disciplina |
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Soggetti |
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Schrodinger operator - Congresses |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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2. |
Record Nr. |
UNINA9910367743403321 |
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Autore |
Brenner J. Chad |
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Titolo |
Application of Bioinformatics in Cancers |
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Pubbl/distr/stampa |
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MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
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ISBN |
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Descrizione fisica |
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1 electronic resource (418 p.) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible. |
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