David F Gleich
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Explore the profile of David F Gleich including associated specialties, affiliations and a list of published articles.
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11
Citations
183
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Recent Articles
1.
Ibrahim R, Gleich D
PLoS One
. 2020 Dec;
15(12):e0243485.
PMID: 33362247
Local graph clustering is an important machine learning task that aims to find a well-connected cluster near a set of seed nodes. Recent results have revealed that incorporating higher order...
2.
Lin C, Konecki D, Liu M, Wilson S, Nassar H, Wilkins A, et al.
Bioinformatics
. 2018 Oct;
35(9):1536-1543.
PMID: 30304494
Motivation: Precision medicine is an emerging field with hopes to improve patient treatment and reduce morbidity and mortality. To these ends, computational approaches have predicted associations among genes, chemicals and...
3.
Sinha A, Gleich D, Ramani K
Sci Rep
. 2018 Aug;
8(1):11909.
PMID: 30093660
The study of network topology provides insight into the function and behavior of physical, social, and biological systems. A natural step towards discovering the organizing principles of these complex topologies...
4.
Whang J, Hou Y, Gleich D, Dhillon I
IEEE Trans Pattern Anal Mach Intell
. 2018 Aug;
41(11):2644-2659.
PMID: 30080141
Traditional clustering algorithms, such as K-Means, output a clustering that is disjoint and exhaustive, i.e., every single data point is assigned to exactly one cluster. However, in many real-world datasets,...
5.
Yin H, Benson A, Leskovec J, Gleich D
KDD
. 2018 May;
2017:555-564.
PMID: 29770258
Local graph clustering methods aim to find a cluster of nodes by exploring a small region of the graph. These methods are attractive because they enable targeted clustering around a...
6.
Mohammadi S, Ravindra V, Gleich D, Grama A
Nat Commun
. 2018 Apr;
9(1):1516.
PMID: 29666373
Single-cell transcriptomic data has the potential to radically redefine our view of cell-type identity. Cells that were previously believed to be homogeneous are now clearly distinguishable in terms of their...
7.
Jiang B, Kloster K, Gleich D, Gribskov M
Bioinformatics
. 2017 Feb;
33(12):1829-1836.
PMID: 28200073
Motivation: Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that...
8.
Mohammadi S, Gleich D, Kolda T, Grama A
IEEE/ACM Trans Comput Biol Bioinform
. 2016 Aug;
14(6):1446-1458.
PMID: 27483461
Network alignment has extensive applications in comparative interactomics. Traditional approaches aim to simultaneously maximize the number of conserved edges and the underlying similarity of aligned entities. We propose a novel...
9.
Benson A, Gleich D, Leskovec J
Science
. 2016 Jul;
353(6295):163-6.
PMID: 27387949
Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that...
10.
Benson A, Gleich D, Leskovec J
Proc SIAM Int Conf Data Min
. 2015 Jan;
2015:118-126.
PMID: 27812399
Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk...