Keun-Joon Park

Molecular Biology · 2003–2007

A researcher in the Science corpus: 2 papers; trusts 10 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 9 citations into it; #4,314 of 17,773 by trust here, #9,030 by citations

Whom Keun-Joon Park trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Amos Bairoch
2. Sujun Hua
3. Rolf Apweiler
4. Olof Emanuelsson
5. Michael S. Brown
6. Zhirong Sun
7. Zhirong Sun
8. Kuo-Chen Chou
9. Henrik Nielsen
10. Chris H. Q. Ding

Who trusts Keun-Joon Park overall

1. Kuo-Chen Chou
2. Chin-Sheng Yu
3. Sebastian Briesemeister
4. Zhi-cheng Wu
5. Hongbin Shen
6. Torsten Blum
7. Michael Heinzinger
8. Yu‐Ching Chen
9. Xuan Xiao
10. Abhishek Niroula

Papers from Keun-Joon Park's perspective

Ordered by how strongly the researchers Keun-Joon Park trusts endorse each paper. Same corpus, a different order for every researcher.

1. The SWISS-PROT protein sequence data bank and its supplement TrEMBL
2. Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. (Journal of Molecular Biology)
3. Support vector machine approach for protein subcellular localization prediction (Bioinform.)
4. Knowledge-based analysis of microarray gene expression data by using support vector machines. (Proceedings of the National Academy of Sciences of the United States of America)
5. A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach.
6. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000
7. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods
8. Multi-class protein fold recognition using support vector machines and neural networks (Bioinform.)
9. Prediction of protein subcellular locations by incorporating quasi-sequence-order effect. (Biochemical and Biophysical Research Communications - BBRC)
10. Prediction of protein cellular attributes using pseudo‐amino acid composition (Proteins: Structure, Function, and Bioinformatics)

Their papers

• WoLF PSORT: protein localization predictor (2007) — 3,509 citations
• Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs (2003) — 385 citations