Using Bayesian networks to analyze expression data
- N. Friedman, M. Linial, I. Nachman, D. Pe’er
- Computer ScienceAnnual International Conference on Research in…
- 8 April 2000
This paper proposes a new framework for discovering interactions between genes based on multiple expression measurements, and presents an efficient algorithm capable of learning such networks and statistical method to assess confidence in their features.
A large-scale evaluation of computational protein function prediction
- P. Radivojac, W. Clark, I. Friedberg
- BiologyNature Methods
- 27 January 2013
Today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets, and there is considerable need for improvement of currently available tools.
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
- Yuxiang Jiang, T. Oron, P. Radivojac
- BiologyGenome Biology
- 3 January 2016
The second critical assessment of functional annotation (CAFA) conducted, a timed challenge to assess computational methods that automatically assign protein function, revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies.
Vesicular neurotransmitter transporters: from bacteria to humans.
- S. Schuldiner, A. Shirvan, M. Linial
- BiologyPhysiological Reviews
- 1 April 1995
Insights into social insects from the genome of the honeybee Apis mellifera
- G. Weinstock, G. Robinson, Rita A. Wright
- BiologyNature
- 26 October 2006
The genome sequence of the honeybee Apis mellifera is reported, suggesting a novel African origin for the species A. melliferA and insights into whether Africanized bees spread throughout the New World via hybridization or displacement.
ProtoMap: automatic classification of protein sequences and hierarchy of protein families
This analysis splits the protein space into well-defined groups of proteins, which are closely correlated with natural biological families and superfamilies, and suggests a hierarchical organization of all proteins.
Novel Unsupervised Feature Filtering of Biological Data
- Roy Varshavsky, A. Gottlieb, M. Linial, D. Horn
- Computer ScienceIntelligent Systems in Molecular Biology
- 10 July 2006
A novel unsupervised criterion, based on SVD-entropy, selecting a feature according to its contribution to the entropy calculated on a leave-one-out basis is proposed, demonstrating that feature filtering according to CE outperforms the variance method and gene-shaving.
The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
- Naihui Zhou, Yuxiang Jiang, I. Friedberg
- BiologybioRxiv
- 29 May 2019
The third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed, concluded that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not.
Viral adaptation to host: a proteome-based analysis of codon usage and amino acid preferences
- Iris Bahir, M. Fromer, Yosef Prat, M. Linial
- BiologyMolecular Systems Biology
- 13 October 2009
It is shown that bacteria‐infecting viruses are strongly adapted to their specific hosts, but that they differ from other unrelated bacterial hosts.
Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space
- Yaniv Loewenstein, E. Portugaly, M. Fromer, M. Linial
- Computer ScienceIntelligent Systems in Molecular Biology
- 1 July 2008
A novel class of memory-constrained UPGMA (MC-UPGMA) algorithms are presented, which demonstrate that leveraging the entire mass embodied in all sequence similarities allows to significantly improve on current protein family clusterings which are unable to directly tackle the sheer mass of this data.
...
...