[Show abstract][Hide abstract]ABSTRACT: Lung Cancer has been an issue of concern these days as there is an alarming toll of rising deaths every year. A good amount of research is pursued on aspects of the genetic and hereditary and also computational methods to detect Lung cancer. Even though there is a lack of awareness about this disease due to a colossal gap between technical and clinical research areas. Accordingly this research paper presents a comprehensive study on Lung Cancer detection in terms of simulation of medical images and clinical analysis wherein one of the KRAS mutations has been analysed in lung cancer patients and their lung images have been used for developing medical images with better tumour spot detection. The computational technique used for simulation purpose involves morphological image processing methods, which mainly work on the topological and shape content of the images acquired.
Full-text available · Article · Dec 2016 · Procedia Computer Science
[Show abstract][Hide abstract]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.
[Show abstract][Hide abstract]ABSTRACT: Brain Derived neurotrophic factor (BDNF) is very well reported in development of neurons and it plays major role in memory and interpretation. It is evident that BDNF is involved in maintaining the equilibrium of body weight and glucose homeostasis mechanism. Out of 96 Subjects included in this study Plasma BDNF level was found low in the patients with Type 2 Diabetes. We didn’t find positive indications wrt association of BDNF G196A (Val66Met) polymorphism in diabetes or obesity. Type 2 Diabetic patients with the complaint of Joint pains were found to have even lower Plasma BDNF levels as compared to the diabetic patients without any Neurological problems or joint pains. Worsening BDNF Levels may be an alarming factor for type 2 diabetic patients with respect to development of Neurological disorders.
[Show abstract][Hide abstract]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.
[Show abstract][Hide abstract]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.
[Show abstract][Hide abstract]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.
[Show abstract][Hide abstract]ABSTRACT: This paper proposes few improvements to “Hybrid Neuro Fuzzy Genetic System (HNFGS)”, which we have implemented and presented in  for protein secondary structure prediction. The hybridization of artificial neural networks, genetic algorithms, and fuzzy logic can produce robust solutions for complex prediction problems, such as protein secondary structure prediction. Due to the complex and dynamic nature of biological data, we have proposed a two step process to model protein secondary structure prediction. As the protein secondary structure prediction problem involves a huge number of inputs, the input variables of I-HNGS are selected carefully in the first phase. In the second phase genetic algorithms are used to optimize fuzzy set definition and the shape and type of fuzzy membership functions. The improved HNFGS has produced better prediction results when experimented on three-class (alpha-helix, beta-sheet or coil) protein secondary structure prediction from amino acid sequence. The experimental results indicate that the proposed system has the advantages of high precision, good generalization, and comprehensibility. This system also exhibits the property of rapid convergence in fuzzy rule generation.
[Show abstract][Hide abstract]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.
[Show abstract][Hide abstract]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.
Application is freely available at http://allamapparao.org/dst/
[Show abstract][Hide abstract]ABSTRACT: Protein secondary structure prediction is an essential step for the understanding of both the mechanisms of folding and the biological function of proteins. Experimental evidences show that the native conformation of a protein is coded within its primary structure. This work investigates the benefits of combining genetic algorithms, fuzzy logic, and neural networks into a hybrid Evolutionary Neuro-Fuzzy System, especially for predicting a protein’s secondary structure directly from its primary structure. The proposed system will include more biological information such as protein structural class, solvent accessibility, hydrophobicity and physicochemical properties of amino acid residues 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.
[Show abstract][Hide abstract]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.
[Show abstract][Hide abstract]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.