Higher eukaryotes’ phenotypes–the unique properties of a system composed of several genes and their relations–are entirely different from a set of properties of individual genes. During evolution, higher organisms’ genes become multipurpose and their phenotypic implication become abstract. We hypothesize that an operating system of life (OSL) exists in “genome space,” encoding a variety of phenotypes and realizing them through chemical circuits in “material space.” Genes’ generalization and abstraction explain the necessity of “genomic modules,” each of which encodes a unitary phenotype as a subroutine in the OSL. A genetic network is a projection map of the OSL’s active modules on the interface between the two spaces. We searched for genomic modules and intermodular networks developed in differentiating cells with the density matrix that mathematically defines a state of the system composed of genes and their relations, and demonstrated the modules’ relevance to the phenotypes of differentiating cells.
Because applying machine learning techniques in support of clinical decision would improve decision makers in healthcare, we present in this paper a comparative framework of Support Vector Machine (SVM) classifiers based on post operative patient (POP) data. We compare the performance of a single multiclass SVM and a multistage SVM (MSVM) to those obtained by a number of other classifiers presented in the literature and show that both SVM approaches significantly outperform the other methods resulting in 84.4% and 94.3% overall accuracy respectively. Results for the non-SVM classifiers ranged from 48% 77.7% accuracy.
In this work we proposed an ensemble of texture descriptors for virus image classification. Novel variants of texture descriptors, coupled with support vector machines as the classifier, are proposed. The novel variants of texture descriptors include: 1) a quinary coding of different local binary pattern variants; 2) two new approaches based on quinary coding (a selected multithreshold local quinary pattern and a selected multithreshold local quinary configuration pattern); 3) a new approach based on the cooccurrence matrix; and 4) an ensemble of local phase quantization variants with ternary encoding. Our system is compared with and shown to outperform several state of the art texture descriptors. These results are validated on a dataset of 1500 images with 15 classes. MATLAB code implementing our descriptors is publically available at http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_ pass=124&preview=
We introduce a new measure for comparing protein structures that is especially applicable to analysis of molecular dynamics simulation results. The new measure generalizes the widely used root-meansquared-deviation (RMSD) measure from three dimensional to n-dimensional Euclidean space, where n equals the number of atoms in the protein molecule. The new measure shows that despite significant fluctuations in the three dimensional geometry of the estrogen receptor protein, the protein’s intrinsic contact geometry is remarkably stable over nanosecond time scales. The new measure also identifies significant structural changes missed by RMSD for a residue that plays a key biological role in the estrogen receptor protein.
Correct interpretation of tandem mass spectrom- etry (MS/MS) data is a critical step in the protein identifi- cation process. Comparing experimental spectra against a library of simulated spectra generated from a database is the most common strategy for this interpretation. Unfortunately, problems arise when treating unsequenced species since, in this case, the proteins to be identified are absent from the databanks and experimental spectra can only be compared to theoretical spectra from close and already sequenced organisms. In this context, spectra comparisons become a notoriously difficult problem. In this paper, we deal with this problem by considerably improving PacketSpectralAlignment ( PSA ), a method we presented in [1]. First, we explain how to take full advantage of PSA by carefully selecting the most promising alignment positions during the algorithm, and how to precisely fix the parameters of PSA . Second, we present a new method, referred to as PSAwEL , which allows a better localisation of modifications. We then propose a new peptide identification framework that integrates these improvements. Finally, we propose a comparison between PSA and the reference, SpectralAlignment [2], which shows that PSA behaves better in terms of: (i) quality of the results; and (ii) execution time. Our tests were conducted on the ISB dataset [3]. We then validate our new framework on Brachypodi
The article deals to explain the object oriented architecture of the HL7 and its security issues. Primarily, the security breaches may occur at the direct interaction point of the HL7 architecture because of its interaction with the users. This direct interaction point is OnDemandDataSource class in the existing object oriented architecture of HL7. To resolve this problem an extended secure architecture of HL7 is provided in the paper. In this two new classes are suggested, one is SettingsManager, which deals to convert the input information in the XML formatted string object and another is DataAccessSettingsManager, which deals to implement all the security measures available in XML document. This XML document can contains different security measures such as Integrity and signatures, Confidentiality, Key Management, Authentication and Authorization, etc. Basic templates to implement these security measures by using XML are also provided for better understanding.
Deciphering the biological networks underlying complex phenotypic traits, e.g., human disease is undoubtedly crucial to understand the underlying molecular mechanisms and to develop effective therapeutics. Due to the network complexity and the relatively small number of available experiments, data-driven modeling is a great challenge for deducing the functions of genes/proteins in the network and in phenotype formation. We propose a novel knowledge-driven systems biology method that utilizes qualitative knowledge to construct a Dynamic Bayesian network (DBN) to represent the biological network underlying a specific phenotype. Edges in this network depict physical interactions between genes and/or proteins. A qualitative knowledge model first translates typical molecular interactions into constraints when resolving the DBN structure and parameters. Therefore, the uncertainty of the network is restricted to a subset of models which are consistent with the qualitative knowledge. All models satisfying the constraints are considered as candidates for the underlying network. These consistent models are used to perform quantitative inference. By in silico inference, we can predict phenotypic traits upon genetic interventions and perturbing in the network. We applied our method to analyze the puzzling mechanism of breast cancer cell proliferation network and we accurately predicted cancer cell growth rate upon manipulating (anti)cancerous marker genes/proteins.
This work presents a novel approach to detect a change in the state of a signal. We propose that in a given state, the values of a signal vary in a subrange of a Gaussian distribution. We describe methods to monitor a signal in real time for change points based upon sub-Gaussian fitting. The proposed algorithm was implemented and tested on heartrate variability data.
