H. Geschwind

Molecular Biology · 2016

A researcher in the Science corpus: 1 paper; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom H. Geschwind trusts

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

  1. Steve Horvath (in Molecular Biology)

Papers from H. Geschwind's perspective

Ordered by how strongly the researchers H. Geschwind trusts endorse each paper. Same corpus, a different order for every researcher.

  1. A General Framework for Weighted Gene Co-Expression Network Analysis (Statistical Applications in Genetics and Molecular Biology)
  2. Fast R Functions for Robust Correlations and Hierarchical Clustering (Journal of Statistical Software)
  3. Eigengene networks for studying the relationships between co-expression modules (BMC Systems Biology)
  4. HTSeq—a Python framework to work with high-throughput sequencing data (bioRxiv)
  5. Derivative Estimation and Testing in Generalized Additive Models
  6. Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks (BMC Genomics)
  7. A prediction-based resampling method for estimating the number of clusters in a dataset (Genome Biology)
  8. The Elements of Statistical Learning
  9. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  10. Cytoscape: a software environment for integrated models of biomolecular interaction networks.

Their papers