41 research outputs found

    FuzzyPPI: Human Proteome at Fuzzy Semantic Space

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    Large scale protein-protein interaction (PPI) network of an organism provides key insights into the cellular and molecular functionalities, signaling pathways and underlying disease mechanisms. If we consider the complete interactome of any given organism, the total number of unexplored protein interactions significantly outnumbers the known positive and negative interactions. For Human 20,350 reviewed proteins can generate over ~207 million potential interactions. However, the combination of all known PPI datasets, contains only ~5.6 million positive and ~758k negative protein-protein interactions (NPPI), that together is ~3.1% what is more, conventional PPI prediction methods produce binary results. At the same time recent studies show that protein binding affinities may prove to be effective in detecting protein complexes, disease association analysis, signaling network reconstruction, etc. In this work we present a fuzzy semantic scoring function using the Gene Ontology (GO) graphs to assess the binding affinity between any two proteins at an organism level. We have implemented a distributed algorithm in Apache Spark that computes this function and processed the complete Human PPI network of ~182 million potential interactions resulting from 19,106 reviewed proteins for which GO annotations are available. The quality of the computed scores has been validated with respect to the available state-of-the-art methods on benchmark data sets

    Multidimensional Bohr radii for vector-valued holomorphic functions

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    The main aim of this paper is to answer certain open questions related to the exact values of multidimensional Bohr radii by using the concept of arithmetic Bohr radius for vector-valued holomorphic functions defined in complete Reinhardt domains in Cn\mathbb{C}^n. More precisely, we study the asymptotic estimates of the arithmetic Bohr radius for holomorphic functions in the unit ball of qn\ell^n_q (1q)(1\leq q\leq \infty) spaces with values in arbitrary complex Banach spaces. Many of our results generalize the results obtained by Defant, Maestre, and Prengel [Q. J. Math. 59, (2008), pp. 189--205].Comment: We have revised some of proof of our result

    Bohr and Rogosinski inequalities for operator valued holomorphic functions

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    For any complex Banach space XX and each p[1,)p \in [1,\infty), we introduce the pp-Bohr radius of order N(N)N(\in \mathbb{N}) is R~p,N(X)\widetilde{R}_{p,N}(X) defined by \widetilde{R}_{p,N}(X)=\sup \left\{r\geq 0: \sum_{k=0}^{N}\norm{x_k}^p r^{pk} \leq \norm{f}^p_{H^{\infty}(\mathbb{D}, X)}\right\}, where f(z)=k=0xkzkH(D,X)f(z)=\sum_{k=0}^{\infty} x_{k}z^k \in H^{\infty}(\mathbb{D}, X). Here D={zC:z<1}\mathbb{D}= \{z\in \mathbb{C}: |z| <1\} denotes the unit disk. We also introduce the following geometric notion of pp-uniformly C\mathbb{C}-convexity of order NN for a complex Banach space XX for some NNN \in \mathbb{N}. In this paper, for p[2,)p\in [2,\infty) and each NNN \in \mathbb{N}, we prove that a complex Banach space XX is pp-uniformly C\mathbb{C}-convex of order NN if, and only if, the pp-Bohr radius of order NN R~p,N(X)>0\widetilde{R}_{p,N}(X)>0. We also study the pp-Bohr radius of order NN for the Lebesgue spaces Lq(μ)L^q (\mu) for 1p<q<1\leq p<q<\infty or 1qp<21\leq q \leq p <2. Finally, we prove an operator valued analogue of a refined version of Bohr and Rogosinski inequality for bounded holomorphic functions from the unit disk D\mathbb{D} into B(H)\mathcal{B(\mathcal{H})}, where B(H)\mathcal{B(\mathcal{H})} denotes the space of all bounded linear operator on a complex Hilbert space H\mathcal{H}.Comment: 16 page

    Composition-Differentiation Operator on Weighted Bergman Spaces

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    In this paper, we study the complex symmetry of weighted composition-differentiation operator Dn,ψ,ϕD_{n, \psi, \phi} on weighted Bergman spaces Aα2\mathcal{A}^2_{\alpha} with respect to the conjugation Cμ,ηC_{\mu, \eta} for μ,η{zC:z=1}\mu, \eta \in \{z\in \mathbb{C}:|z|=1\}. We obtain explicit conditions for which the operator Dn,ψ,ϕD_{n, \psi, \phi} is Hermitian and normal. We also characterize the complex symmetric weighted composition-differentiation operator for derivative Hardy spaces.Comment: 14 page

    \u27Silalipi\u27: Time-stamped historical artifacts or personal betrayals/ ‘শিলালিপি’ : সময় চিহ্নিত ইতিহাসের আখরমালা অথবা ব্যক্তিগত দ্রোহগাথা

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    Narrator Narayan Gangopadhyay has been a well-known figure in Bengali Literature. His political consciousness is very distinguished. Narayan Gangopadhyay\u27s political ideology and narrative portrayal have often been prominent in the elitist literary canon. Despite all his artistry, Narayan Gangopadhyay\u27s personal life and political ideals and choice leave the readers bewildered about his position as an author. However, his novel Shilalipi debunks all the doubts and confusion. In textual hermeneutics, the narrator, on one hand, upholds the trajectory of political history but on the other, the personal self of Narayan Gangopadhyay and his political identity has also immersed with it. Ranjan alias Ranju is the narrator of this novel. His perspective, turmoil and ideology are reflected in each and every segment and possible cessation of the author\u27s life

    RUBic: rapid unsupervised biclustering

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    Biclustering of biologically meaningful binary information is essential in many applications related to drug discovery, like protein-protein interactions and gene expressions. However, for robust performance in recently emerging large health datasets, it is important for new biclustering algorithms to be scalable and fast. We present a rapid unsupervised biclustering (RUBic) algorithm that achieves this objective with a novel encoding and search strategy. RUBic significantly reduces the computational overhead on both synthetic and experimental datasets shows significant computational benefits, with respect to several state-of-the-art biclustering algorithms. In 100 synthetic binary datasets, our method took ~71.1s to extract 494,872 biclusters. In the human PPI database of size 4085x4085, our method generates 1840 biclusters in ~48.6s. On a central nervous system embryonic tumor gene expression dataset of size 712,940, our algorithm takes   101 min to produce 747,069 biclusters, while the recent competing algorithms take significantly more time to produce the same result. RUBic is also evaluated on five different gene expression datasets and shows significant speed-up in execution time with respect to existing approaches to extract significant KEGG-enriched bi-clustering. RUBic can operate on two modes, base and flex, where base mode generates maximal biclusters and flex mode generates less number of clusters and faster based on their biological significance with respect to KEGG pathways. The code is available at ( https://github.com/CMATERJU-BIOINFO/RUBic ) for academic use only

    Role of rural off-farm employment in earning income and livelihood in the coastal region of West Bengal, India

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    The study was conducted in the coastal region of West Bengal, India to document the prevalent farming systems and explore the opportunity of nonfarm activities in generating income and livelihood for the rural households. This paper concentrates in finding out the key determinants of participation in nonfarm income and employment generation activities across rural households. The analytical framework yields different activity choices as optimal solutions to a simple utility maximization problem. The empirical inquiry reveals that education, family size and access to land assets plays major role in accessing more remunerative nonfarm employment. The region is quite underdeveloped such that traditional rural self-employment activities still contributes 30.94 percent of household income and provide employment to 40.71 percent rural household. The number of working men, number of working women, age and education level are the other important determinants of nonfarm activities for the rural households
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