Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis

This paper reviews the work of the Chemoinformatics Research Group in the Department of Information Studies at the University of Sheffield, focusing particularly on the work carried out in the period 1985-2002. Four major research areas are discussed, these involving the development of methods for: substructure searching in databases of three-dimensional structures, including both rigid and flexible molecules; the representation and searching of the Markush structures that occur in chemical patents; similarity searching in databases of both two-dimensional and three-dimensional structures; and compound selection and the design of combinatorial libraries. An analysis of citations to 321 publications from the Group shows that it attracted a total of 3725 residual citations during the period 1980-2002. These citations appeared in 411 different journals, and involved 910 different citing organizations from 54 different countries, thus demonstrating the widespread impact of the Group's work

Pattern discovery in adverse event data

Imperial Users onl

Subseries Join and Compression of Time Series Data Based on Non-uniform Segmentation

A time series is composed of a sequence of data items that are measured at uniform intervals. Many application areas generate or manipulate time series, including finance, medicine, digital audio, and motion capture. Efficiently searching a large time series database is still a challenging problem, especially when partial or subseries matches are needed. This thesis proposes a new denition of subseries join, a symmetric generalization of subseries matching, which finds similar subseries in two or more time series datasets. A solution is proposed to compute the subseries join based on a hierarchical feature representation. This hierarchical feature representation is generated by an anisotropic diffusion scale-space analysis and a non-uniform segmentation method. Each segment is represented by a minimal polynomial envelope in a reduced-dimensionality space. Based on the hierarchical feature representation, all features in a dataset are indexed in an R-tree, and candidate matching features of two datasets are found by an R-tree join operation. Given candidate matching features, a dynamic programming algorithm is developed to compute the final subseries join. To improve storage efficiency, a hierarchical compression scheme is proposed to compress features. The minimal polynomial envelope representation is transformed to a Bezier spline envelope representation. The control points of each Bezier spline are then hierarchically differenced and an arithmetic coding is used to compress these differences. To empirically evaluate their effectiveness, the proposed subseries join and compression techniques are tested on various publicly available datasets. A large motion capture database is also used to verify the techniques in a real-world application. The experiments show that the proposed subseries join technique can better tolerate noise and local scaling than previous work, and the proposed compression technique can also achieve about 85% higher compression rates than previous work with the same distortion error

Similarity Methods in Chemoinformatics

promoting access to White Rose research paper

Mass & secondary structure propensity of amino acids explain their mutability and evolutionary replacements

Why is an amino acid replacement in a protein accepted during evolution? The answer given by bioinformatics relies on the frequency of change of each amino acid by another one and the propensity of each to remain unchanged. We propose that these replacement rules are recoverable from the secondary structural trends of amino acids. A distance measure between high-resolution Ramachandran distributions reveals that structurally similar residues coincide with those found in substitution matrices such as BLOSUM: Asn Asp, Phe Tyr, Lys Arg, Gln Glu, Ile Val, Met → Leu; with Ala, Cys, His, Gly, Ser, Pro, and Thr, as structurally idiosyncratic residues. We also found a high average correlation (\overline{R} R = 0.85) between thirty amino acid mutability scales and the mutational inertia (I X ), which measures the energetic cost weighted by the number of observations at the most probable amino acid conformation. These results indicate that amino acid substitutions follow two optimally-efficient principles: (a) amino acids interchangeability privileges their secondary structural similarity, and (b) the amino acid mutability depends directly on its biosynthetic energy cost, and inversely with its frequency. These two principles are the underlying rules governing the observed amino acid substitutions. © 2017 The Author(s)

PROTEIN SURFACE SIMILARITIES EVALUATION FOR FUNCTIONAL ANNOTATION STUDIES

One of the main targets of bioinformatics is to assign functions to proteins whose function is unknown relying on homologies identifications with proteins with known functions. Several approaches are currently available: the best choice depends on the evolutionary distance that separates the protein of interest from its homologous. Recently attention has been focused on molecular surfaces since they do not depend on the three-dimensional structure and allow similarities to be identified which other methods can\u2019t identify. Furthermore, molecular surfaces are the interface of interaction between molecules, and their geometrical and physical descriptions will lead to the comprehension of the molecular recognition process, since the geometrical component has a fundamental role in the early stage of complex formation. This particular aspect would have a major impact in the field of drug design and in the understanding of the side effects due to interactions between proteins. During this thesis a protocol for similarities identification on molecular surfaces has been developed and optimized. In this process, molecular surfaces are calculated according to Lee Richard\u2019s model, and then are represented through triangular meshes. Successively surfaces are transformed into a set of object oriented images using a computer vision approach. This type of representation has the advantage of being independent from the position of the objects represented, and thus similar surfaces can be described by similar images. The search for similarities is then performed by indentifying correspondences between pairs of similar images, by filtering matches relying on geometrical criteria and then by clustering correspondences in high similarity groups. These groups are then used to align surfaces in order to evaluate results both by visual inspection and through appropriate indexes. This process can be applied in the field of functional annotation, through the identification of similarities between surfaces of homologous proteins, and in study of interaction between proteins, through the identification of complementary areas between interacting proteins. The whole process of similarities detection depends on the configuration of 15 parameters that balance the time needed to perform calculation with the quality of results found. The problem of parameters estimation has been addressed using an implementation of genetic algorithm, which allowed representing different configuration parameters as a population in which individuals that are able to align surfaces satisfactory are rewarded with an high fitness score. The effectiveness of the algorithm was then improved by the introduction of neighbor heuristic which reduced the computational time required for correspondence clustering on surfaces. Particular interest was placed in results displaying and in the construction of indices that can quantify the quality of results. Regarding the visualization problem, a display system was implemented based on the Visualization ToolKit libraries in order to represent surfaces aligned as objects in three-dimensional space, enabling the user to interact with the scene represented by changing the point of view or enlarging details of the scene represented. Regarding the definition of useful indexes for results evaluation, two indexes had a fundamental role. The first one, called overlap index, measures the percentage of vertices of two surfaces that are closer than 1 A\ub0 after the alignment. This index in particular is useful for evaluating the surface similarity since similar aligned surfaces will have a large number of vertices closer than this distance. The second index, called RMSD, is important because it evaluates the Root Mean Square Deviation of alpha carbons of two aligned proteins in the case of a complementary search. This index allows evaluating how the aligned protein is distant from the correct position in the crystal complex. Concerning results evaluation, we have noticed that the consideration of electrostatic potential allows assigning good scores in case of strong geometrical similarity in context of functional annotations, thus facilitating the identification of homologous surfaces. This method has been validated both in the search of similarities and in the search of complementarities. Regarding the search of similarities, we tried to analyze a sample of 13 known proteins with a prosite domain in order to identify the presence of such domains on molecular surfaces. For doing this, we first reduced the number of structures present in the Protein Data Bank to a group of representative structures. Then we calculated the molecular surfaces for each representative protein and we created a dataset of patches corresponding to the prosite functional domain. The test was then performed trying to align the surface of the 13 known proteins to the patches dataset of functional domains. The results showed that in most cases we are able to properly align a functional domain to a protein surface with the same functional domain, and that these evidence was easily identifiable both by the parameters used for results evaluations, both by visually inspecting the results of the alignments. The method was then tested for complementary research, trying to reconstruct the protein-protein complex present in a well known dataset used to validate docking methods. In the case of searching for similarities it is important to describe surfaces in details in order to increase the accuracy, but high precision when searching for complementarity is counterproductive, since the interaction between proteins is not only determined by geometrical features but also involves the formation of favorable electrostatic interactions and rearrangements of side chains. Thus molecular surfaces were calculated using smoothed surfaces, where most details are lost but allowing to detect more easily interacting surfaces. Results showed that the algorithm is able to align complexes with comparable scores than the programs currently available; Considering this experimental design and that the method does not take into account the electrostatic potential, we can assume that the results obtained are particularly interesting since the proposed method provides a wider set of conformations than other algorithms, upon which we can extend the analysis in order to identify a better prediction. In conclusions the proposed system is able to identify similarities on molecular surfaces through the analysis of images of local description. The results show that the system implemented is effective in identifying similar surface areas in the context of functional annotation. In regards to the search for complementarities, the algorithm seems to have an interesting perspective, even though the best complex proposed is not always biologically correct. From this point of view, we have to do more analysis in order to improve the methods in protein interaction studies

Geometric and photometric affine invariant image registration

This thesis aims to present a solution to the correspondence problem for the registration of wide-baseline images taken from uncalibrated cameras. We propose an affine invariant descriptor that combines the geometry and photometry of the scene to find correspondences between both views. The geometric affine invariant component of the descriptor is based on the affine arc-length metric, whereas the photometry is analysed by invariant colour moments. A graph structure represents the spatial distribution of the primitive features; i.e. nodes correspond to detected high-curvature points, whereas arcs represent connectivities by extracted contours. After matching, we refine the search for correspondences by using a maximum likelihood robust algorithm. We have evaluated the system over synthetic and real data. The method is endemic to propagation of errors introduced by approximations in the system.BAE SystemsSelex Sensors and Airborne System

Patch-based Denoising Algorithms for Single and Multi-view Images

In general, all single and multi-view digital images are captured using sensors, where they are often contaminated with noise, which is an undesired random signal. Such noise can also be produced during transmission or by lossy image compression. Reducing the noise and enhancing those images is among the fundamental digital image processing tasks. Improving the performance of image denoising methods, would greatly contribute to single or multi-view image processing techniques, e.g. segmentation, computing disparity maps, etc. Patch-based denoising methods have recently emerged as the state-of-the-art denoising approaches for various additive noise levels. This thesis proposes two patch-based denoising methods for single and multi-view images, respectively. A modification to the block matching 3D algorithm is proposed for single image denoising. An adaptive collaborative thresholding filter is proposed which consists of a classification map and a set of various thresholding levels and operators. These are exploited when the collaborative hard-thresholding step is applied. Moreover, the collaborative Wiener filtering is improved by assigning greater weight when dealing with similar patches. For the denoising of multi-view images, this thesis proposes algorithms that takes a pair of noisy images captured from two different directions at the same time (stereoscopic images). The structural, maximum difference or the singular value decomposition-based similarity metrics is utilized for identifying locations of similar search windows in the input images. The non-local means algorithm is adapted for filtering these noisy multi-view images. The performance of both methods have been evaluated both quantitatively and qualitatively through a number of experiments using the peak signal-to-noise ratio and the mean structural similarity measure. Experimental results show that the proposed algorithm for single image denoising outperforms the original block matching 3D algorithm at various noise levels. Moreover, the proposed algorithm for multi-view image denoising can effectively reduce noise and assist to estimate more accurate disparity maps at various noise levels