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Meta-Learning on QSAR data

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Automatisierte Medienanalyse

Erkannte Entitäten
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me and my 4 inches high
Domain <Netzwerk>
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Gruppe <Mathematik>
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Kompakter Raum
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Vervollständigung <Mathematik>
Profil <Aerodynamik>
Boolesche Algebra
Kollaboration <Informatik>
Ordnung <Mathematik>
Tabelle <Informatik>
Folge <Mathematik>
Mathematische Logik
Virtuelle Maschine
Weg <Topologie>
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Elektronischer Fingerabdruck
Tabelle <Informatik>
Anwendungsspezifischer Prozessor
Wort <Informatik>
Prozess <Physik>
Statistische Hypothese
Computerunterstütztes Verfahren
Technische Optik
Komplex <Algebra>
Lineares Funktional
Flüssiger Zustand
Kommutator <Quantentheorie>
Konfiguration <Informatik>
Arithmetisches Mittel
Automatische Indexierung
Projektive Ebene
Web Site
Zellularer Automat
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Rhombus <Mathematik>
Strom <Mathematik>
Kollaboration <Informatik>
Physikalisches System
Dreiecksfreier Graph
Räumliche Anordnung
Brennen <Datenverarbeitung>
Program Slicing
Partielle Differentiation
Bridge <Kommunikationstechnik>
Gesetz <Physik>
Statistische Hypothese
Deskriptive Statistik
Trennschärfe <Statistik>
Anwendungsspezifischer Prozessor
Metropolitan area network
Gebäude <Mathematik>
Flüssiger Zustand
Rhombus <Mathematik>
Dienst <Informatik>
Rechter Winkel
Zellularer Automat
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Familie <Mathematik>
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Stabilitätstheorie <Logik>
Mathematische Logik
Klasse <Mathematik>
Automatische Handlungsplanung
Delisches Problem
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Elektronische Publikation
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Maschinelles Sehen
Wohlgeordnete Menge
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Kartesische Koordinaten
Figurierte Zahl
Zentrische Streckung
Nichtlinearer Operator
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Funktion <Mathematik>
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Kategorie <Mathematik>
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Gebäude <Mathematik>
Kombinatorische Gruppentheorie
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Lineare Regression
Inverser Limes
Mapping <Computergraphik>
Singularität <Mathematik>
Innerer Automorphismus
Shape <Informatik>


Formale Metadaten

Titel Meta-Learning on QSAR data
Serientitel OpenML 2014
Autor King, Ross D
Vanschoren, Joaquin
Mitwirkende CityTV (recording)
Lizenz CC-Namensnennung 3.0 Deutschland:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
DOI 10.5446/16277
Herausgeber Vanschoren, Joaquin
Erscheinungsjahr 2014
Sprache Englisch
Produktionsort Eindhoven, The Netherlands

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Abstract Can we learn how to design drugs? Topics include: Automating drug discovery with the Robot Scientist. Using chemoinformatic databases and in-house datasets to systematically run extensive comparative QSAR experiments. Learning how to better apply existing QSAR methods. Decreasing the time and cost to develop new drugs. Prof. Dr. Ross D. King is Professor of Machine Intelligence in the School of Computer Science at the University of Manchester. King's research interests are in the automation of science, drug design, AI, machine learning and synthetic biology. He is probably best known for the Robot Scientist.
Schlagwörter Drug discovery
QSAR data
Automation of science

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