International Journal of Bioinformatics Research and Applications (37 papers in press)
In-silico approach for the detection of key genes and their interaction involved in breast cancer cell lines
by Desam Neeharika, Swetha Sunkar
Abstract: Genes are known to play a pivotal role in Breast cancer. Any factor that that leads to a change in the expression level of the genes influences the mechanism and disturbs the functionality. Hence, our study aims to identify the key genes in the MCF-7 breast cancer cell line and their interaction with other genes using in silico approach. The microarray dataset GSE1400 was selected from GEO database and GEO2R tool was used and identified a total of 1932 Differentially Expressed Genes (DEGs), of which 1809 and 123 genes were up-regulated and down-regulated respectively. DAVID tool was used for functional annotations. Cytoscape was used for the screening of clusters and identification of hub genes. Among them, EGFR and HNRNPR were identified to have highest degree nodes from up-regulated and down-regulated gene networks respectively and hence can be used as possible molecular target.
Keywords: Breast cancer; Differentially Expressed Genes; GEO; in-silico analysis; Gene Ontology; Hub genes.
LIFT: LncRNA Identification and Function-prediction Tool
by Sumukh Deshpande, James Shuttleworth, Jianhua Yang, Sandy Taramonli, Matthew England
Abstract: Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a significant role in several biological processes. Accurate identification and sub-classification of lncRNAs is crucial for exploring their characteristic functions in the genome as most coding potential computation (CPC) tools fail to accurately identify, classify and predict their biological functions in plant species. In this study, a novel computational framework called LIFT has been developed, which implements LASSO optimization and iterative Random Forests classification for selection of optimal features, a novel Position-Based Classification (PBC) method for sub-classifying lncRNAs into different classes and Bayesian-based function prediction approach for annotating lncRNA transcripts. Using LASSO, LIFT selected 31 optimal features and achieved 15-30% improvement in the prediction accuracy on plant species when evaluated against state-of-the-art CPC tools. Using PBC, LIFT successfully identified the intergenic and antisense transcripts with greater accuracy in A. thaliana and Z. mays datasets. The predicted functions were verified with published experimental results.
Keywords: lncRNA; LASSO; iterative Random Forests; Position-Based Classification; BMRF; function prediction.
Predicting Novel Interactions from HIV-1-Human PPI Data Integrated with Protein Signatures and GO Annotations
by Debasmita Pal, Kartick Chandra Mondal
Abstract: The research on host-pathogen protein-protein interactions (PPIs) becomes one of the most challenging areas of medical science for antiviral drug invention. Specifically, the virologists are now more focused on the pathogenesis of Human Immunodeficiency Virus - Type 1 (HIV-1) due to its virulent nature and epidemic spread throughout the world. The virus exploits a complex interaction network of HIV-1 and human cellular proteins to replicate and gradually destroys the human immunity power causing Acquired Immunodeficiency Syndrome (AIDS). In this paper, we propose a pattern mining based approach to predict novel interactions between HIV-1 and human proteins with an estimated confidence based on the experimentally validated known interactions curated in public PPI database. While predicting interactions, we also utilize the information on protein signatures and Gene Ontology (GO) annotations (Biological Process, Cellular Component and Molecular Function annotations) of human proteins. The integration of these additional information with PPI dataset results in predicting more potential interactions and also provides the signatures and GO annotations which might hold for the predicted interactions. We validate our predicted interactions by finding evidences from the literature and comparing with the predictions made by different computational approaches. We believe that our predicted information on PPIs along with the corresponding signatures and GO terms enlightens the PPI research field with greater knowledge and better understanding of viral replication process; subsequently enhancing the discovery of new drug targets.
Keywords: HIV-1 Proteins; Antiretroviral Drugs; Protein Signatures; GO Annotations; Protein-Protein Interactions; Association Rule Mining.
Identification of novel and available Bacillus anthracis inhibitors using drug repurposing approach and In-Silico methods
by Masood Aleeyar, Hamid Moghimi, Abbas Rouhollahi, Ramezan Ali Taheri
Abstract: Anthrax is a dangerous disease all around the world and it can be lethal if it is not properly treated. In order to address this problem, we decided to use the combination of drug repurposing and structure-based drug discovery to identify useful and effective alternative treatment for anthrax. In this study, we used molecular docking, molecular dynamics simulations and related analysis and found that Kanamycin, Tobramycin and Framycetin can be potential drugs for the treatment of Anthrax. Moreover, because our identified compounds are FDA approved drugs, there is no need to pass any safety test and all of the hit compounds can be tested in human subjects.
Keywords: Bacillus anthracis; anthrax; nucleoside hydrolase; molecular docking; molecular dynamic; In-Silico drug repurposing.
Clustering Analysis of Soil Microbial Community on a Global Scale
by Tetsushi Tanaka, Andre Freire Cruz, Naoaki Ono, Shigehiko Kanaya
Abstract: There are huge numbers of bacteria in the soil, and their existence and function a?ect soil properties. Bacteria form a complex community called the microbiome. Compared to the ecosystem of plants and animals, we still know little about the soils microbial ecosystem. How soil microbiomes are di?erent throughout the world and how they relate to the region and the environment is of major interest. Recently, the development of next-generation sequencing has enabled researchers to accumulate metagenome profile data, and large-scale and comprehensive microbiome analyses are required by the scientific approach. In this study, we compared and analyzed the data from various environments on a global scale using the microbiome database, the Earth Microbiome Project (EMP). We calculated the distance based on genetic distance, named the UniFrac distance, and did clustering analysis. Clustering results were ecologically interpreted from the view of the function and the characteristics of bacteria. We revealed the characteristics of groups of bacteria related to paddy, vineyard, grasslands in Mongolian, forests, and biofilter. Furthermore, we investigated the relationship between clusters and climate zones. This research is expected to lead to knowledge of soil management based on the soil microbiome.
Keywords: Clustering Analysis; Metagenome; Microbiome; Soil Microbiology; UniFrac Distance.
Homology modeling and docking studies of strictosidine beta-D-glucosidase from Madagascar periwinkle (Catharanthus roseus Bunge)
by Piotr Szymczyk, Grazyna Szymanska, Malgorzata Majewska, Izabela Weremczuk-Jezyna, Michal Kolodziejczyk, Kamila Czarnecka, Pawel Szymanski, Ewa Kochan
Abstract: Here, the structure of the enzyme and substrate binding site of C. roseus strictosidine beta-D-glucosidase is reported. An homology model of C. roseus strictosidine beta-D-glucosidase was built using the Discovery Studio 4.1 software package and rice Os3bglu6 beta-glucosidase (PDB code: 3GNPA) as a template. The CDOCKER algorithm was used to dock the natural substrate, strictosidine, as well as an inhibitor D-glucono 1,5-lactone. Obtained structures were refined in the course of molecular dynamic simulation. Analysis of the acquired data provided information on the C. roseus strictosidine beta-D-glucosidase amino acid residues interaction with the natural substrate and inhibitor. Our findings expand the basic knowledge of the structure of the C. roseus strictosidine beta-D-glucosidase active site, and may also be used in the design of enzyme point mutations with improved stability and catalytic properties, or to change substrate specificity.
Keywords: homology modeling; ligand docking; enzyme-ligand interactions; alanine scanning.
A Framework for Neighborhood Configuration to Improve the KNN based Imputation Algorithms on Microarray Gene Expression Data
by Shilpi Bose, Chandra Das, Kuntal Ghosh, Matangini Chattopadhyay, Samiran Chattopadhyay
Abstract: In view of the several technical problems associated with microarray experiments, a considerable amount of entries are found missing in a typical microarray gene expression dataset. As a consequence, due to the unavailability of complete data, the effectiveness of the analysis algorithms deteriorates. Different imputation techniques are employed to address this problem among which, the weighted average based methods are widely used in several applications. These methods generate consistent results and are algorithmically simple, but they also suffer from some drawbacks that are seldom elaborated upon. These deficiencies have been pointed out in this work, and a new approach has been suggested to overcome those. The proposed framework is embedded in the K-nearest neighbor imputation method (KNNimpute), as well as its different versions. The idea is to achieve better neighborhood formation, in order to improve the prediction accuracies. It is based on a hybrid distance and gene transformation procedure which utilizes simultaneously the advantages of Euclidean distance, Mean squared residue score, and Pearson correlation coefficient to select the best possible neighbors, using pattern-based similarity. The framework is tested on ten well-known microarray datasets. From the experimental results it has been found that in each and every case, the proposed modified methods significantly outperform their corresponding traditional versions and are also comparable with the existing robust numerical methods.
Keywords: Missing value prediction; Microarray technology; Gene expression data; K-nearest neighbors; Pearson correlation coefficient; Mean Square Residue; Euclidean distance.
Multi-Descriptor Approaches to Oxygen Binding Proteins Prediction and Classification using Deep Learning
by Soumiya Hamena
Abstract: Oxygen binding proteins play a key role in the transport and storage of oxygen through the bodys cells, and identifying oxygen binding proteins is highly desirable for predicting functional annotation of these proteins. However, costly and time consuming biological tests can only determine a very small portion of all proteins sequences available currently in the databases. This has made computational approaches increasingly essential to help biologists predict and classify proteins function effectively. The key idea behind this work is to investigate the effect of using several descriptors and deep learning to achieve better prediction. To the best of our knowledge, no study on the subject has been undertaken. Three kinds of descriptors to represent protein sequences are considered in this study namely amino acid composition (AAC), dipeptide composition (DC) and conjoint triad feature (CTF). To carry out the classification process, Deep Neural Networks (DNN) has been designed. Firstly, we applied DNN to single descriptor prediction of oxygen binding proteins, that is using AAC, DC, CTF descriptors separately and then we applied DNN in a multi-descriptor prediction context by investigation of the integration of AAC-DC, AAC-CTF, DC-CTF and AAC-DC-CTF descriptors combinations. Secondly, DNN was developed to classify the oxygen binding proteins into six different classes which are: Erythrocruorin, Myoglobin, Hemerythrin, Hemocyanin, Hemoglobin and Leghemoglobin. The proposed approach can be viewed as a two stages process by applying first a binary classification then a multiclass classification. The experimental results show that the proposed models outperform the existing methods where we obtained a Matthews Correlation Coefficient (MCC) of 0.9677 for identifying oxygen binding proteins with the combination of AAC and DC. For classifying oxygen binding proteins, we achieved MCC of 0.4539, 0.9054, 0.9830, 0.9434, 0.8813 and 0.9441 for the above six classes, respectively.
Keywords: Oxygen binding proteins; Proteins function; AAC; DC; CTF; Deep Neural Networks; Deep Learning; Data Mining.
Special Issue on: CBM 2018 Biomedicine, Machine Learning and Big Data
ExBWS: Extended Bioinformatics Web Services for Sequence Analyses
by Robert Penchovsky
Abstract: The Extended Bioinformatics Web Services (ExBWS) represent a significant extension of the published EBWS PHP-based server providing useful tools for analyses of DNA, RNA, and protein sequences. Six new Web-based applets are freely available via the ExBWS to the user. They include a DNA/RNA translator, an AminoCODE transformer, a virtual PCR analyzer, a protein hydropathy plotter, protein reverse translator, and a Eukaryotic ORF finder. Each applet includes some novel feature. The AminoCODE transformer takes a protein sequence from one letter code to three and vice versa, the virtual PCR analyzer generates fragments with or without overhangs, the protein hydropathy plotter makes hydropathy plots of 10 frames of the input sequence, the reverse translator converts proteins to DNA according to the highest codons bias present in the selected organism. The Eukaryotic ORF finder searches for introns in the query sequence and translates ORFs of the processed sequence into proteins. All programs are freely available at http://penchovsky.atwebpages.com/applications.php.
Keywords: Bioinformatics web server; nucleic acids; and protein sequence analyses; virtual PCR; hydropathy plot; Reverse translator; Protein sequence conversion; eukaryotic ORF finder.
Prediction of Alzheimer Associated Proteins (PAAP): A perspective to understand Alzheimer Disease for Therapeutic Design
by Gaurav Gupta, Neha Gupta, Ankit Gupta, Pankaj Vaidya, Girish Kumar Singh, Varun Jaiswal
Abstract: Alzheimer disease is complex progressive neurodegenerative disease with no cure and its occurrence rate increased worldwide with increase of human life span. It became the sixth killer in US and no vaccines is available for the disease. Its unclear aetiology is the major hurdle in therapeutics discovery against it. Discovery of proteins/genes associated with AD can decipher the disease aetiology and further discovery of vaccine and drug targets. Analysing association of genes/proteins with AD required resources and expertise which have practical limitation. Computational methods can be used to predict the association of all possible genes/protein with AD. In current research data of all known proteins/genes associated with AD was used to develop machine learning based method for the prediction of proteins for their association with AD. High accuracy of the developed model warrants the reliability of the method. The developed method is expected to help in understanding of AD and discovery of new vaccine and drug target candidates for AD. The developed method is implemented on webserver and available at (http://188.8.131.52/cgi-enabled/index.html).
Keywords: Alzheimer Disease; Machine Learning; Protr; PAAP; SVM.
A Review of Dimensionality Reduction Methods Applied on Clinical Data of Diabetic Neuropathy Complaints
by Usharani R
Abstract: The Dimensionality Reduction technique on a large clinical data of Type II diabetes patients in identifying the causes and symptoms that they tend to develop neuropathic complaints. Considering the techniques using Machine Learning and Data Mining are ineffective for big data, it becomes necessary to use the most important phase called data preprocessing. An effective approach to downsizing the data to be considered for analysis is dimensionality reduction. The reduction of dimensions technically is to reduce the number of independent / dependent variables which are necessary for our analysis. These processes help in identifying the features to be considered for selection and extraction of data avoiding the redundant and irrelevant features. Such a selection chooses an optimal subset of the features originally defined for a pre conceived objective and the extraction is used to build a new set of features which are a linear combination of the original features. Dimensionality Reduction is done by applying the supervised and unsupervised learning one can conclude on the prediction and further analysis. The primary focus of this paper is on Supervised Learning where the variables are known beforehand and a combination of Feature Selection Techniques and Machine Learning Algorithms used with the goal of providing the practitioners with the probability of methods for using Dimensionality Reduction mechanism for feature extraction
Keywords: Type II Diabetes Mellitus; Diabetes & Neuropathic complaints; Dimensionality Reduction; Machine Learning Algorithms; Feature Selection; Feature Extraction;.
A System for Continuous Monitoring of Food Intake in Patients with Dysphagia
by Ingridy Barbalho, Patrício Silva, Cynthia Maia, Cicilia Leite
Abstract: The use of mobile devices for continuous monitoring of patients with a specific pathology can significantly help in their recovery. In this perspective, this work presents the development of an mHealth monitoring system to follow-up of patients with difficulty in swallowing and/or Oropharyngeal Dysphagia. The developed system aims to capture the movements and the acoustic signals generated during the process of chewing and swallowing, and classify them in solid, liquid or pasty material. After classified, this information is stored and generated a food record with detailed information about the meals made, relating the level of dysphagia in the patient, based on this information. To this end, it was implemented a domain ontology for data classification. Finally, the system was validated through experiments in the real environment, showing relevant results and providing quality of life to patients who need remote monitoring.
Keywords: Artificial Intelligence; Difficulty in Swallowing; Food Intake; Ontologies; Food History; Remote Monitoring; Patient monitoring; Health Technology.
AUTISM DETECTION USING MACHINE LEARNING
by U.B. Mahadevaswamy, Rohan Ravikumar, Rachana Mahadev, Kadaparthi Varun Rao, Anurag K S
Abstract: Autism is one of the most eminent bio-neurological developmental disorders characterized by disablement in social-communication and behavioral aspects like repetitive behaviors and restrictive interests. Present methods of autism detection are based on behavioral analysis of the subject. These methods are dependent on the skills of the examiner and are laborious. This paper proposes a method for autism detection by virtue of structural MRI of brain obtained from the ABIDE-2 dataset and well-trained machine learning models. The prediction was done based on supervised learning methods namely Random Forest and Logistic Regression. These methods yielded accuracy of 0.82 and 0.80 respectively which are comparatively better than previous works. VBM analysis was conducted on the same dataset with SPM8 in order to obtain the difference in brain structure of typical controls and autism affected subjects and a resultant glass brain image was obtained.
Keywords: Autism; ABIDE-2; s-MRI; Voxel Based Morphometry Analysis; Random Forest; Logistic Regression.
Biclustering of Diabetic Nephropathy and Diabetic Retinopathy Microarray Data Using a Similarity-Based Biclustering Algorithm
by Titin Siswantining, Alhadi Bustamam, Fahrezal Zubedi, Sofia Debi Puspa, Zuherman Rustam
Abstract: In this study, we implemented a modified similarity-based biclustering (SBB) algorithm to identify a significant bicluster in diabetic nephropathy and retinopathy microarray data. Theoretically, SBB consists of four main phases, transforming data, the construction of row (gene) and column (condition) similarity matrices, the clustering of each similarity matrix and the extraction of the bicluster. Before implementing the SBB method, genes are selected using relative deviations and absolute deviations. We modified the SBB algorithm at the stage of data transformation using minmax normalization and compared the partitioning methods using medoids, k-means clustering and agglomerative hierarchical clustering (Wards linkage). Based on silhouette index validation experiments, SBB using PAM provided better clustering of genes and samples than K-means and AHC (Wards linkage). Furthermore, the proposed technique identified a meaningful non-overlapping bicluster on a real dataset. Using gene ontology (GO) enrichment analysis and the Bonferroni correction provided by the Database for Annotation, Visualization and Integrated Discovery, we have identified biological evidence in each bicluster that is significant in terms of gene functions and biological processes.
Keywords: biclustering; gene expression; microarray data; similarity-based biclustering.
Special Issue on: CMBH 18 Advances in Bioinformatics and Biotechnology towards Medicine and Health
COMPUTATIONAL SYSTEMS BIOLOGY OF PITYRIASIS LICHENOIDES CHRONICA and ITS COMORBIDITIES
by KEZIAH SOUZA, Sravani Valligari, Pavan Kumar, Daniel Alex Anand
Abstract: Pityriasis Lichenoides Chronica (PLC) is a rare idiopathic skin disorder which is characterized by scaly, rose pink papules mainly on the trunks and limbs which may persist for months or sometimes years. The probable cause of PLC may be due to a hypersensitivity reaction caused by a viral infection. Pityriasis Lichenoides has two main variants: PLC which is the chronic form and PLEVA (Pityriasis Lichenoides et varioliformis acuta) the acute form. PLC is usually treated with immunosuppressants like methotrexate. The etiology and immunological pathways are yet unknown. In this study we have attempted to study gene expression, gene regulatory networks and comorbidities as a systems biology approach which may lead us to the identification of pathways of immunomodulation involved in PLC and its comorbidities, thereby facilitating therapeutic intervention. We identified a gene list using various datasets like Personal Genome Project (PGP), DisGeNet, Disease Connect, and Geodatasets. We also tried to understand the disease by studying its variants. Differentially Expressed Genes (DEG) using Geo2R tool gave us up and down-regulated genes. We also constructed a network between Transcription Factors and Genes. We used Panther and DAVID tool for pathway analysis. The insight derived from the study was that PLC and Psoriasis are related genotypically and phenotypically. With all these similarities there are also huge differences in molecular mechanisms and predisposing factors and so different courses have to be adopted for the management and therapy of these dermatological disorders.
Keywords: gene expression; miRNA; SNPs; Transcription factors; PLC; HLA; networks; pathways; susceptibility; computational biology.
Numerical Investigation of Human Knee Joint for understanding the influence of Anterior Cruciate Ligament on the displacement and stress
by Bharath K. Bhat, Raviraja Adhikari, Kiran Kumar V Acharya
Abstract: Purpose: This study on healthy knee is to understand the response of human knee joint during normal loading. Method: Magnetic Resonance Images (MRI) of five healthy human knee joints were considered for this study. 3-Dimensional (3D) models of these healthy human knee joints were generated. Finite element analysis was undertaken on these models after their discretization. A load of 134 N along with corresponding boundary conditions on the human knee joint. Linear elastic material properties were considered for all the parts of human knee joint in this study. All the five subjects were also analyzed considering ruptured ACL under the same load and boundary conditions as earlier. Results: Among the five subjects, it was observed that maximum Von-Mises stress was in the expected regions of Anterior Cruciate Ligament with the mean value of stress being 13.934 MPa with a standard deviation of 5.1 MPa for ACL. Conclusion: The higher displacement and reduced stress observed in case of knee joint with ruptured ACL when compared with that of healthy knee joint indicate that the joint becomes more flexible in the absence of ACL.
Keywords: ACL injury; Lachman Test Simulation; Finite Element Analysis; Human Knee Joint.
A computational analysis of cancer inhibitor ortho benzoyl vanillin
by Jaynthy CHELLAM, Usha S
Abstract: The oxidative stress caused by improper dieting increases the formation of free radicals during metabolism results in the cancerous disease, threatening human life globally. The scavenging of free radicals by small organic molecules quench the thrust in search of drugs for cancer appears to be an area of new approaches towards the motive. In this aspect, the title compound ortho benzoyl vanillin (OBV) was synthesized , carried out characterisation studies, thermal study and powder XRD. The charge transfer mechanism, structure activity relationship property of OBV serves to be a potential target to fight against cancer. OBV has been analyzed for its pharmacophores and its anticancer activity using computational methods. OBV showed a good dock score and hence thereby can be further analysed by other techniques to prove its potential as an inhibitor of cancer.
Keywords: Ortho Benzoyl Vanillin (OBV); Anticancer activity; Powder XRD; Charge transfer mechanism; Pharmacophore.
Identification of Malignancy in Lung Using Artificial Neural Network
by Lalithakumari S, Pandian R
Abstract: Earlier diagnosis of cancer cell growth leads to save lots of precious human lives. It is necessary to develop some automated tool, in order to detect malignant state at the beginning stage itself. Many algorithms had been proposed earlier by many researchers in the past, but, the accuracy of prediction is always a challenging task. In this work, an artificial neural network based methodology is proposed to find the abnormal growth of lung tissues. Higher probability of detection is taken as an objective to get an automated tool, with great accuracy. Manual interpretation always leads to misdiagnosis .A full fledged Computer Tomography image set of lung of sixty five different humans, with normal and malignant health states have been considered in this work. The three different views of the CT scanning system such as axial, coronal and sagittal have also been considered in the data base creation. Distinct textural features of the images provide an intra class variation in its nature, make a neural network, feasible for classification of the normal images, identifying away from the malignant ones. Optimal feature sets derived from Haralick Gray level co occurrence Matrix and used as the dimension reduction way for feeding neural network. In this work, a binary classifier neural network has been proposed to identify the normal images out of all the images. The capability of the proposed neural network has been quantitatively computed using confusion matrix and found in terms of classification accuracy.
Keywords: GLCM; Haralick; Classification Accuracy; MSE; SSE and MSEREG.
Bio-Inspired Approaches for classification of Benign and Malignant tumor of skin
by Aman Gautam, Usha Chouhan
Abstract: In this paper, Skin diseases are malignancies that emerge from the skin. The reasons for skin malignant growth are carcinogenic cells can spread to different parts of the skin. There are three primary sorts of skin diseases: BCC, SCC and melanoma. The initial two are known as non-melanoma skin malignant growth or kindhearted (NMSC). More prominent than 90% of skin malignant growths are caused by bright beams from the Sun. This introduction expands the danger of every one of these kinds of skin malignancy. Because of a more slender ozone layer the presentation has expanded. Another normal wellspring of bright radiation Tanning beds. The motivation behind this paper is to arrange among kindhearted and dangerous tumor dependent on dermoscopic picture ISIC datasets utilizing five machine learning methods (MLP, SVM, RF, KNN, and LR). Accurately characterized cases were found as 94%, 89.50%, 82.00% and 90.00% for MLP, SVM, RF,DT and LR individually The precision is accomplished that is higher than the current methodologies.
Keywords: tumor images; Medical Decision Support System; Machine learning techniques.
Micro Electro Mechanical Systems in Nephrology
by Subhashini Radhakrishnan, Niranjani Ravi Chandran
Abstract: MEMS plays a major role in the clinical field and it is used to create new contrivance related to both biology and medicine and this contrivances saving people from mortal. MEMS has an ability to integrate several components into single device. In nephrology MEMS are used for hemodialysis. The kidney is one of the important organ in our body and it is responsible for removing wastes. If your kidney is not functioning properly then it leads to CKD (Chronic Kidney Diseases). CKD treatment can ameliorate patient from end stage of kidney failure, which is sufficient to cause mortal without artificial filtering (hemodialysis) or a kidney transplant. In artificial filtering (hemodialysis), we use MEMS technology in the part of vascular access. MEMS based pressure sensor are implanted into a plastic tube or coil and are attached to arteries and veins of your arm. Further, it is used to filter the blood of kidney failure patient. The technology helps to safeguard kidney failure patient from mortal. MEMS are easy to integrate into systems or modify. MEMS contrivances can use both mechanical and electrical components and it reduces the usage of energy. Initially,it is not fit for any huge power transformation, because of its size. The review briefs about an overview of hemodialysis and MEMS usage in nephrology
Keywords: Micro Electro Mechanical Systems; nephrology; hemodialysis; CKD; pressure sensor; IRAD; ESRD; vascular access.
Special Issue on: Data Mining and Its Applications in Bioinformatics and Biomedical Engineering
Cuckoo Search based Deterministic Scale (CSDS) for Computer Aided Heart Disease Detection
by Vankara Jayavani, Lavanya Devi D
Abstract: Predicative analysis in medical domain for computer aided disease Prediction become crucial practice in regular clinical practices. This is since, the false alarming or delay in disease detection is inversely proportionate to the clinical experience of the medical practitioner. Unlike the other domains the sensitivity that is the accuracy in disease prone is very much crucial in clinical practices. Particularly, the accuracy and sensitivity are more crucial in computer aided heart disease prediction methods. Hence, the recent research contributions are quantifying thepossibilities of optimizing machine learning approaches to achieve significance in computer aided methods to perform predictive analysis on heart disease detection. In regard to this context, this manuscript is defining a supervised learning approach by Cuckoo Search based Deterministic Scale (CSDS) to perform heart disease prediction. The experimental study indicating the significance of the proposed model in related to detection accuracy and sensitivity along with other performance metrics.
Keywords: Soft-Computing; NOPAS; CSFT; CSDS; Cuckoo Search; Dice Similarity Coefficient.
Infected cells of mammogram image and performance analysis using Imaging techniques
by Diderot. P. Kumara Guru, N. Vasudevan
Abstract: Radiological image acquisition of mammogram and interpretation of cell count with image processing techniques is the focus behind this work. Image processing techniques incorporated Mammogram Image based Nuclei Count (MINC) calculates cell count and pixel intensity in malignant and entire breast area. MINC thus provides pixel based supervision of the image in the entire area and its malignant area and along with corresponding view of CC and MLO. Specific focus has been done with calcification type which are malignant and PLEMORPHIC in nature. In addition, Region Morphometry features of area, perimeter, circularity and elongation has been discussed for the infected area.
Keywords: Cell count; Micro-calcifications; and Regional Morphometry.
CS-ABC: Cuckoo Search based Adaptive Boosting Classifier for Malaria Infected Erythrocyte Detection
by Chaya Jagtap D, Usharani Nsai
Abstract: Abstract:Malaria Microscopic image processing and feature extraction allows to define computer aided disease prediction methods using machine learning. This practice enables to predict the disease scope regardless of technical expertise of the individual domain expert. In this context, design of computer aided methods those learned from the labeled erythrocytes given as training corpus to detect malaria scope at premature level is a crucial research objective in recent past. The contribution of this manuscript is a supervised learning technique that enables to detect the malaria scope in given erythrocyte.Contrast to former efforts in this region, this study utilizes multiple observable phase images of the unspotted cells. Automatically, erythrocytes are segmented utilizing optical phase thresholds and redeployed to allow quantitative association of the phase images. Redeployed images are examined to mine manifold morphological descriptors on the basis of phase information. When the entire individual descriptors are statistically diverse among uninfected & infected cells, every descriptor will not allow parting of the populations at the satisfactory level aimed at clinical service. The experimental study carried on the proposal and other contemporary models evincing that the proposed CS-ABC is considerably significant with maximal prediction accuracy and minimal misclassification rate that compared toother contemporary models.rnKeywords: WHO
Keywords: WHO; QPI; HSV method; AGNES; morphological gradient approaches.
ADAPTIVE BIO-INSPIRED GENE OPTIMIZATION BASED DEEP NEURAL ASSOCIATIVE CLASSIFICATION FOR DIABETIC DISEASE DIAGNOSIS
by D. SASIREKHA, Punitha A
Abstract: Associative classification plays a significant role in data mining. The Several classification techniques have been proposed in existing works using association rules. However, the accuracy of existing classification technique was not adequate. In order to overcome this limitation, an Adaptive Bio-Inspired Gene Optimization Based Deep Neural Associative Classification (ABGO-DNAC) technique is proposed. ABGO-DNAC technique is developed to improve the classification performance for diabetic disease diagnosis at an early stage by generating association rules with a minimal number of medical attributes.The ABGO-DNAC technique used Adaptive Bio-Inspired Gene Optimization ABGO algorithm to generate the association rule by choosing a minimal number of optimal attributes from a medical dataset. With the support of formulated association rules, the ABGO-DNAC technique design a Gaussian Deep FeedForward Neural Learning (GDFNL) for diabetic disease classification.The GDFNL deeply analyses the patient's medical data with the aid of created association rules and classify the patients as normal or abnormal.Thus, ABGO-DNAC technique efficiently identifies the diabetic disease at an earlier stage with higher classification accuracy and minimum time.The simulation evaluation of ABGO-DNAC technique is performed on factors such as disease prediction accuracy, disease prediction time and false positive rate with respect to various number of patients. The simulation results depict the ABGO-DNAC technique is able to increase the disease prediction accuracy and also reduce the diabetic disease diagnosing as compared to state-of-the-art works.
Keywords: Association Rules; Diabetic Disease; Logistic Loss Function; Adaptive Bio-Inspired Gene Optimization; Gaussian Deep Feedforward Neural Learning; Adaptive Levy Mutation.
Multi spectral image classification based on deep feature extraction using deep learning technique
by Muralimohanbabu Y., Radhika K.
Abstract: Remote sensing image classification accuracy depends on the extraction of Deep Feature Extraction. Unsupervised deep feature extraction employs single-layer and deep convolutional networks. Application of supervised convolutional networks is highly challenging for multi- and hyperspectral imagery when input data dimensionality is high and labelled set is limited. To accomplish the mentioned, greedy layer-wise unsupervised pre-training combined with an appropriate algorithm for unsupervised learning of sparse features is proposed. This algorithm concentrates on sparse representations and sparsity of the extracted features at a time. The proposed method is applied for land use/cover classification of different spatial/spectral remote imagery. Comparing the current algorithms for classification, the proposed method performs well. Extraction of powerful discriminative features is possible with single-layer convolutional networks to obtain detailed results in classification. Different spatial/spectral parameters are calculated to quantify the results.
Keywords: unsupervised learning; deep learning; multispectral images; segmentation; accuracy; classification.
Fusion of registered Medical Images Using Deep Learning Convolutional Neural Network with Statistics-based Steered Image Filter
by Suneetha Rikhari
Abstract: Medical image fusion technique plays an increasingly critical role in many clinical applications by deriving the complementary information from medical images with different modalities. In this, a novel MR and CT image fusion approach is proposed which utilizes the deep learning convolutional neural networks (CNNs) with statistics based steered image filter (SSIF). In our method, a deep learning convolutional network is adopted to generate a weight map which integrates the pixel activity information from MR and CT images. The fusion process is conducted via SSIF fusion rule which computes the weights of obtained detail layers using image statistics. In addition, weighted average method is utilized to obtain the fused image. Further, proposed fusion algorithm is extended to applicable for RGB image fusion. Experimental results demonstrate that the proposed method can achieve promising results in terms of both visual quality and objective assessment.
Keywords: image registration; medical image fusion; convolutional neural networks; steered image filter; image statistics; image quality metrics.
Visualization of Meniscus from Knee Joint MRI and Assessment of its Size Differences due to Age, Gender and BMI
by Mallikarjunaswamy M S, Mallikarjun S Holi, Rajesh Raman, Sujana Theja J S
Abstract: Menisci play a major role in cushioning and distributing the stress due to body weight over the cartilage surface of femur and tibia. They also contribute in joint lubrication and provide stability for the joint with the support of ligaments. It is essential to quantify the size and volume of the meniscus in treatment of injured and discoid meniscus. Visualization of menisci using MRI is helpful in understanding location of menisci tears and degradation. In this work, menisci were segmented from knee joint MRI using seeded region growing algorithm and volume rendered for 3D visualization. The menisci are quantified using image processing method. The noninvasive method of visualization and quantification of menisci is useful for diagnosis and surgical planning of diseased knee joints especially elderly people and sports injured. Influencing factors for size difference in menisci due to age, gender and BMI is analyzed using statistical methods.
Keywords: Knee joint; meniscus; magnetic resonance imaging; body mass index; segmentation.
Special Issue on: Capsules in Medical Image Processing
Hybrid Kernel Fuzzy C-Means clustering segmentation algorithm for Content Based Medical Image Retrieval application
by Lakshmana B., Sunil Kumar S. Manvi, Karibasappa K.G.
Abstract: Nowadays, a huge number of therapeutic images are generated because of number of patients' daily medical activities which are millions in size. Retrieving these therapeutic images from the huge dataset is a challenging task, hence Content Based Medical Image Retrieval (CBMIR) system is used. Clustering based segmentation for diagnosing the query image was proposed by retrieval system. A wide range of research has been made by the scientists to build up an enhanced algorithm for clustering. In this research paper, Hybrid Bee Colony and Cuckoo Search (HBCCS) based centroid initialization for segmenting the images with the help of Kernel Fuzzy C-Means clustering (KFCM) is proposed which is also known as CBMIR-KFCM-HBCCS. For CBMIR segmentation, KFCM is considered as a preferable system because of its execution in segmenting the query images. But, the major drawback of the conventional KFCM is initialization of centroids random that leads to rise of execution time to achieve the optimal solution. In order to stimulate the segmentation procedure, HBCSS is utilized to initialize the centroids of required clusters. The quantitative proportions of results were analyzed by utilizing the measurements like the number of emphases and processing time. The number of iterations and processing of CBMIR-KFCM-HBCCS technique takes least value while contrasted with conventional KFCM. The CBMIR-KFCM-HBCCS technique is efficient and faster than existing KFCM for segmenting the images in terms of specificity, sensitivity and accuracy. The proposed technique accomplished nearly 96% accuracy compared to other existing techniques.
Keywords: Therapeutic Images; Clustering; Segmentation; Fuzzy C-Means; Bee Colony; Cuckoo Search.
Special Issue on: Soft Computing and Optimisation Techniques for Biomedical Data Mining and Analysis
Recursive Subspace Based Feature Selection Approach for early risk prediction of Chronic Disease in Patients
by Sandeepkumar Hegde, Monica R.Mundada
Abstract: These days chronic disease is considered as threat to human health. These diseases persist for a longer period of time and responsible for over 70% of death and disability all over the world. The Diabetes mellitus is considered one of such chronic disease which is challenging to both developed and developing nations. It is anticipated that the world's 10% population will suffer from diabetes by 2045. It has become a driving reason for death. Healthcare information's are multidimensional in nature. Feature selection is considered as part of the preprocessing stage which is applied to the data with higher dimension in order to lessen the features which impact on the prediction of the disease. In this paper, a novel recursive subspace based feature selection (RSFS) algorithm is proposed. The feature subspace is obtained recursively by computing the covariance matrix and eigenvalue pairs. The process of selecting the features from the given data set is repeated until the machine learning model early predicts the Diabetes Miletus disease with the optimal accuracy. The experiment is conducted using a diabetes data set accumulated from National Institute of Diabetes. Experimental results are compared with existing techniques. The outcome demonstrates an accuracy of 88.5% which is higher compared to the existing approaches.
Keywords: Feature subspace; Covariance; Eigen value; machine learning; chronic disease.
Nearest Neighbor Based Feature Selection and Classification approach for analyzing sentiments
by Rajalaxmi Hegde, S. Seema
Abstract: Sentiment analysis is considered as one of the most important aspect in the field of research. The aim in this paper is to select features and perform classification of data using positive and negative. The objective of work is to analyze sentiment and perform classification. The main aim is to perform feature selection and use neighbor based classifier and tune the hyper parameters to get the optimal value to find the model accuracy and improve the performance. Proposed method performs the feature selection of data using the nearest neighbor based approach where initially the distance metrics and the cosine similarity of the data are calculated based on the preprocessed data. The term weighting mechanism is obtained to identify the weightage of each terms in the document. Experiments are conducted using several feature vectorization methods and produces better results.
Keywords: Accuracy; classification; Feature Selection; Preprocessing; Vectors.
ANN Model for Detection and Classification of Sleep and Non-Sleep stages
by Bollampally Anupama, Somayajulu Laxmi Narayana, K.S. Rao
Abstract: Electroencephalogram (EEG) is the most prominent tool used in the sleep related researches; disorders related to sleep has become one of the prime issues in human life. This work proposes an efficient approach to discriminate sleep stage from non-sleep stage or awake fullness by analysing EEG signals from frontal lobes. A second order FIR filter is designed to segregate Delta and Theta waves from EEG. Empirical Mode Decomposition technique is adopted to extract distinct features like Kurtosis, MAD (Median Absolute Deviation) and IQR (InterQuartile range). An artificial neural network model trained by Feed forward back propagation algorithm is adopted to classify the sleep and non-sleep classes. The amplitude and frequency values of sub-bands of electroencephalogram signals vary in sleep stage compared to non-sleep stage. The experimental results obtained determine that the features extracted proved to be good discriminators of sleep and non-sleep stage with an accuracy of 92%, sensitivity 100% and specificity 98%.
Keywords: Empirical mode decomposition; Sleep disorders; Electroencephalogram; Artificial neural network.
Privacy preserving Reversible watermarking in the encrypted domain through self-blinding
by Jeeva K.A, Sheeba V.S
Abstract: This paper presents robust image-based reversible watermarking in the encrypted domain. Two algorithms with high embedding rate are proposed for embedding data in a homomorphic encrypted domain using Paillier encryption scheme. Both algorithms exploit the self-blinding property of Paillier scheme to accomplish flexibility in extraction. Using these algorithms, blind and error-free watermark extraction is possible in the plaintext domain and encrypted domain. The robustness of the algorithms has been validated by considering various noise attacks in the encrypted domain. The proposed methods outperform its predecessors on the same embedding platform either in the flexibility of extraction process or in the embedding capacity and finds applications in privacy preserving distributed signal processing which is a major requirement in cloud environment.
Keywords: Privacy; Encryption; Encrypted DCT; Homomorphism; Paillier encryption; Reversible watermarking; Signal processing in the encrypted domain; Self-blinding.
Optimization of sub-space clustering in a high dimension data using Laplacian graph and machine learning
by Ambika P.R, Bharathi Malakreddy A
Abstract: There are many applications like business analytics, computer vision and medical data analytics, where an unsupervised approach of learning is used for the high-dimension data clustering. The problem of the subspace clustering is modelled as a graph problem which has to retain the critical features from the N-dimension while applying a dimension reduction technique to maintain a higher accuracy and lower computational overhead trade-off. Most of the traditional approaches suffer from the efficiency degradation when applied to the high-dimension data. An optimization of sub-space clustering is proposed in this paper for learning models using Laplacian Graph on a high-dimension data of inheritable factor and metamorphosis. The proposed model addresses the curse of dimensionality problem through Laplacian matrix function which eliminates the diagonal components or elements from input data matrix to minimize the data redundancy within sub-space. The traditional KNN algorithm is improvised for the non-linear classification of subspace clustering on high-dimension data clinical importance. The study outcome exhibits that proposed system offers significant increment of 99% of accuracy in clustering operation.
Keywords: Subspace clustering; dimension reduction; graph partition problem; high dimension data; Laplacian graph.
Artificial Neural Network Model for Detection and Classification of Alcoholic patterns in EEG
by Bollampally Anupama, Somayajulu Laxmi Narayana, K.S. Rao
Abstract: Alcoholism influences the brain function and is one of the major causes for cognitive, emotional and behavioural impairments. This work investigates alcohol and normal states by analysing EEG signals recorded from Frontal lobes of brain. Delta and Theta waves are the primary signals which vary by the effect of Alcohol. A second order FIR filter is designed to segregate Delta and Theta waves from EEG data. Empirical mode decomposition technique is adopted to extract distinct features like kurtosis, median absolute deviation and Inter quartile range. These features are given as input to Artificial Neural network for the feature classification. After consumption of Alcohol, the amplitude and frequency corresponding to delta and theta waves, which are responsible for inactive and sleep states of the brain are found to be very low in comparison to a normal state. The results indicate that features extracted proved to be good discriminators of alcoholics and normal with an accuracy of 92%, sensitivity 100% and specificity 83%.
Keywords: EEG; Empirical Mode Decomposition; Intrinsic Mode Functions (IMF); Artificial Neural Network (ANN); Back Propagation Algorithm; Hilbert Transform.
Configuring Artificial Neural Network Using Optimization Techniques For Speaker Voice Recognition
by Namburi Dhana Lakshmi, M. Satya Sai Ram
Abstract: Speaker recognition is proposed in this work using ANN and optimization technique which finds wide variety of applications. MFCC and LPC coefficients are utilized to extract features from voice signal as preliminary process. In this work, these features are applied to Artificial Neural Network (ANN) to recognize speaker. This research focuses on configuring conventional ANN structure with zero hidden layers to multiple hidden layers. Neural Network based Speaker Recognition generally achieves better recognition rate and it is possible to improve the accuracy by utilizing the large training data set or by increasing the number of hidden layers. Over-fitting and under-fitting problems can be addressed by optimizing the number of hidden layers. Genetic algorithm is applied to optimize hidden layers and the number of neurons for performance enhancement. The result reveals that the performance of GA in configuring ANN accomplishes 98% accuracy which is superior to conventional ANN. This research utilizes two types of database preliminary with 980 standard TIMIT voice signal database and 200 real time voice signal datasets.
Keywords: Speaker Recognition; Mel-Frequency Cepstral Coefficient (MFCC); Linear Prediction-filter Coefficients (LPC); Artificial Neural Network (ANN); Genetic Algorithm.
Gestational Age Determination of Ultrasound Foetal Images Using Artificial Neural Network
by Jujjavarapu Sunitha Kumari, Usha Rani Nelakuditi
Abstract: Gestation age estimation serves as an important task in assessing the high risks existing during pregnancy. Ultrasound fetal images provide valuable information for better understanding of the developmental stages. To accomplish this, suitable biometric parameters are monitored manually and the accuracy of this manual determination relies on skilled sonographer or image qualities. Such manual parametric determination subjects to multiple decisions resulting in causing observational errors. Thus, the main aim of the proposed approach is to evaluate the biometric parameters automatically for determining gestational age based fetal growth using ultrasound images. Fetal images undergo adaptive histogram equalization for its characteristics enhancement that is then followed by Normal shrink homomorphic filtering and canny edge detection-based segmentation process to extract the significant features with enhanced quality. The desired parameters are recognized by applying PBT classification that are further provided to the adaptive ANN for evaluating the status of the fetus in terms of gestational age. This integrated neural network accurately measures the growth of fetus with minimized error, shorter time and no significant difference exists between the estimated and the actual values.
Keywords: Ultrasound fetal images; Image enhancement; Edge detection; Normal shrink homomorphic technique; PBT classification; Artificial neural network.
Classification of Breast Cancer Images Using Completed Local Ternary Pattern and Support Vector Machine
by M. Kusuma Sri, E. Gomathi
Abstract: Breast cancer is the major cause for deaths in women compared to other cancers. Though early detection of breast cancer reduces cancer deaths, it is a challenging task for physicians. Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) techniques are widely applied in texture classification applications. Since LBP is more sensitive to noise in texture classification, it needs to be improved for achieving better results. Though LTP is more robust to noise, there are few drawbacks. Completed LBP and Completed Local Binary Count techniques achieve good accuracy for texture classification, but they inherit few drawbacks of LBP. In this paper, completed LTP operator is applied on breast cancer images for better classification accuracy than LBP and Completed LBP operators, by extracting sign and magnitude components. Experimental results based on breast cancer database show that the proposed technique achieved better classification accuracy than existing similar approaches.
Keywords: Breast cancer image; Image Segmentation; Texture classification; Machine learning; Decision Tree; Logistic Regression; Support Vector Machine.