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BMCBI
2008
165views more  BMCBI 2008»
13 years 7 months ago
Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics
Background: Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides...
Wiebke Timm, Alexandra Scherbart, Sebastian Bö...
MLCW
2005
Springer
14 years 26 days ago
Lessons Learned in the Challenge: Making Predictions and Scoring Them
In this paper we present lessons learned in the Evaluating Predictive Uncertainty Challenge. We describe the methods we used in regression challenges, including our winning method ...
Jukka Kohonen, Jukka Suomela
ANNS
2007
13 years 8 months ago
Direct and indirect classification of high-frequency LNA performance using machine learning techniques
The task of determining low noise amplifier (LNA) high-frequency performance in functional testing is as challenging as designing the circuit itself due to the difficulties associa...
Peter C. Hung, Seán F. McLoone, Magdalena S...
ICTAI
2007
IEEE
14 years 1 months ago
ExOpaque: A Framework to Explain Opaque Machine Learning Models Using Inductive Logic Programming
In this paper we developed an Inductive Logic Programming (ILP) based framework ExOpaque that is able to extract a set of Horn clauses from an arbitrary opaque machine learning mo...
Yunsong Guo, Bart Selman
BMCBI
2010
176views more  BMCBI 2010»
13 years 7 months ago
TargetSpy: a supervised machine learning approach for microRNA target prediction
Background: Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recen...
Martin Sturm, Michael Hackenberg, David Langenberg...