Allam Appa Rao

University of Hyderabad, Bhaganagar, Andhra Pradesh, India

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Publications (86)28.62 Total impact

  • M. N.VamsiThalatam · Allam Appa Rao ·

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  • Ramachandra Rao Kurada · K. Karteeka Pavan · Allam Appa Rao ·
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    ABSTRACT: Multi-objective optimization emerged as a significant research area in engineering studies because most of the real-world problems require optimization with a group of objectives. The most recently developed meta-heuristics called the teaching–learning-based optimization (TLBO) and its variant algorithms belongs to this category. This paper provokes the importance of hybrid methodology by illuminating this meta-heuristic over microarray datasets to attain functional enrichments of genes in the biological process. This paper persuades a novel automatic clustering algorithm (AutoTLBO) with a credible prospect by coalescing automatic assignment of k value in partitioned clustering algorithms and cluster validations into TLBO. The objectives of the algorithm were thoroughly tested over microarray datasets. The investigation results that endorse AutoTLBO were impeccable in obtaining optimal number of clusters, co-expressed cluster profiles, and gene patterns. The work was further extended by inputting the AutoTLBO algorithm outcomes into benchmarked bioinformatics tools to attain optimal gene functional enrichment scores. The concessions from these tools indicate excellent implications and significant results, justifying that the outcomes of AutoTLBO were incredible. Thus, both these rendezvous investigations give a lasting impression that AutoTLBO arises as an impending colonizer in this hybrid approach.
    Computational Intelligence Techniques for Comparative Genomics, 01/2015: pages 17-35;
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    ABSTRACT: Knowledge discovery refers to identifying hidden and valid patterns in data and it can be used to build knowledge inference systems. Decision tree is one such successful technique for supervised learning and extracting knowledge or rules. This paper aims at developing a decision tree model to predict the occurrence of diabetes disease. Traditional decision tree algorithms have a problem with crisp boundaries. Much better decision rules can be identified from these clinical data sets with the use of the fuzzy decision boundaries. The key step in the construction of a decision tree is the identification of split points and in this work best split points are identified using the Gini index. Authors propose a method to minimize the calculation of Gini indices by identifying false split points and used the Gaussian fuzzy function because the clinical data sets are not crisp. As the efficiency of the decision tree depends on many factors such as number of nodes and the length of the tree, pruning of decision tree plays a key role. The modified Gini index-Gaussian fuzzy decision tree algorithm is proposed and is tested with Pima Indian Diabetes (PID) clinical data set for accuracy. This algorithm outperforms other decision tree algorithms.
    Computers & Electrical Engineering 07/2014; 40(5):1758–1765. DOI:10.1016/j.compeleceng.2013.07.003 · 0.82 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. DOI:10.6026/97320630010028 · 0.50 Impact Factor
  • M. N. VamsiThalatam · Allam Appa Rao ·

    International Journal of Computer Applications 12/2013; 85(11). DOI:10.5120/14884-3318
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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. DOI:10.6026/97320630009908 · 0.50 Impact Factor
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    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. DOI:10.6026/97320630009551 · 0.50 Impact Factor
  • N. GopalaKrishnaMurthy · O. Naga Raju · Allam Appa Rao ·

    International Journal of Computer Applications 06/2013; 72(5):13-18. DOI:10.5120/12489-8329
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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
    Bioinformation 02/2013; 9(4):207-9. DOI:10.6026/97320630009207 · 0.50 Impact Factor
  • Andey Krishnaji · Allam Appa Rao ·
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    ABSTRACT: The hybridization and application of computational intelligence techniques such as artificial neural networks, fuzzy logic, and genetic algorithms has become hotspot research area in the recent times. This paper presents a work which investigates the benefits of combining genetic algorithms, fuzzy logic and artificial neural networks into a hybrid Neuro Fuzzy Genetic System, especially for the prediction of protein secondary structure. The proposed Neuro Fuzzy Genetic System will assign a secondary structure type for each residue in the target protein sequence by way of including more biological information such as protein structural class, solvent accessibility, and physicochemical properties of residues in order to improve accuracy of protein secondary structure prediction. The proposed system will experiment on three-class secondary structure prediction of proteins, that is, alpha helix, beta sheet or coil. The experimental results indicate that the proposed method has the advantages of high precision, good generalization, and comprehensibility. The method also exhibits the property of rapid convergence in fuzzy rule generation.
    Computing and Communication Systems (NCCCS), 2012 National Conference on; 11/2012
  • 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. DOI:10.6026/97320630008980 · 0.50 Impact Factor
  • Dharmaiah Devarapalli · Allam Appa Rao · G. R Sridhar ·
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    ABSTRACT: The BDNF\TrkB signaling system which plays a major role in the regulation of neuronal activity. This neuronal regulation influences the potential role of this system in the therapeutic efficacy of many neurological and psychiatric disorders. Despite these roles decreased levels of BDNF are associated with diabetes and obesity which can be regulated by increasing the insulin sensitivity and glucose tolerance. BDNF mediated signaling through phosphoinositide 3-kinase (PI3K) pathway plays a key role in insulin sensitivity. These beneficial effects of BDNF as antidiabetic agents can be enhanced by the activating BDNF\TrkB signaling. These multiple functionality of BDNF\TrkB complex relies on the protein-protein interactions where those interactions are important in designing pharmacological targets. This apporach focus the use of BDNF peptides in place of BDNF protein in binding to the TrkB receptor. Protein–peptide docking studies were performed to know the interactions of the TrkB and BDNF peptides. Docking studies were done using ZDOCK pro (Accerlys Discovery studio). The interacting amino acids residues identified at the binding site
    International Journal of Computer Applications 10/2012; 55(9):42-45. DOI:10.5120/8786-2759
  • Phani KumarYadla · Allam Appa Rao · A. Swaroopa Rani · M. Naresh Babu ·

    International Journal of Computer Applications 10/2012; 56(8):31-34. DOI:10.5120/8912-2959
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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. DOI:10.6026/97320630008276 · 0.50 Impact Factor
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    Allam Appa Rao · Anupam Bhattacharya · Amit Kumar · Amita Kashyap · GR Sridhar ·
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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.
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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
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    K. Karteeka Pavan · Allam Appa Rao · A. V. Dattatreya Rao ·
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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; 2(4).

Publication Stats

199 Citations
28.62 Total Impact Points


  • 2013
    • University of Hyderabad
      • "C R Rao"Advanced Institute of Mathematics, Statistics and Computer Science (AIMSCS)
      Bhaganagar, Andhra Pradesh, India
  • 2009-2013
    • Jawaharlal Nehru Technological University, Kakinada
      Cocanada, Andhra Pradesh, India
  • 2012
    • C.R.Rao Advanced Institute Of Mathematics, Statistics And Computer Science
      Bhaganagar, Andhra Pradesh, India
  • 2007-2010
    • Andhra University
      • • College of Engineering
      • • Department of Computer Science and Systems Engineering
      Vizag, Andhra Pradesh, India
  • 2006
    • Anil Neerukonda Institute of Technology and Sciences
      Vizag, Andhra Pradesh, India