[SocBiN] Deadline Extended /Call for Submissions: Annotation and Curation of Uncharacterized Proteins

[Prash]

What: Annotation and Curation of Uncharacterized Proteins: Systems Biology
Approaches

Deadline for abstract submission: 12 Mar 2014

Deadline for full article submission: 20 Jul 2014

Extended deadline for full article submission:


More here:
http://www.frontiersin.org/Bioinformatics_and_Computational_Biology/researchtopics/Annotation_and_Curation_of_Unc/2557
<http://www.frontiersin.org/Bioinformatics_and_Computational_Biology/researchtopics/Annotation_and_Curation_of_Unc/2557>


 A hypothetical protein (HPs) is a protein whose existence is predicted but
whether or not it is expressed remains uncertain. However, many HPs in the
recent past have been known to be expressed in vivo. With various methods
known to identify components in cell membrane, the functional significance
of thousands of proteins, especially those that that have no functional
annotations, not expressed, unique or common among genomes, is least
understood. Apart from this, many HPs might turn out to be pseudogenes at a
later point of time and the use of these proteins remains mis-conducive. To
better understand the problem many different areas have been investigated,
from methods to screen functional candidates among many hypothetical
proteins using feature selection algorithms, to the application of
statistical methods to test the efficacy of these proteins annotations in
terms of precision and accuracy. With experimental procedures for HP
function prediction being low throughput by nature, there is a strong need
to precisely know their functions for a better understanding of the
underlying biological mechanisms associated with them. We encourage authors
interested in protein annotation research to contribute in any of the
following areas:

1. Computational flow for the functional annotation of uncharacterized
proteins using motif discovery, sequence and structural similarity,
structural homology, hypothome (interactome of hypothetical proteins) or
combination of data from highly similar non-interacting proteins
(Similactors).
2. Predictive approaches based on Gene Ontology (GO).
3. Feature selection and machine learning methods.
4. Diseasome approaches.




*Prashanth Suravajhala, PhD.*
Wiki: http://www.bioinformatics.org/wiki/Prash

 Linkedin: http://dk.linkedin.com/in/prashbio
<http://dk.linkedin.com/in/prashbio>

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