Automatic Parameter Learning for Multiple Network Alignment

Jason Flannick1, Antal Novak1, Chuong B. Do1, Balaji S. Srinivasan2, and Serafim Batzoglou1
1Department of Computer Science, Stanford University, Stanford, CA 94305, USA
2Department of Statistics, Stanford University, Stanford, CA 94305, USA


We developed Græmlin 2.0, a new multiple network aligner with (1) a novel scoring func-
tion; (2) an algorithm that automatically learns the scoring function’s parameters; and (3) an
algorithm that uses the scoring function to globally align multiple networks. Existing alignment
tools use heuristic scoring functions, which must be hand-tuned to a given set of networks and
do not apply to multiple network alignment.
Our scoring function can use arbitrary features of a multiple network alignment, such as
protein deletions, protein duplications, protein mutations, and interaction losses. Our parameter
learning algorithm uses a training set of known network alignments to learn parameters for
our scoring function and thereby automatically adapts it to any set of networks. Our global
alignment algorithm finds approximate multiple network alignments in linear time.
We tested Græmlin 2.0’s accuracy on protein interaction networks from IntAct, DIP, and
the Stanford Network Database. We show that, on each of these datasets, Græmlin 2.0 has
higher sensitivity and specificity than existing network aligners. Græmlin 2.0 is available under
the GNU public license at


A Family of Algorithms for Computing Consensus about Node State from Network Data

Eleanor R. Brush, David C. Krakauer, Jessica C. Flack


Biological and social networks are composed of heterogeneous nodes that contribute differentially to network structure and function. A number of algorithms have been developed to measure this variation. These algorithms have proven useful for applications that require assigning scores to individual nodes–from ranking websites to determining critical species in ecosystems–yet the mechanistic basis for why they produce good rankings remains poorly understood. We show that a unifying property of these algorithms is that they quantify consensus in the network about a node’s state or capacity to perform a function. The algorithms capture consensus by either taking into account the number of a target node’s direct connections, and, when the edges are weighted, the uniformity of its weighted in-degree distribution (breadth), or by measuring net flow into a target node (depth). Using data from communication, social, and biological networks we find that that how an algorithm measures consensus–through breadth or depth– impacts its ability to correctly score nodes. We also observe variation in sensitivity to source biases in interaction/adjacency matrices: errors arising from systematic error at the node level or direct manipulation of network connectivity by nodes. Our results indicate that the breadth algorithms, which are derived from information theory, correctly score nodes (assessed using independent data) and are robust to errors. However, in cases where nodes “form opinions” about other nodes using indirect information, like reputation, depth algorithms, like Eigenvector Centrality, are required. One caveat is that Eigenvector Centrality is not robust to error unless the network is transitive or assortative. In these cases the network structure allows the depth algorithms to effectively capture breadth as well as depth. Finally, we discuss the algorithms’ cognitive and computational demands. This is an important consideration in systems in which individuals use the collective opinions of others to make decisions.


SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments

Oved Ourfali 1, Tomer Shlomi 1, Trey Ideker 3, Eytan Ruppin 1,2 and Roded Sharan 1

1 School of Computer Science, 2 School of Medicine, Tel-Aviv University, Tel-Aviv, Israel and 3 Department of Bioengineering, University of California, San Diego, CA 92093, USA

Motivation: The complex program of gene expression allows the cell to cope with changing genetic, developmental and environmental conditions. The accumulating large-scale measurements of gene knockout effects and molecular interactions allow us to begin to uncover regulatory and signaling pathways within the cell that connect causal to affected genes on a network of physical interactions.

Results: We present a novel framework, SPINE, for Signaling-regulatory Pathway INferencE. The framework aims at explaining gene expression experiments in which a gene is knocked out and as a result multiple genes change their expression levels. To this end, an integrated network of protein–protein and protein-DNA interactions is constructed, and signaling pathways connecting the causal gene to the affected genes are searched for in this network. The reconstruction problem is translated into that of assigning an activation/repression attribute with each protein so as to explain (in expectation) a maximum number of the knockout effects observed. We provide an integer programming formulation for the latter problem and solve it using a commercial solver.

We validate the method by applying it to a yeast subnetwork that is involved in mating. In cross-validation tests, SPINE obtains very high accuracy in predicting knockout effects (99%). Next, we apply SPINE to the entire yeast network to predict protein effects and reconstruct signaling and regulatory pathways. Overall, we are able to infer 861 paths with confidence and assign effects to 183 genes. The predicted effects are found to be in high agreement with current biological knowledge.

Availability: The algorithm and data are available at


Factor Graphs and the Sum-Product Algorithm

Frank R. Kschischang, Senior Member, IEEE, Brendan J. Frey, Member, IEEE, and
Hans-Andrea Loeliger, Member, IEEE

Algorithms that must deal with complicated global functions of many variables often exploit the manner in which the given functions factor as a product of “local” functions, each of which depends on a subset of the variables. Such a factorization can be visualized with a bipartite graph that we call a factor graph, In this tutorial paper, we present a generic message-passing algorithm, the sum-product algorithm, that operates in a factor graph. Following a single, simple computational rule, the sum-product algorithm computes-either exactly or approximately-various marginal functions derived from the global function. A wide variety of algorithms developed in artificial intelligence, signal processing, and digital communications can be derived as specific instances of the sum-product algorithm, including the forward/backward algorithm, the Viterbi algorithm, the iterative “turbo” decoding algorithm, Pearl’s (1988) belief propagation algorithm for Bayesian networks, the Kalman filter, and certain fast Fourier transform (FFT) algorithms.