Keywords:Limitations of existing tools Second generation tools Tool architecture Parallel processing Tool metrics Data design & abstraction Forensic Workflow Functional decomposition Standardised tests a b s t r a c tThe architecture of existing -first generation -computer forensic tools, including the widely used EnCase and FTK products, is rapidly becoming outdated. Tools are not keeping pace with increased complexity and data volumes of modern investigations. This paper discuses the limitations of first generation computer forensic tools. Several metrics for measuring the efficacy and performance of computer forensic tools are introduced. A set of requirements for second generation tools are proposed. A high-level design for a (work in progress) second generation computer forensic analysis system is presented.
NMR offers the possibility of accurate secondary structure for proteins that would be too large for structure determination. In the absence of an X-ray crystal structure, this information should be useful as an adjunct to protein fold recognition methods based on low resolution force fields. The value of this information has been tested by adding varying amounts of artificial secondary structure data and threading a sequence through a library of candidate folds. Using a literature test set, the threading method alone has only a one-third chance of producing a correct answer among the top ten guesses. With realistic secondary structure information, one can expect a 60-80% chance of finding a homologous structure. The method has then been applied to examples with published estimates of secondary structure. This implementation is completely independent of sequence homology, and sequences are optimally aligned to candidate structures with gaps and insertions allowed. Unlike work using predicted secondary structure, we test the effect of differing amounts of relatively reliable data.Keywords: chemical shift index; fold recognition; protein folding; protein structure prediction; protein threading; remote homology detection; secondary structureThere is no shortage of methods for predicting a protein's structure based only on its sequence~Böhm, 1996; Westhead & Thornton, 1998!. Unfortunately, unless the sequence has significant sequence homology to something of known structure, one could not regard any of the methods as reliable~Lesk, 1997; Levitt, 1997; MarchlerBauer et al., 1997!. At the same time, they may be the only means of predicting structure in the absence of experimental data. A different problem arises for a protein sequence when a limited amount of experimental information is available. A typical case might come from a protein that yields a barely useful NMR spectrum. In this situation, one would like to use the available data, even if it is not suitable for conventional high resolution structure calculations. This has led to a series of approaches that have their roots in structure prediction, but attempt to incorporate very sparse experimental data such as a few intramolecular distance estimates~Smith- Brown et al., 1993;Aszódi et al., 1995;Lund et al., 1996; Skolnick et al., 1997!. Typically, these methods produce low resolution structures and operate with the caveat that answers may sometimes be quite wrong.Taking this theme further, NMR data may provide still more low resolution data. Even if a protein's structure will never be solved, its proton and heteronuclear NMR assignments may be largely determined. The relationship between chemical shift and structure has long been recognized~Pardi et al., 1983; Spera & Bax, 1991!, but it can be better quantified. Given a fairly complete set of proton and heteronuclear chemical shifts, one can expect secondary structure assignments to be more than 92% accurate~Wishart et al., 199192% accurate~Wishart et al., , 1992Wishart & Sykes, 1994a!. The aim of ...
Sausage is a protein sequence threading program, but with remarkable run-time flexibility. Using different scripts, it can calculate protein sequence-structure alignments, search structure libraries, swap force fields, create models from alignments, convert file formats and analyse results. There are several different force fields which might be classed as knowledge-based, although they do not rely on Boltzmann statistics. Different force fields are used for alignment calculations and subsequent ranking of calculated models.
In chaotic dynamical systems such as the weather, prediction errors grow faster in some situations than in others.Real-time knowledge about the error growth could enable strategies to adjust the modelling and forecasting infrastructure on-the-fly to increase accuracy and/or reduce computation time. For example one could change the spatiotemporal resolution of the numerical model, locally increase the data availability, etc. Local Lyapunov exponents are known indicators of the rate at which very small prediction errors grow over a finite time interval. However, their computation is very expensive: it requires maintaining and evolving a tangent linear model, orthogonalisation algorithms and storing large matrices. In this study, we investigate the capability of supervised machine learning to estimate the imminent local Lyapunov exponents, from input of current and recent time steps of the system trajectory, as an alternative to the classical method. Thus machine learning is not used here to emulate a physical model or some of its components, but "non intrusively" as a complementary tool. Specifically, we test the accuracy of four popular supervised learning algorithms: regression trees, multilayer perceptrons, convolutional neural networks and long short-term memory networks. Experiments are conducted 1
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.