Profile-based string kernels for remote homology detection and motif extraction.

By Rui Kuang, Eugene Ie, Ke Wang, Kai Wang, Mahira Siddiqi, Yoav Freund, Christina S. Leslie. 2005

60 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Michael Heinzinger
2. Christian Dallago
3. Ahmed Elnaggar
4. Yu Wang
5. Florian Matthes
6. Dmitrii Nachaev
7. Yanjun Qi
8. Burkhard Rost

In Spectroscopy

1. Ahmed Elnaggar
2. Yu Wang
3. Florian Matthes
4. Christian Dallago
5. Burkhard Rost
6. Michael Heinzinger

Citations in the corpus

• Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “This concept resembles that of using k-mers for database searches [29], clustering [52], motifs [53, 54], and prediction methods [42, 47, 55-59].”
• Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction (2016, background): “(3) The profile kernel [17] uses the notion of similarity based on a probabilistic model (e.”