Learning the Edit Costs of Graph Edit Distance Applied to Ligand-Based Virtual Screening.

BACKGROUND: Graph edit distance is a methodology used to solve error-tolerant graph matching. This methodology estimates a distance between two graphs by determining the minimum number of modifications required to transform one graph into the other. These modifications, known as edit operations, have an edit cost associated that has to be determined depending on the problem. OBJECTIVE: This study focuses on the use of optimization techniques in order to learn the edit costs used when comparing graphs by means of the graph edit distance. METHODS: Graphs represent reduced structural representations of molecules using pharmacophore-type node descriptions to encode the relevant molecular properties. This reduction technique is known as extended reduced graphs. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were used. RESULTS: In the experiments, the graph edit distance using learned costs performed better or equally good than using predefined costs. This is exemplified with six publicly available datasets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. CONCLUSION: This study shows that the graph edit distance along with learned edit costs is useful to identify bioactivity similarities in a structurally diverse group of molecules. Furthermore, the target-specific edit costs might provide useful structure-activity information for future drug-design efforts.

FlexGraph: Flexible partitioning and storage for scalable graph mining.

How can we analyze large graphs such as the Web, and social networks with hundreds of billions of vertices and edges? Although many graph mining systems have been proposed to perform various graph mining algorithms on such large graphs, they have difficulties in processing Web-scale graphs due to massive communication and I/O costs caused by communication between workers, and reading subgraphs repeatedly. In this paper, we propose FlexGraph, a scalable distributed graph mining method reducing the costs by exploiting properties of real-world graphs. FlexGraph significantly decreases the communication cost, which is the main bottleneck of distributed systems, by exploiting different edge placement policies based on types of vertices. Furthermore, we propose a flexible storage format to reduce I/O costs when reading input graph repeatedly. Experiments show that FlexGraph succeeds in processing up to 64× larger graphs than existing distributed memory-based graph mining methods, and consistently outperforms previous disk-based graph mining methods.

A GPU-accelerated and Monte Carlo-based intensity modulated proton therapy optimization system.

PURPOSE: Conventional spot scanning intensity modulated proton therapy (IMPT) treatment planning systems (TPSs) optimize proton spot weights based on analytical dose calculations. These analytical dose calculations have been shown to have severe limitations in heterogeneous materials. Monte Carlo (MC) methods do not have these limitations; however, MC-based systems have been of limited clinical use due to the large number of beam spots in IMPT and the extremely long calculation time of traditional MC techniques. In this work, the authors present a clinically applicable IMPT TPS that utilizes a very fast MC calculation. METHODS: An in-house graphics processing unit (GPU)-based MC dose calculation engine was employed to generate the dose influence map for each proton spot. With the MC generated influence map, a modified least-squares optimization method was used to achieve the desired dose volume histograms (DVHs). The intrinsic CT image resolution was adopted for voxelization in simulation and optimization to preserve spatial resolution. The optimizations were computed on a multi-GPU framework to mitigate the memory limitation issues for the large dose influence maps that resulted from maintaining the intrinsic CT resolution. The effects of tail cutoff and starting condition were studied and minimized in this work. RESULTS: For relatively large and complex three-field head and neck cases, i.e., >100,000 spots with a target volume of ∼ 1000 cm(3) and multiple surrounding critical structures, the optimization together with the initial MC dose influence map calculation was done in a clinically viable time frame (less than 30 min) on a GPU cluster consisting of 24 Nvidia GeForce GTX Titan cards. The in-house MC TPS plans were comparable to a commercial TPS plans based on DVH comparisons. CONCLUSIONS: A MC-based treatment planning system was developed. The treatment planning can be performed in a clinically viable time frame on a hardware system costing around 45,000 dollars. The fast calculation and optimization make the system easily expandable to robust and multicriteria optimization.

Comparison of 3D reconstructive technologies used for morphometric research and the translation of knowledge using a decision matrix.

The use of three-dimensional (3D) models for education, pre-operative assessment, presurgical planning, and measurement have become more prevalent. With the increase in prevalence of 3D models there has also been an increase in 3D reconstructive software programs that are used to create these models. These software programs differ in reconstruction concepts, operating system requirements, user features, cost, and no one program has emerged as the standard. The purpose of this study was to conduct a systematic comparison of three widely available 3D reconstructive software programs, Amira(®), OsiriX, and Mimics(®) , with respect to the software's ability to be used in two broad themes: morphometric research and education to translate morphological knowledge. Cost, system requirements, and inherent features of each program were compared. A novel concept selection tool, a decision matrix, was used to objectify comparisons of usability of the interface, quality of the output, and efficiency of the tools. Findings indicate that Mimics was the best-suited program for construction of 3D anatomical models and morphometric analysis, but for creating a learning tool the results were less clear. OsiriX was very user-friendly; however, it had limited capabilities. Conversely, although Amira had endless potential and could create complex dynamic videos, it had a challenging interface. These results provide a resource for morphometric researchers and educators to assist the selection of appropriate reconstruction programs when starting a new 3D modeling project.

Exploiting parallel R in the cloud with SPRINT.

BACKGROUND: Advances in DNA Microarray devices and next-generation massively parallel DNA sequencing platforms have led to an exponential growth in data availability but the arising opportunities require adequate computing resources. High Performance Computing (HPC) in the Cloud offers an affordable way of meeting this need. OBJECTIVES: Bioconductor, a popular tool for high-throughput genomic data analysis, is distributed as add-on modules for the R statistical programming language but R has no native capabilities for exploiting multi-processor architectures. SPRINT is an R package that enables easy access to HPC for genomics researchers. This paper investigates: setting up and running SPRINT-enabled genomic analyses on Amazon's Elastic Compute Cloud (EC2), the advantages of submitting applications to EC2 from different parts of the world and, if resource underutilization can improve application performance. METHODS: The SPRINT parallel implementations of correlation, permutation testing, partitioning around medoids and the multi-purpose papply have been benchmarked on data sets of various size on Amazon EC2. Jobs have been submitted from both the UK and Thailand to investigate monetary differences. RESULTS: It is possible to obtain good, scalable performance but the level of improvement is dependent upon the nature of the algorithm. Resource underutilization can further improve the time to result. End-user's location impacts on costs due to factors such as local taxation. CONCLUSIONS: Although not designed to satisfy HPC requirements, Amazon EC2 and cloud computing in general provides an interesting alternative and provides new possibilities for smaller organisations with limited funds.

High-throughput sequence alignment using Graphics Processing Units.

BACKGROUND: The recent availability of new, less expensive high-throughput DNA sequencing technologies has yielded a dramatic increase in the volume of sequence data that must be analyzed. These data are being generated for several purposes, including genotyping, genome resequencing, metagenomics, and de novo genome assembly projects. Sequence alignment programs such as MUMmer have proven essential for analysis of these data, but researchers will need ever faster, high-throughput alignment tools running on inexpensive hardware to keep up with new sequence technologies. RESULTS: This paper describes MUMmerGPU, an open-source high-throughput parallel pairwise local sequence alignment program that runs on commodity Graphics Processing Units (GPUs) in common workstations. MUMmerGPU uses the new Compute Unified Device Architecture (CUDA) from nVidia to align multiple query sequences against a single reference sequence stored as a suffix tree. By processing the queries in parallel on the highly parallel graphics card, MUMmerGPU achieves more than a 10-fold speedup over a serial CPU version of the sequence alignment kernel, and outperforms the exact alignment component of MUMmer on a high end CPU by 3.5-fold in total application time when aligning reads from recent sequencing projects using Solexa/Illumina, 454, and Sanger sequencing technologies. CONCLUSION: MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by new, high-throughput sequencing technologies. MUMmerGPU demonstrates that even memory-intensive applications can run significantly faster on the relatively low-cost GPU than on the CPU.

Inexpensive ethography using digital video.

We describe an inexpensive method for digital video recording of behavioral experiments and present a simple, freely-redistributable software tool enabling ethographic analysis of these recordings via classification of video frames into user-defined categories. While high-end commercial solutions already exist for these purposes, we focus on minimizing equipment expenses and complexity for research projects or undergraduate laboratory courses employing compatible behavioral paradigms.

Diving into the depths of immersive 3D.

NASA: Commercial use of three-dimensional imaging is discussed. Applications include surgical simulation in medical education, simulation of stealthy torpedos, astronaut training, and data visualization.^ieng

The transition to digital media in biocommunications.

As digital audiovisual media become dominant in biomedical communications, the skills of human interface design and the technology of client-server multimedia data networks will underlie and influence virtually every aspect of biocommunications professional practice. The transition to digital communications media will require financial, organizational, and professional changes in current biomedical communications departments, and will require a multi-disciplinary approach that will blur the boundaries of the current biocommunications professions.

Computer-generated slide graphics on a shoestring budget.

The use of computer-generated slides in weekly didactic conferences and at regional and national meetings is commonplace. Though adding a professional look, the expense of computer-generated slides can be prohibitive. We herein present a simple technique for generating color slides that captures the professional look of computer-generated slides at a fraction of the cost.

The application of fractal clustering to efficient molecular ray tracing on low-cost computers.

Ray tracing is a powerful, and highly computer intensive means for generating high-quality molecular displays. A variety of simple, yet effective optimization strategies are described that allow large molecular models to be ray traced on microcomputers and low-cost desktop workstations. In particular, the method of fractal clustering provides a time and space-efficient means for spatially subdividing the molecular scene into a hierarchy of spherical bounding volumes, permitting ray-atom intersections to be determined by a form of binary search. An implementation of the algorithms, MolRay, is described which demonstrates that large structures may be ray traced in a reasonable time on a PC or small Unix workstation. Images generated by PC and Unix versions of MolRay are shown.

[A simple and rapid method of preparing schematic anatomic drawings using modern computer technology]. / Einfache und schnelle Erstellung von anatomischen Schemazeichnungen mittels moderner Computertechnologie.

Function and usage of the graphic-software "MacDraw" is described and illustrated by drawing a lumbar vertebrae and pathologic clefts in a layer technique.