J
Jian Qiu
Researcher at University of Washington
Publications - 5
Citations - 359
Jian Qiu is an academic researcher from University of Washington. The author has contributed to research in topics: Support vector machine & Kernel (statistics). The author has an hindex of 4, co-authored 5 publications receiving 345 citations.
Papers
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Journal ArticleDOI
A new pairwise kernel for biological network inference with support vector machines
TL;DR: The metric learning pairwise kernel is a new formulation to infer pairwise relationships with SVM, which provides state-of-the-art results for the inference of several biological networks from heterogeneous genomic data.
Journal ArticleDOI
Ranking predicted protein structures with support vector regression
TL;DR: This work develops a scoring function using support vector regression (SVR) that is able to select significantly better models and yield significantly better Pearson correlation coefficients than the two best Quality Assessment groups in CASP7, QA556 (LEE), and QA634 (Pcons).
Journal ArticleDOI
Predicting Co-Complexed Protein Pairs from Heterogeneous Data
Jian Qiu,William Stafford Noble +1 more
TL;DR: A computational method is presented that predicts co-complexed protein pair (CCPP) relationships using kernel methods from heterogeneous data sources and finds that predicted positives are highly enriched with CCPPs that are identified by both datasets, suggesting that the method successfully identifies true CCPPs.
Journal ArticleDOI
A structural alignment kernel for protein structures
TL;DR: This work describes a kernel that is derived in a straightforward fashion from an existing structural alignment program, MAMMOTH, and finds that this kernel significantly out-performs a variety of other kernels, including several previously described kernels.
Posted Content
Metric learning pairwise kernel for graph inference
TL;DR: This work proposes a supervised approach for the direct case by translating it into a distance metric learning problem, and demonstrates, using several real biological networks, that this direct approach often improves upon the state-of-the-art SVM for indirect inference with the tensor product pairwise kernel.