Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

https://snap.stanford.edu/class/cs224w-readings/tomita06cliques.pdf

The worst-case time complexity for generating all maximal cliques and computational experiments

Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics. Metaheuristics include but are not limited to constraint logic programming; greedy random adaptive search procedures; natural evolutionary computation; neural networks; non-monotonic search strategies; space-search methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings.

Metaheuristics: A bibliography

Motivation: Protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict protein complexes from protein–protein interaction (PPI) networks. However, protein interaction data produced by high-throughput experiments are often associated with high false positive and false negative rates, which makes it difficult to predict complexes accurately.

Results: We use an iterative scoring method to assign weight to protein pairs, and the weight of a protein pair indicates the reliability of the interaction between the two proteins. We develop an algorithm called CMC (clustering-based on maximal cliques) to discover complexes from the weighted PPI network. CMC first generates all the maximal cliques from the PPI networks, and then removes or merges highly overlapped clusters based on their interconnectivity. We studied the performance of CMC and the impact of our iterative scoring method on CMC. Our results show that: (i) the iterative scoring method can improve the performance of CMC considerably; (ii) the iterative scoring method can effectively reduce the impact of random noise on the performance of CMC; (iii) the iterative scoring method can also improve the performance of other protein complex prediction methods and reduce the impact of random noise on their performance; and (iv) CMC is an effective approach to protein complex prediction from protein interaction network.

Contact: liugm@comp.nus.edu.sg

Supplementary information: Supplementary data are available at Bioinformatics online.

/pdf/complex-discovery-from-weighted-ppi-networks-2gnceu922g.pdf

Complex discovery from weighted PPI networks

Most proteins form macromolecular complexes to perform their biological functions. However, experimentally determined protein complex data, especially of those involving more than two protein partners, are relatively limited in the current state-of-the-art high-throughput experimental techniques. Nevertheless, many techniques (such as yeast-two-hybrid) have enabled systematic screening of pairwise protein-protein interactions en masse. Thus computational approaches for detecting protein complexes from protein interaction data are useful complements to the limited experimental methods. They can be used together with the experimental methods for mapping the interactions of proteins to understand how different proteins are organized into higher-level substructures to perform various cellular functions. Given the abundance of pairwise protein interaction data from high-throughput genome-wide experimental screenings, a protein interaction network can be constructed from protein interaction data by considering individual proteins as the nodes, and the existence of a physical interaction between a pair of proteins as a link. This binary protein interaction graph can then be used for detecting protein complexes using graph clustering techniques. In this paper, we review and evaluate the state-of-the-art techniques for computational detection of protein complexes, and discuss some promising research directions in this field. Experimental results with yeast protein interaction data show that the interaction subgraphs discovered by various computational methods matched well with actual protein complexes. In addition, the computational approaches have also improved in performance over the years. Further improvements could be achieved if the quality of the underlying protein interaction data can be considered adequately to minimize the undesirable effects from the irrelevant and noisy sources, and the various biological evidences can be better incorporated into the detection process to maximize the exploitation of the increasing wealth of biological knowledge available.

/pdf/computational-approaches-for-detecting-protein-complexes-1o11azp3os.pdf

Computational approaches for detecting protein complexes from protein interaction networks: a survey

We present a depth-first search algorithm for generating all maximal cliques of an undirected graph, in which pruning methods are employed as in Bron and Kerbosch's algorithm. All maximal cliques generated are output in a tree-like form. Then we prove that its worst-case time complexity is O(3 n/3 ) for an n-vertex graph. This is optimal as a function of n, since there exist up to 3 n/3 cliques in an n-vertex graph.

The Worst-Case Time Complexity for Generating All Maximal Cliques (Extended Abstract)

We present a depth-first search algorithm for generating all maximal cliques of an undirected graph, in which pruning methods are employed as in Bron and Kerbosch’s algorithm. All maximal cliques generated are output in a tree-like form. Then we prove that its worst-case time complexity is O(3 n/3) for an n-vertex graph. This is optimal as a function of n, since there exist up to 3 n/3 cliques in an n-vertex graph.

The Worst-Case Time Complexity for Generating All Maximal Cliques

Kernel Selection for the Support Vector Machine

In this paper we propose a new fast learning algorithm for the support vector machine (SVM). The proposed method is based on the technique of second-order cone programming. We reformulate the SVM's quadratic programming problem into the second-order cone programming problem. The proposed method needs to decompose the kernel matrix of SVM's optimization problem, and the decomposed matrix is used in the new optimization problem. Since the kernel matrix is positive semidefinite, the dimension of the decomposed matrix can be reduced by decomposition (factorization) methods. The performance of the proposed method depends on the dimension of the decomposed matrix. Experimental results show that the proposed method is much faster than the quadratic programming solver LOQO if the dimension of the decomposed matrix is small enough compared to that of the kernel matrix. The proposed method is also faster than the method proposed in (S. Fine and K. Scheinberg, 2001) for both low-rank and full-rank kernel matrices. The working set selection is an important issue in the SVM decomposition (chunking) method. We also modify Hsu and Lin's working set selection approach to deal with large working set. The proposed approach leads to faster convergence.

Haruhisa Takahashi

Papers

The worst-case time complexity for generating all maximal cliques and computational experiments

The Worst-Case Time Complexity for Generating All Maximal Cliques (Extended Abstract)

The Worst-Case Time Complexity for Generating All Maximal Cliques

Kernel Selection for the Support Vector Machine

An Efficient Support Vector Machine Learning Method with Second-Order Cone Programming for Large-Scale Problems