In recent years the development of computational techniques that build models to correctly assign chemical compounds to various classes or to retrieve potential drug-like compounds has been an active area of research. Many of the best-performing techniques for these tasks utilize a descriptor-based representation of the compound that captures various aspects of the underlying molecular graph?s topology. In this paper we compare different set of descriptors that are currently used for chemical compound classification. In this process, we also introduce four different descriptors derived from all connected fragments present in the molecular graphs. In addition, we introduce an extension to existing vector-based kernel functions to take into account the length of the fragments present in the descriptors. We experimentally evaluate the performance of the previously introduced and the new descriptors in the context of SVM-based classification and ranked-retrieval on 28 classification and retrieval problems derived from 18 datasets. Our experiments show that for both these tasks, the new descriptors consistently and statistically outperform previously developed schemes based on the widely used fingerprint- and Maccs keys-based descriptors, as well as recently introduced descriptors obtained by mining and analyzing the structure of the molecular graphs.

/pdf/comparison-of-descriptor-spaces-for-chemical-compound-4ec6sgpkpf.pdf

Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification

On-line construction of suffix trees

Any paper proposing a new algorithm should come with an evaluation of efficiency and scalability (particularly when we are designing methods for "big data"). However, there are several (more or less serious) pitfalls in such evaluations. We would like to point the attention of the community to these pitfalls. We substantiate our points with extensive experiments, using clustering and outlier detection methods with and without index acceleration. We discuss what we can learn from evaluations, whether experiments are properly designed, and what kind of conclusions we should avoid. We close with some general recommendations but maintain that the design of fair and conclusive experiments will always remain a challenge for researchers and an integral part of the scientific endeavor.

The (black) art of runtime evaluation: Are we comparing algorithms or implementations?

The task of matching patterns in graph-structured data has applications in such diverse areas as computer vision, biology, electronics, computer aided design, social networks, and intelligence analysis. Consequently, work on graph-based pattern matching spans a wide range of research communities. Due to variations in graph characteristics and application requirements, graph matching is not a single problem, but a set of related problems. This paper presents a survey of existing work on graph matching, describing variations among problems, general and specific solution approaches, evaluation techniques, and directions for further research. An emphasis is given to techniques that apply to general graphs with semantic characteristics.

Matching Structure and Semantics: A Survey on Graph-Based Pattern Matching.

Mining for frequent subgraphs in a graph database has become a popular topic in the last years. Algorithms to solve this problem are used in chemoinformatics to find common molecular fragments in a database of molecules represented as two-dimensional graphs. However, the search process in arbitrary graph structures includes costly graph and subgraph isomorphism tests. In our ParMol package we have implemented four of the most popular frequent subgraph miners using a common infrastructure: MoFa, gSpan, FFSM, and Gaston. Besides the pure re-implementation, we have added additional functionality to some of the algorithms like parallel search, mining directed graphs, and mining in one big graph instead of a graph database. Also a 2D-visualizer for molecules has been integrated.

/pdf/the-parmol-package-for-frequent-subgraph-mining-2y92cadq95.pdf

The ParMol Package for Frequent Subgraph Mining

Procedural abstraction (PA) extracts duplicate code segments into a newly created method and hence reduces code size. For embedded micro computers the amount of memory is still limited so code reduction is an important issue. This paper presents a novel approach to PA, that is especially targeted towards embedded systems. Earlier approaches of PA are blind with respect to code reordering, i.e., two code segments with the same semantic effect but with different instruction orders were not detected as candidates for PA. Instead of instruction sequences, in our approach the data flow graphs of basic blocks are considered. Compared to known PA techniques more than twice the number of instructions can be saved on a set of binaries, by detecting frequently appearing graph fragments with a graph mining tool based on the well known gSpan algorithm. The detection and extraction of graph fragments is not as straight forward as extracting sequential code fragments. NP-complete graph operations and special rules to decide which parts can be abstracted are needed. However, this effort pays off as smaller sizes significantly reduce costs on mass-produced embedded systems.

/pdf/graph-based-procedural-abstraction-53g01vi4wh.pdf

Graph-Based Procedural Abstraction

Although in the last few years about a dozen sophisticated algorithms for mining frequent fragments in molecular databases have been proposed, searching big databases with 100,000 compounds and more is still a time-consuming process. Even the currently fastest algorithms like gSpan, FFSM, Gaston, or MoFa require hours to complete their tasks. This paper presents thread-based parallel versions of MoFa [5] and gSpan [26] that achieve speedups up to 11 on a shared-memory SMP system using 12 processors. We discuss the design space of the parallelization, the results, and the obstacles that are caused by the irregular search space and by the current state of Java technology.

/pdf/mining-molecular-datasets-on-symmetric-multiprocessor-4h8lza9fdc.pdf

Mining Molecular Datasets on Symmetric Multiprocessor Systems

The research group ParMol (parallel molecular mining) has the task to parallelize the complex search for interesting substructures in huge graph databases. The main focus is the search for frequent fragments in a molecular database. In [38] some sequencial algorithms for that task (MoFa [5], gSpan [39], FFSM [14], and Gaston [26]) were compared. gSpan is the most flexible but also fast algorithm, so this work deals with this algorithm in detail. In this thesis gSpan is extended in two different directions. On the one hand, it gets the possiblity to search in and for directed graphs instead just mining undirected ones, so that gSpan can be used in more application areas like weblog-mining [8] or procedural abstraction [9]. On the other hand the speedup ideas for closed graph mining of the CloseGraph [40] extension are tested. In the context of procedural abstraction (or graph shrinking in general) another kind of determining the frequency of a fragment is necessary. The number of (independent) embeddings is there more significant than the number of database graphs a fragment occurs in. The last part is the parallelization of the depth first search (DFS), that gSpan uses to find all frequent subgraphs. Two different parallel systems, a shared memory symmetric multiprocessing (SMP) system and a cluster of independent computers, are the basis for two parallel Java / JavaParty [28] implementations. The effects of the completely different memory structures and communication overhead are looked at and solutions to elliminate or minimise resulting weak points are worked out.

Extension and parallelization of a graph-mining-algorithm

In this paper we present the novel graph mining algorithm Edgar which is based on the well-known gSpan algorithm. The need for another subgraph miner results from procedural abstraction (an im- portant technique to reduce code size in embedded systems). Assembler code is represented as a data flow graph and subgraph mining on this graph returns frequent code fragments that can be extracted into proce- dures. When mining for procedural abstraction, not the number of data flow graphs a fragment occurs in is important but the number of all non-overlapping occurrences in all graphs. Several changes in the min- ing process are therefore necessary. As traditional pruning strategies are inappropriate, Edgar uses a new embedding-based frequency and on av- erage saves 160% more instructions compared to classical approaches.

/pdf/edgar-the-embedding-based-graph-miner-3ukmmqvme8.pdf

Edgar: the Embedding-baseD GrAph MineR

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