Application of kernel functions for accurate similarity search in large chemical databases

Background Similaritysearch in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models, graph kernel functions can not be applied to large chemical compound database due to the high computational complexity and the difficulties in indexing similarity search for large databases. Results To bridge graph kernel function and similarity search in chemical databases, we applied a novel kernel-based similarity measurement, developed in our team, to measure similarity of graph represented chemicals. In our method, we utilize a hash table to support new graph kernel function definition, efficient storage and fast search. We have applied our method, named G-hash, to large chemical databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Moreover, the similarity measurement and the index structure is scalable to large chemical databases with smaller indexing size, and faster query processing time as compared to state-of-the-art indexing methods such as Daylight fingerprints, C-tree and GraphGrep. Conclusions Efficient similarity query processing method for large chemical databases is challenging since we need to balance running time efficiency and similarity search accuracy. Our previous similarity search method, G-hash, provides a new way to perform similarity search in chemical databases. Experimental study validates the utility of G-hash in chemical databases

Identification of functionally related enzymes by learning-to-rank methods

Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored. In this work we show that rankings of that kind can be substantially improved by applying kernel-based learning algorithms. This approach enables the detection of statistical dependencies between similarities of the active cleft and the biological function of annotated enzymes. This is in contrast to search-based approaches, which do not take annotated training data into account. Similarity measures based on the active cleft are known to outperform sequence-based or structure-based measures under certain conditions. We consider the Enzyme Commission (EC) classification hierarchy for obtaining annotated enzymes during the training phase. The results of a set of sizeable experiments indicate a consistent and significant improvement for a set of similarity measures that exploit information about small cavities in the surface of enzymes

SQL query log analysis for identifying user interests and query recommendations

In the sciences and elsewhere, the use of relational databases has become ubiquitous. To get maximum profit from a database, one should have in-depth knowledge in both SQL and a domain (data structure and meaning that a database contains). To assist inexperienced users in formulating their needs, SQL query recommendation system (SQL QRS) has been proposed. It utilizes the experience of previous users captured by SQL query log as well as the user query history to suggest. When constructing such a system, one should solve related problems: (1) clean the query log and (2) define appropriate query similarity functions. These two tasks are not only necessary for building SQL QRS, but they apply to other problems. In what follows, we describe three scenarios of SQL query log analysis: (1) cleaning an SQL query log, (2) SQL query log clustering when testing SQL query similarity functions and (3) recommending SQL queries. We also explain how these three branches are related to each other. Scenario 1. Cleaning SQL query log as a general pre-processing step The raw query log is often not suitable for query log analysis tasks such as clustering, giving recommendations. That is because it contains antipatterns and robotic data downloads, also known as Sliding Window Search (SWS). An antipattern in software engineering is a special case of a pattern. While a pattern is a standard solution, an antipattern is a pattern with a negative effect. When it comes to SQL query recommendation, leaving such artifacts in the log during analysis results in a wrong suggestion. Firstly, the behaviour of "mortal" users who need a recommendation is different from robots, which perform SWS. Secondly, one does not want to recommend antipatterns, so they need to be excluded from the query pool. Thirdly, the bigger a log is, the slower a recommendation engine operates. Thus, excluding SWS and antipatterns from the input data makes the recommendation better and faster. The effect of SWS and antipatterns on query log clustering depends on the chosen similarity function. The result can either (1) do not change or (2) add clusters which cover a big part of data. In any case, having antipatterns and SWS in an input log increases only the time one need to cluster and do not increase the quality of results. Scenario 2. Identifying User Interests via Clustering To identify the hot spots of user interests, one clusters SQL queries. In a scientific domain, it exposes research trends. In business, it points to popular data slices which one might want to refactor for better accessibility. A good clustering result must be precise (match ground truth) and interpretable. Query similarity relies on SQL query representation. There are three strategies to represent an SQL query. FB (feature-based) query representation sees a query as structure, not considering the data, a query accesses. WB (witness-based) approach treat a query as a set of tuples in the result set. AAB (access area-based) representation considers a query as an expression in relational algebra. While WB and FB query similarity functions are straightforward (Jaccard or cosine similarities), AAB query similarity requires additional definition. We proposed two variants of AAB similarity measure – overlap (AABovl) and closeness (AABcl). In AABovl, the similarity of two queries is the overlap of their access areas. AABcl relies on the distance between two access areas in the data space – two queries may be similar even if their access areas do not overlap. The extensive experiments consist of two parts. The first one is clustering a rather small dataset with ground truth. This experiment serves to study the precision of various similarity functions by comparing clustering results to supervised insights. The second experiment aims to investigate on the interpretability of clustering results with different similarity functions. It clusters a big real-world query log. The domain expert then evaluates the results. Both experiments show that AAB similarity functions produce better results in both precision and interpretability. Scenario 3. SQL Query Recommendation A sound SQL query recommendation system (1) provides a query which can be run directly, (2) supports comparison operators and various logical operators, (3) is scalable and has low response times, (4) provides recommendations of high quality. The existing approaches fail to fulfill all the requirements. We proposed DASQR, scalable and data-aware query recommendation to meet all four needs. In a nutshell, DASQR is a hybrid (collaborative filtering + content-based) approach. Its variations utilize all similarity functions, which we define or find in the related work. Measuring the quality of SQL query recommendation system (QRS) is particularly challenging since there is no standard way approaching it. Previous studies have evaluated the results using quality metrics which only rely on the query representations used in these studies. It is somewhat subjective since a similarity function and a quality metric are dependent. We propose AAB quality metrics and then evaluate each approach based on all the metrics. The experiments test DASQR approaches and competitors. Both performance and runtime experiments indicate that DASQR approaches outperform the existing ones

In silico estimation of annealing specificity of query searches in DNA databases

We consider DNA implementations of databases for digital signals with retrieval and mining capabilities. Digital signals are encoded in DNA sequences and retrieved through annealing between query DNA primers and data carrying DNA target sequences. The hybridization between query and target can be non-specific containing multiple mismatches thus implementing similarity-based searches. In this paper we examine theoretically and by simulation the efficiency of such a system by estimating the concentrations of query-target duplex formations at equilibrium. A coupled kinetic model is used to estimate the concentrations. We offer a derivation that results in an equation that is guaranteed to have a solution and can be easily and accurately solved computationally with bi-section root-finding methods. Finally, we also provide an approximate solution at dilute query concentrations that results in a closed form expression. This expression is used to improve the speed of the bi-section algorithm and also to find a closed form expression for the specificity ratios

Exploring the effectiveness of similarity-based visualisations for colour-based image retrieval

In April 2009, Google Images added a filter for narrowing search results by colour. Several other systems for searching image databases by colour were also released around this time. These colour-based image retrieval systems enable users to search image databases either by selecting colours from a graphical palette (i.e., query-by-colour), by drawing a representation of the colour layout sought (i.e., query-by-sketch), or both. It was comments left by readers of online articles describing these colour-based image retrieval systems that provided us with the inspiration for this research. We were surprised to learn that the underlying query-based technology used in colour-based image retrieval systems today remains remarkably similar to that of systems developed nearly two decades ago. Discovering this ageing retrieval approach, as well as uncovering a large user demographic requiring image search by colour, made us eager to research more effective approaches for colour-based image retrieval. In this thesis, we detail two user studies designed to compare the effectiveness of systems adopting similarity-based visualisations, query-based approaches, or a combination of both, for colour-based image retrieval. In contrast to query-based approaches, similarity-based visualisations display and arrange database images so that images with similar content are located closer together on screen than images with dissimilar content. This removes the need for queries, as users can instead visually explore the database using interactive navigation tools to retrieve images from the database. As we found existing evaluation approaches to be unreliable, we describe how we assessed and compared systems adopting similarity-based visualisations, query-based approaches, or both, meaningfully and systematically using our Mosaic Test - a user-based evaluation approach in which evaluation study participants complete an image mosaic of a predetermined target image using the colour-based image retrieval system under evaluation

Evaluation of a Bayesian inference network for ligand-based virtual screening

Background Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query. Here, we modify the approach to enable a Bayesian inference network to be used for chemical similarity searching, where a database is ranked in order of decreasing probability of bioactivity. Results Bayesian inference networks were implemented using two different types of network and four different types of belief function. Experiments with the MDDR and WOMBAT databases show that a Bayesian inference network can be used to provide effective ligand-based screening, especially when the active molecules being sought have a high degree of structural homogeneity; in such cases, the network substantially out-performs a conventional, Tanimoto-based similarity searching system. However, the effectiveness of the network is much less when structurally heterogeneous sets of actives are being sought. Conclusion A Bayesian inference network provides an interesting alternative to existing tools for ligand-based virtual screening

Div-BLAST: Diversification of sequence search results

Cataloged from PDF version of article.Sequence similarity tools, such as BLAST, seek sequences most similar to a query from a database of sequences. They return results significantly similar to the query sequence and that are typically highly similar to each other. Most sequence analysis tasks in bioinformatics require an exploratory approach, where the initial results guide the user to new searches. However, diversity has not yet been considered an integral component of sequence search tools for this discipline. Some redundancy can be avoided by introducing non-redundancy during database construction, but it is not feasible to dynamically set a level of non-redundancy tailored to a query sequence. We introduce the problem of diverse search and browsing in sequence databases that produce non-redundant results optimized for any given query. We define diversity measures for sequences and propose methods to obtain diverse results extracted from current sequence similarity search tools. We also propose a new measure to evaluate the diversity of a set of sequences that is returned as a result of a sequence similarity query. We evaluate the effectiveness of the proposed methods in post-processing BLAST and PSI-BLAST results. We also assess the functional diversity of the returned results based on available Gene Ontology annotations. Additionally, we include a comparison with a current redundancy elimination tool, CD-HIT. Our experiments show that the proposed methods are able to achieve more diverse yet significant result sets compared to static non-redundancy approaches. In both sequence-based and functional diversity evaluation, the proposed diversification methods significantly outperform original BLAST results and other baselines. A web based tool implementing the proposed methods, Div-BLAST, can be accessed at cedar.cs.bilkent.edu.tr/Div-BLAS

Object-based Image Ranking using Neural Networks

In this paper an object-based image ranking is performed using both supervised and unsupervised neural networks. The features are extracted based on the moment invariants, the run length, and a composite method. This paper also introduces a likeness parameter, namely a similarity measure using the weights of the neural networks. The experimental results show that the performance of image retrieval depends on the method of feature extraction, types of learning, the values of the parameters of the neural networks, and the databases including query set. The best performance is achieved using supervised neural networks for internal query set

Relaxation of Subgraph Queries Delivering Empty Results

Graph databases with the property graph model are used in multiple domains including social networks, biology, and data integration. They provide schema-flexible storage for data of a different degree of a structure and support complex, expressive queries such as subgraph isomorphism queries. The exibility and expressiveness of graph databases make it difficult for the users to express queries correctly and can lead to unexpected query results, e.g. empty results. Therefore, we propose a relaxation approach for subgraph isomorphism queries that is able to automatically rewrite a graph query, such that the rewritten query is similar to the original query and returns a non-empty result set. In detail, we present relaxation operations applicable to a query, cardinality estimation heuristics, and strategies for prioritizing graph query elements to be relaxed. To determine the similarity between the original query and its relaxed variants, we propose a novel cardinality-based graph edit distance. The feasibility of our approach is shown by using real-world queries from the DBpedia query log