Sara Ballouz
Sara Ballouz
Home
Posts
Projects
Publications
Contact
Gene Regulatory Networks
Predictability of human differential gene expression
Differential expression (DE) is commonly used to explore molecular mechanisms of biological conditions. While many studies report …
Megan Crow
,
Nathaniel Lim
,
Sara Ballouz
,
Paul Pavlidis
,
Jesse Gillis
Cite
DOI
EGAD: ultra-fast functional analysis of gene networks
Summary: Evaluating gene networks with respect to known biology is a common task but often a computationally costly one. Many …
Sara Ballouz
,
Melanie Weber
,
Paul Pavlidis
,
Jesse Gillis
Cite
DOI
Exploiting single-cell expression to characterize co-expression replicability
BACKGROUND: Co-expression networks have been a useful tool for functional genomics, providing important clues about the cellular and …
Megan Crow
,
Anirban Paul
,
Sara Ballouz
,
Z. Josh Huang
,
Jesse Gillis
Cite
DOI
Ligand Similarity Complements Sequence, Physical Interaction, and Co-Expression for Gene Function Prediction
The expansion of protein-ligand annotation databases has enabled large-scale networking of proteins by ligand similarity. These …
Matthew J. O'Meara
,
Sara Ballouz
,
Brian K. Shoichet
,
Jesse Gillis
Cite
DOI
Positive and negative forms of replicability in gene network analysis
MOTIVATION: Gene networks have become a central tool in the analysis of genomic data but are widely regarded as hard to interpret. This …
Wim Verleyen
,
Sara Ballouz
,
Jesse Gillis
Cite
DOI
Guidance for RNA-seq co-expression network construction and analysis: safety in numbers
MOTIVATION: RNA-seq co-expression analysis is in its infancy and reasonable practices remain poorly defined. We assessed a variety of …
Sara Ballouz
,
Wim Verleyen
,
Jesse Gillis
Cite
DOI
Measuring the wisdom of the crowds in network-based gene function inference
MOTIVATION: Network-based gene function inference methods have proliferated in recent years, but measurable progress remains elusive. …
Wim Verleyen
,
Sara Ballouz
,
Jesse Gillis
Cite
DOI
Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies
BACKGROUND: Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the …
Erdahl T. Teber
,
Jason Y. Liu
,
Sara Ballouz
,
Diane Fatkin
,
Merridee A. Wouters
Cite
DOI
Cite
×