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SOME GENERALIZED BOUNDS ON RELATIVE ENTROPIES
M.A.K. Baig,Rayees Ahmad Dar 한국산업응용수학회 2006 Journal of the Korean Society for Industrial and A Vol.10 No.2-1
Divergence measures and information inequalities are well known in the literature of information theory. In this paper we proposed some generalized bounds on Csiszar's f-divergence in terms of relative J-divergence of type s. The bounds in terms well known divergence measures such as relative J-divergence, Chi-square divergence and triangular discrimination becomes the particular cases of our proposed bounds.
Baig, M. H.,Ahmad, K.,Hasan, Q.,Khan, M. K. A.,Rao, N. S.,Kamal, M. A.,Choi, I. Hindawi Publishing Corporation 2015 Evidence-based Complementary and Alternative Medic Vol.2015 No.-
<P>Glucagon receptor (GCGR) is a secretin-like (class B) family of G-protein coupled receptors (GPCRs) in humans that plays an important role in elevating the glucose concentration in blood and has thus become one of the promising therapeutic targets for treatment of type 2 diabetes mellitus. GCGR based inhibitors for the treatment of type 2 diabetes are either glucagon neutralizers or small molecular antagonists. Management of diabetes without any side effects is still a challenge to the medical system, and the search for a new and effective natural GCGR antagonist is an important area for the treatment of type 2 diabetes. In the present study, a number of natural compounds containing antidiabetic properties were selected from the literature and their binding potential against GCGR was determined using molecular docking and other<I> in silico</I> approaches. Among all selected natural compounds, curcumin was found to be the most effective compound against GCGR followed by amorfrutin 1 and 4-hydroxyderricin. These compounds were rescored to confirm the accuracy of binding using another scoring function (<I>x</I>-score). The final conclusions were drawn based on the results obtained from the GOLD and <I>x</I>-score. Further experiments were conducted to identify the atomic level interactions of selected compounds with GCGR.</P>
Coding Theorems on A Generalized Information Measures
( M. A. K. Baig ),( Rayees Ahmad Dar ) 한국산업응용수학회(구 한국산업정보응용수학회) 2007 한국산업정보응용수학회 Vol.11 No.2
In this paper a generalized parametric mean length L(Pv, R) has been defined an bounds for L(Pv, R) are obtained in terms of generalized R-norm information measure.
CODING THEOREMS ON A GENERALIZED INFORMATION MEASURES
M.A.K Baig,Rayees Ahmad Dar 한국산업응용수학회 2007 Journal of the Korean Society for Industrial and A Vol.11 No.2
In this paper a generalized parametric mean length L(P<SUP>v</SUP>, R) has been defined and bounds for L(P<SUP>v</SUP>, R) are obtained in terms of generalized R-norm information measure.
Ahmad, K.,Baig, M.H.,Gupta, G.K.,Kamal, M.A.,Pathak, N.,Choi, I. Elsevier 2016 Journal of computational science Vol.17 No.1
<P>Neurodegenerative disorders (NDs) are a heterogeneous group of disorders generally characterized by a profound decrease in the size and volume of the human brain due to death of neurons. These disorders include a variety of progressive disorders that result in cognitive and/or motor degradation. The present study was conducted to identify common potential targets for multi-neurodegenerative diseases. To accomplish this, we have selected six common neurodegenerative diseases, Alzheimer's disease (AD), Parkinson's disease (PD), Amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), Prion disease and Dentatorubral-pallidoluysian atrophy (DRPLA) for identification of common regulatory target proteins. A total of sixteen common proteins were identified as target proteins by disease pathway analysis and previous studies based on their association with more than two NDs, including AD. An interaction network of each of the sixteen target proteins was then constructed against causative proteins selected from all six NDs by using the STRING 9.1 program. Pathway analysis and the protein-protein interaction network suggested that CASP-3 and CASP-8 were associated with the maximum number of selected NDs and may therefore be the most potent target proteins for, treatment of multi-neurodegenerative diseases. (C) 2016 Elsevier B.V. All rights reserved.</P>
Safa M.,Pandian A.,Mohammad Gouse Baig,Reddy Sadda Bharath,Kumar K. Satish,Banu A. S. Gousia,Srihari K.,Chandragandhi S. 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.4
Cardiac disease analysis in big data is an emerging factor for human health protection against heart attacks. Most cardiovascular diseases lead to heart failure due to an imbalance of immunity and attention in health conditions. Hence, immunity-based feature analysis of patients’ records is essential to predict accurate results. The machine learning methods make predictions depending on the extended-lasting features to analyze the health data. But the marginal features expose non-relational feature observation to reduce the classifi cation prediction accuracy. We propose a Deep Spectral Time-Variant Feature Analytic Model (DSTV-FAM) using SoftMax Recurrent Neural Network (SMRNN) in a wireless sensor network to improve cardiac disease prediction accuracy. Initially, the IoT sensor devices collect the data from patient observation to validate the data transmission in route propagation. The collected data is organized as features in the collective dataset. The parts are initially preprocessed into the redundant dataset and estimate the Cardiac Immunity Infl uence Rate (CIIR) depending on the time-variant feature selection model. The estimated weights are marginalized as spectral features trained into the classifi ers. Further, Soft-Max Activation Function (SMAF) creates a logical function depending on the Cardiac Aff ection Rate (CAR). Then the trained, rational neurons are constructed into a Recurrent Neural Network (RNN) Feed-forward feature values using a classifi er and Rate of Disease Aff ection (RDA) by Class Type. The proposed structure yields high prescient exactness concerning order, accuracy, and review to help early treatment for early cardiovascular gamble expectation.
Lee, E. J.,Jan, A. T.,Baig, M. H.,Ahmad, K.,Malik, A.,Rabbani, G.,Kim, T.,Lee, I.-K.,Lee, Y. H.,Park, S.-Y. Federation of American Societies for Experimental 2018 The FASEB Journal Vol. No.
<P>Interactions between myoblasts and the surrounding microenvironment led us to explore the role of fibromodulin (FMOD), an extracellular matrix protein, in the maintenance of myoblast stemness and function. Microarray analysis of FMODkd myoblasts and in silico studies were used to identify the top most differentially expressed genes in FMODkd, and helped establish that FMOD-based regulations of integral membrane protein 2a and clusterin are essential components of the myogenic program. Studies in knockout, obese, and diabetic mouse models helped characterize the operation of a novel FMOD-based regulatory circuit that controls myoblast switching from a myogenic to a lipid accumulation fate. FMOD regulation of myoblasts is an essential part of the myogenic program, and it offers opportunities for the development of therapeutics for the treatment of different muscle diseases.</P>
Characterization of New Two Parametric Generalized Useful Information Measure
Bhat, Ashiq Hussain,Baig, M. A. K. Korea Institute of Science and Technology Informat 2016 Journal of Information Science Theory and Practice Vol.4 No.4
In this paper we define a two parametric new generalized useful average code-word length $L_{\alpha}^{\beta}$(P;U) and its relationship with two parametric new generalized useful information measure $H_{\alpha}^{\beta}$(P;U) has been discussed. The lower and upper bound of $L_{\alpha}^{\beta}$(P;U), in terms of $H_{\alpha}^{\beta}$(P;U) are derived for a discrete noiseless channel. The measures defined in this communication are not only new but some well known measures are the particular cases of our proposed measures that already exist in the literature of useful information theory. The noiseless coding theorems for discrete channel proved in this paper are verified by considering Huffman and Shannon-Fano coding schemes on taking empirical data. Also we study the monotonic behavior of $H_{\alpha}^{\beta}$(P;U) with respect to parameters ${\alpha}$ and ${\beta}$. The important properties of $H_{{\alpha}}^{{\beta}}$(P;U) have also been studied.