Allam Appa Rao

University of Hyderabad, Bhaganagar, Andhra Pradesh, India

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Publications (82)32.21 Total impact

  • Computers & Electrical Engineering 07/2014; 40(5):1758–1765. · 0.99 Impact Factor
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    ABSTRACT: The paper presents a new approach for medical image segmentation. Exudates are a visible sign of diabetic retinopathy that is the major reason of vision loss in patients with diabetes. If the exudates extend into the macular area, blindness may occur. Automated detection of exudates will assist ophthalmologists in early diagnosis. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies K-means clustering to the image segmentation after getting optimized by Pillar algorithm; pillars are constructed in such a way that they can withstand the pressure. Improved pillar algorithm can optimize the K-means clustering for image segmentation in aspects of precision and computation time. This evaluates the proposed approach for image segmentation by comparing with Kmeans and Fuzzy C-means in a medical image. Using this method, identification of dark spot in the retina becomes easier and the proposed algorithm is applied on diabetic retinal images of all stages to identify hard and soft exudates, where the existing pillar K-means is more appropriate for brain MRI images. This proposed system help the doctors to identify the problem in the early stage and can suggest a better drug for preventing further retinal damage.
    Bioinformation 01/2014; 10(1):28-32. · 0.50 Impact Factor
  • M. N. VamsiThalatam, Allam Appa Rao
    International Journal of Computer Applications 12/2013; 85(11). · 0.82 Impact Factor
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    ABSTRACT: Brain derived neurotrophic factor (BDNF) is a member of neurotrophic family of growth factors, mainly found in the hippocampus and cerebral cortex of brain. Studies have shown that there is a link between BDNF and cognitive dysfunction, as well as there is a relationship between the PUFAs intake and their effect on BDNF production. Intake of PUFAs, mainly omega-3 and omega-6 has show increase in production of BDNF in brain. In our study we performed docking studies on PUFAs and their metabolites with BDNF using MVD (Molegro Virtual Docker), this has shown that the metabolites of the PUFAs mainly LXA_4, NPD1, HDHA have shown more binding affinity towards BDNF. These metabolites of PUFAs are responsible for modulation of BDNF activity.
    Bioinformation 11/2013; 9(18):908-911. · 0.50 Impact Factor
  • Allam Appa Rao
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    ABSTRACT: Type 2 diabetes mellitus (T2DM) is a known cause of cognitive dysfunction and involves increased risk of dementia. Brain-derived neurotrophic factor (BDNF) is a member of neurotrophic family of nerve growth factors, a key protein in promoting memory, growth and survival of neurons. BDNF is recognized as a metabotrophic factor, a molecule that is involved in Alzheimer’s disease (AD) as well as in other neurological disorders. It provides cellular and local regulatory mechanisms for mediating synaptic plasticity. Impaired BDNF signaling can compromise many aspects of brain functions. Studies investigating the relationship between diabetes and BDNF in adults demonstrate that BDNF levels are decreased in T2DM and are regulated in response to plasma levels of glucose. BDNF could serve as biomarker in predicting the development of obesity and T2DM. Thirty-two cavities were predicted to locate the active sites of BDNF for the ligands to bind. The shape of the site was identified by extracting the cavity volume surfaces enclosing regions with highest probability. Different ligands can be chosen for interaction of active sites of BDNF and can be targeted for drug discovery. This review focuses on computational exploitation selectively to deliver BDNF as a drug to appropriate hypothalamic neurons, which can serve as a novel approach in diabetic encephalopathy treatment.
    Bioinformation 06/2013; 9(11):551-4. · 0.50 Impact Factor
    This article is viewable in ResearchGate's enriched format
  • International Journal of Computer Applications 06/2013; 72(5):13-18. · 0.82 Impact Factor
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    ABSTRACT: A method is described for the analysis of the results obtained from the docking studies applied on a protein target and small molecules chemical compounds as ligands from various sources using different docking tools. We show the use of Dempster Shafer Theory (DST) to select the high ranking top compounds for further analysis and consideration. AVAILABILITY: Application is freely available at http://allamapparao.org/dst/
    Bioinformation 02/2013; 9(4):207-9. · 0.50 Impact Factor
  • M. Naresh Babu, R. Bhramaramba, Allam Appa Rao
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    ABSTRACT: The urokinase plasminogen activator receptor (uPAR) is a glycosylphosphatidylinositol (GPI) membrane-anchored receptor that binds the serine protease urokinase plasminogen activator (uPA). That uPAR plays an important role in determining malignancy of most human tumours based on a large number of experimental studies of both human cancers. A set of 5 inhibitors were taken for docking studies with 1OWD structure. These 5 small molecules are tested in wet lab for their activity. Docking analysis on a set of urokinase plasminogen activator inhibitors resulted in excellent correlation with experimental values. That is active molecule is identified as active and similarly with moderate actives and inactive.
    Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology; 10/2012
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    Naresh Babu Muppalaneni, Allam Appa Rao
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    ABSTRACT: The role of the aldose reductase in type 2 diabetes is widely described. Therefore, it is of interest to identify plant derived compounds to inhibit its activity. We studied the protein-ligand interaction of 267 compounds from different parts of seven plants (Allium sativum, Coriandrum sativum, Dacus carota, Murrayyakoneigii, Eucalyptus, Calendula officinalis and Lycopersicon esculentum) with aldose reductase as the target protein. Molecular docking and re-scoring of top ten compounds (using GOLD, AutoDock Vina, eHiTS, PatchDock and MEDock) followed by rank-sum technique identified compound allium38 with high binding affinity for aldose reductase.
    Bioinformation 10/2012; 8(20):980-3. · 0.50 Impact Factor
  • International Journal of Computer Applications 10/2012; 55(9):42-45. · 0.82 Impact Factor
  • International Journal of Computer Applications 10/2012; 56(8):31-34. · 0.82 Impact Factor
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    ABSTRACT: Diabetes, Obesity and Neurological disturbances, most often show co-occurrence. There has been an extensive research in this domain, but the exact mechanism underlying the co-occurrence of the three conditions is still an enigma. The current paper is an approach to establish the role of Butyryl cholinesterase (BCHE) in Diabetes, Obesity and Neurological disorders by performing a comparative analysis with Neuroligin (NLGN2) a protein belonging to the same family. BCHE has its role in glucose regulation, Lipid metabolism and nerve signaling. Emphasis is laid on BCHE's diverse functions whose impediment affects the above mentioned metabolic pathways. Insilco techniques were employed to analyze the sequence, structural and functional similarities of the two proteins. A point mutation is focused which is common to both BCHE and Neuroligin. The mutation occurs at the homologous position in both the proteins making them deficient. This affects the three metabolic pathways leading to the respective disorders. The work describes the pathway that describes the role of BCHE in the onset of obesity mediated diabetes. The pathway further explains the association between Diabetes, Obesity and neurological disturbances.
    Bioinformation 03/2012; 8(6):276-80. · 0.50 Impact Factor
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    ABSTRACT: Type 1 diabetes mellitus was formally known as IDDM, type I, or juvenile onset diabetes. Type 1 DM can occur at any age. In this study,we analyzed the involvement of HOX domain of PDX-1 protein.The homeodomain transcription factor, pancreas duodenum homeobox (PDX)-1, encoded by PDX-1 gene, which is a transcriptional activator of several genes, including insulin, somatostatin, glucokinase, islet amyloid polypeptide, and glucose transporter type 2 and essential for pancreas development, insulin production, and glucose homeostasis.[1,13]. HOX domain has a length of 63aa and control developmental patterns and cell differentiation in vertebrates by acting positive or negative regulators[4,9,16]. Different approached had been applied to identify the mutational hot spot region of HOX domain and calculate mutational frequency of the amino acids which resides in the hotspot region. Binding site of the domain had been identified and found that THR208, GLN246 ,VAL247, ASN253 involved in interaction with ligand. Potential Inhibitors had been screened on the basis of various criteria and bioactivity score had been calculated. Energy optimization was done by applying AMBER force field and steepest descent method. Docking was performed by CCDC GOLD, Molegro, HEX, and Argus lab to find the best potent inhibitor and increase the accuracy of the docking process. Sitagliptin showed satisfactory result on both docking and bioactivity analysis. It showed a GOLD fitness score of 49.8386 and had a moldock score of -122.919 with a ligand efficiency -4.33692. Compound had a bioactivity score of 0.56 for protease inhibitor. Sitagliptin showed good binding affinity to the target, which helps to work the pancreas in proper way and to secret insulin.
    International Journal of Computer Science Issues. 03/2012; 9(2-1694-0814).
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    ABSTRACT: Selection of initial seeds greatly affects the quality of the clusters and in k-means type algorithms. Most of the seed selection methods result different results in different independent runs. We propose a single, optimal, outlier insensitive seed selection algorithm for k-means type algorithms as extension to k-means++. The experimental results on synthetic, real and on microarray data sets demonstrated that effectiveness of the new algorithm in producing the clustering results
    02/2012;
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    ABSTRACT: Determining optimal number of clusters in a dataset is a challenging task. Though some methods are available, there is no algorithm that produces unique clustering solution. The paper proposes an Automatic Merging for Single Optimal Solution (AMSOS) which aims to generate unique and nearly optimal clusters for the given datasets automatically. The AMSOS is iteratively merges the closest clusters automatically by validating with cluster validity measure to find single and nearly optimal clusters for the given data set. Experiments on both synthetic and real data have proved that the proposed algorithm finds single and nearly optimal clustering structure in terms of number of clusters, compactness and separation.
    02/2012;
  • Naresh Babu Muppalaneni, Allam Appa Rao
    International Journal of Computer Applications. 10/2011; 31(2):14-17.
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    ABSTRACT: This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Merging for Optimal Clusters (AMOC) which aims to generate nearly optimal clusters for the given datasets automatically. The AMOC is an extension to standard k-means with a two phase iterative procedure combining certain validation techniques in order to find optimal clusters with automation of merging of clusters. Experiments on both synthetic and real data have proved that the proposed algorithm finds nearly optimal clustering structures in terms of number of clusters, compactness and separation.
    International Journal of Computer Science, Engineering and Applications. 09/2011; 1(4).
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    Naresh Babu Muppalaneni, Allam Appa Rao
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    ABSTRACT: Protein Data Bank (PDB) file contains atomic data for protein and ligand in protein-ligand complexes. Structure data file (SDF) contains data for atoms, bonds, connectivity and coordinates of molecule for ligands. We describe PDBToSDF as a tool to separate the ligand data from pdb file for the calculation of ligand properties like molecular weight, number of hydrogen bond acceptors, hydrogen bond receptors easily.
    Bioinformation 08/2011; 6(10):383-6. · 0.50 Impact Factor
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    Srinivasa Rao Peri, Allam Appa Rao, K. Srinivas
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    ABSTRACT: Both environmental and genetic factors have roles in the development of any disease. The quest for an understanding of how genetic factors contribute to human disease is gathering speed. Differential gene expression analysis plays an important role for the study of genetic factors causing diseases. We proposed a method for identifying differentially expressed genes causing Type-2 diabetes mellitus using microarray data for diabetes with parental history and healthy. This method focuses on identifying multivariate and univariate outliers using Mahalanobis Distance, Minimum Co-variance Determinant (MCD) and other statistical methods. For the identified inflammatory genes we performed the functional classification by using Gene Ontology and identified the pathways between these inflammatory genes using pathway analysis. This method is applied on microarray data from two samples one from diabetes with parental history and the other from healthy and identified 1579 genes which are differentially expressed and functional classification was preformed to these genes. Prior to analysis, the microarray data is normalized using Lowess Normalization method.
    International Journal of Engineering and Technology. 01/2011; 3(3).

Publication Stats

124 Citations
32.21 Total Impact Points

Institutions

  • 2013
    • University of Hyderabad
      Bhaganagar, Andhra Pradesh, India
  • 2010
    • Jawaharlal Nehru Technological University, Kakinada
      Cocanada, Andhra Pradesh, India
  • 2009
    • Institute for Systems Biology
      Seattle, Washington, United States
  • 2007–2008
    • Andhra University
      • Department of Computer Science and Systems Engineering
      Vizag, Andhra Pradesh, India
  • 2006
    • Anil Neerukonda Institute of Technology and Sciences
      Vizag, Andhra Pradesh, India