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» Predicting Time Series with Support Vector Machines
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121
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BMCBI
2007
121views more  BMCBI 2007»
15 years 3 months ago
Predicting zinc binding at the proteome level
Background: Metalloproteins are proteins capable of binding one or more metal ions, which may be required for their biological function, for regulation of their activities or for ...
Andrea Passerini, Claudia Andreini, Sauro Menchett...
136
Voted
KDD
2004
ACM
166views Data Mining» more  KDD 2004»
16 years 4 months ago
Predicting prostate cancer recurrence via maximizing the concordance index
In order to effectively use machine learning algorithms, e.g., neural networks, for the analysis of survival data, the correct treatment of censored data is crucial. The concordan...
Lian Yan, David Verbel, Olivier Saidi
170
Voted
BMCBI
2007
207views more  BMCBI 2007»
15 years 3 months ago
Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clus
Background: The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell...
Nikhil R. Pal, Kripamoy Aguan, Animesh Sharma, Shu...
170
Voted
TCBB
2008
138views more  TCBB 2008»
15 years 3 months ago
PairProSVM: Protein Subcellular Localization Based on Local Pairwise Profile Alignment and SVM
The subcellular locations of proteins are important functional annotations. An effective and reliable subcellular localization method is necessary for proteomics research. This pap...
Man-Wai Mak, Jian Guo, Sun-Yuan Kung
135
Voted
ICRA
2002
IEEE
150views Robotics» more  ICRA 2002»
15 years 8 months ago
Detecting Surface Features During Locomotion using Optic Flow
We test the hypothesis that: (1) Optic flow can be used to detect significant environmental features during locomotion in a biped, even given significant up and down movement and j...
M. Anthony Lewis