Philip M Kim

Summary

Affiliation: Massachusetts Institute of Technology
Country: USA

Publications

  1. pmc Positive selection at the protein network periphery: evaluation in terms of structural constraints and cellular context
    Philip M Kim
    Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
    Proc Natl Acad Sci U S A 104:20274-9. 2007
  2. pmc MOTIPS: automated motif analysis for predicting targets of modular protein domains
    Hugo Y K Lam
    Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
    BMC Bioinformatics 11:243. 2010
  3. pmc Analysis of copy number variants and segmental duplications in the human genome: Evidence for a change in the process of formation in recent evolutionary history
    Philip M Kim
    Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520, USA
    Genome Res 18:1865-74. 2008
  4. pmc Identification of a major determinant for serine-threonine kinase phosphoacceptor specificity
    Catherine Chen
    Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
    Mol Cell 53:140-7. 2014
  5. pmc Identification of specificity determining residues in peptide recognition domains using an information theoretic approach applied to large-scale binding maps
    Kevin Y Yip
    Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
    BMC Biol 9:53. 2011
  6. ncbi request reprint An integrated system for studying residue coevolution in proteins
    Kevin Y Yip
    Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06511, USA
    Bioinformatics 24:290-2. 2008
  7. pmc Multi-level learning: improving the prediction of protein, domain and residue interactions by allowing information flow between levels
    Kevin Y Yip
    Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06511, USA
    BMC Bioinformatics 10:241. 2009
  8. pmc Nucleotide-resolution analysis of structural variants using BreakSeq and a breakpoint library
    Hugo Y K Lam
    Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
    Nat Biotechnol 28:47-55. 2010
  9. pmc The role of disorder in interaction networks: a structural analysis
    Philip M Kim
    Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
    Mol Syst Biol 4:179. 2008
  10. pmc Measuring the evolutionary rewiring of biological networks
    Chong Shou
    Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
    PLoS Comput Biol 7:e1001050. 2011

Collaborators

Detail Information

Publications18

  1. pmc Positive selection at the protein network periphery: evaluation in terms of structural constraints and cellular context
    Philip M Kim
    Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
    Proc Natl Acad Sci U S A 104:20274-9. 2007
    ..e., extracellular space or cell membrane). This suggests that the observed positive selection at the network periphery may be due to an increase of adaptive events on the cellular periphery responding to changing environments...
  2. pmc MOTIPS: automated motif analysis for predicting targets of modular protein domains
    Hugo Y K Lam
    Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
    BMC Bioinformatics 11:243. 2010
    ..However, predicting domain targets by motif sequence alone without considering other genomic and structural information has been shown to be lacking in accuracy...
  3. pmc Analysis of copy number variants and segmental duplications in the human genome: Evidence for a change in the process of formation in recent evolutionary history
    Philip M Kim
    Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520, USA
    Genome Res 18:1865-74. 2008
    ..In addition to a coarse-grained analysis, we performed targeted sequencing of 67 CNVs and then analyzed a combined set of 270 CNVs (540 breakpoints) to verify our conclusions...
  4. pmc Identification of a major determinant for serine-threonine kinase phosphoacceptor specificity
    Catherine Chen
    Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
    Mol Cell 53:140-7. 2014
    ..Understanding the rules governing kinase phosphoacceptor preference allows kinases to be classified as serine or threonine specific based on their sequence. ..
  5. pmc Identification of specificity determining residues in peptide recognition domains using an information theoretic approach applied to large-scale binding maps
    Kevin Y Yip
    Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
    BMC Biol 9:53. 2011
    ..They mediate protein-protein interactions by recognizing and binding short motifs in their ligands. Although a great deal is known about PRDs and their interactions, prediction of PRD specificities remains largely an unsolved problem...
  6. ncbi request reprint An integrated system for studying residue coevolution in proteins
    Kevin Y Yip
    Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06511, USA
    Bioinformatics 24:290-2. 2008
    ..The system also provides facilities for studying the relationship between coevolution scores and inter-residue distances from a crystal structure if provided, which may help in understanding protein structures...
  7. pmc Multi-level learning: improving the prediction of protein, domain and residue interactions by allowing information flow between levels
    Kevin Y Yip
    Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06511, USA
    BMC Bioinformatics 10:241. 2009
    ..The predictions at each level could benefit from using the features at all three levels. However, it is not trivial as the features are provided at different granularity...
  8. pmc Nucleotide-resolution analysis of structural variants using BreakSeq and a breakpoint library
    Hugo Y K Lam
    Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
    Nat Biotechnol 28:47-55. 2010
    ..As new data become available, we expect our BreakSeq approach will become more sensitive and facilitate rapid SV genotyping of personal genomes...
  9. pmc The role of disorder in interaction networks: a structural analysis
    Philip M Kim
    Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
    Mol Syst Biol 4:179. 2008
    ..A good illustration of this trend can be found in signaling pathways and, more specifically, in kinase cascades. Finally, our findings have implications for the current controversy related to party and date-hubs...
  10. pmc Measuring the evolutionary rewiring of biological networks
    Chong Shou
    Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
    PLoS Comput Biol 7:e1001050. 2011
    ....
  11. pmc The current excitement about copy-number variation: how it relates to gene duplications and protein families
    Jan O Korbel
    Molecular Biophysics and Biochemistry Department, Yale University, New Haven, CT 06520, USA
    Curr Opin Struct Biol 18:366-74. 2008
    ..These trends are likely reflective of CNV formation biases and natural selection, both of which differentially influence distinct protein families...
  12. pmc Paired-end mapping reveals extensive structural variation in the human genome
    Jan O Korbel
    Molecular Biophysics and Biochemistry Department, Yale University, New Haven, CT 06520, USA
    Science 318:420-6. 2007
    ..The breakpoint junction sequences of more than 200 SVs were determined with a novel pooling strategy and computational analysis. Our analysis provided insights into the mechanisms of SV formation in humans...
  13. doi request reprint Rewiring of transcriptional regulatory networks: hierarchy, rather than connectivity, better reflects the importance of regulators
    Nitin Bhardwaj
    Program in Computational Biology and Bioinformatics, Yale University, Bass 426, 266 Whitney Avenue, New Haven, CT 06520, USA
    Sci Signal 3:ra79. 2010
    ..Overall, our analysis shows that broadly constructed hierarchies may better reflect the importance of regulators for cell growth than classifications based on the number of connections (hubbiness)...
  14. pmc Deciphering protein kinase specificity through large-scale analysis of yeast phosphorylation site motifs
    Janine Mok
    Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520, USA
    Sci Signal 3:ra12. 2010
    ..Together, these results elucidate how kinase catalytic domains recognize their phosphorylation targets and suggest general avenues for the identification of previously unknown kinase substrates across eukaryotes...
  15. ncbi request reprint Relating three-dimensional structures to protein networks provides evolutionary insights
    Philip M Kim
    Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
    Science 314:1938-41. 2006
    ....
  16. ncbi request reprint Comparing classical pathways and modern networks: towards the development of an edge ontology
    Long J Lu
    Department of Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Avenue, New Haven, CT 06520, USA
    Trends Biochem Sci 32:320-31. 2007
    ..Therefore, we suggest that a standardized and well-defined edge ontology is necessary and propose a prototype as a starting point for reaching this goal...
  17. pmc The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics
    Haiyuan Yu
    Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
    PLoS Comput Biol 3:e59. 2007
    ....
  18. ncbi request reprint Understanding modularity in molecular networks requires dynamics
    Roger P Alexander
    Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
    Sci Signal 2:pe44. 2009
    ....