Diabetic retinopathy (DR) is on the increase nowadays due to the high sugar level in the blood, and it is the reason for blindness that mainly occurs in middle-aged people. Furthermore, the Internet of Medical Things (IoMT) enabled computer-aided diagnostic (CAD) systems record DR-related data online and give patients with reassuring information. The internet allows for the interconnection of a variety of smart devices, enabling remote healthcare systems based on the IoMT to link patients with medical professionals. The proper diagnosis of diabetic patients and detection of DR severity in earlier stages help in preventing blindness. Therefore, the basic aim of this study is to prevent the diabetic patient from losing vision by detecting and classifying the severity of DR fundus images using the IoMT-enabled CAD system. This article designed a novel diabetic retinopathy classification (DRC) system by hybridizing the DL model with optimization algorithms to classify the DR images based on severity. This system begins with preprocessing phase for removing the noise from edges. Next, the proposed K-mean cluster-based growing region segmentation is employed to extract the useful region from the images. Then, pretrained convolutional neural network (CNN) model, i.e., RESnet with the proposed hybrid genetic and ant colony optimization (HGACO) algorithm, is applied to extract the features from the region of interest (ROI) and classify them into four severity levels. Performance indices such as AUC,$F$-measure, accuracy, sensitivity, and specificity are analyzed to evaluate the performance on MESSIDOR dataset. The performance of the proposed DRC system is compared with state-of-the-art classification systems. The proposed HGACO algorithm is also compared with Adam and gradient descent (GD) optimizers. The system is also evaluated by employing different parameters of CNN and HGACO. Additionally, for the determination of the classification accuracy of the DRC system, the confidence interval statistical test is implemented considering various parameters and configurations of the neural network. The results revealed that the proposed DR system provides higher classification results by achieving 95.78%, 91.98%, 97.7%, and 94.56% AUC, sensitivity, accuracy, and specificity rate, respectively. CNN alleviates the difficulty of developing image features, whereas the HGACO algorithm-based technique automates CNN hyperparameter design.

The availability of a reliable prediction method for prediction of bacterial virulent proteins has several important applications in research efforts targeted aimed at finding novel drug targets, vaccine candidates, and understanding virulence mechanisms in pathogens. In this work, we have studied several feature extraction approaches for representing proteins and propose a novel bacterial virulent protein prediction method, based on an ensemble of classifiers where the features are extracted directly from the amino acid sequence and from the evolutionary information of a given protein. We have evaluated and compared several ensembles obtained by combining six feature extraction methods and several classification approaches based on two general purpose classifiers (i.e., Support Vector Machine and a variant of input decimated ensemble) and their random subspace version. An extensive evaluation was performed according to a blind testing protocol, where the parameters of the system are optimized using the training set and the system is validated in three different independent data sets, allowing selection of the most performing system and demonstrating the validity of the proposed method. Based on the results obtained using the blind test protocol, it is interesting to note that even if in each independent data set the most performing stand-alone method is not always the same, the fusion of different methods enhances prediction efficiency in all the tested independent data sets.

UNLABELLED: ProtVirDB is a comprehensive and user-friendly web-based knowledgebase of virulent proteins belonging to protozoan species. The database will facilitate research and provide an integrated platform for comparative studies of virulent proteins in different parasitic protozoans and organize them under a unifying classification schema with functional categories. Remarkably, one-third of the protein sequences in the database showed presence of either mono- or hetero-repeats, or both concomitantly--hence reiterating the importance of repeats in parasite virulence mechanisms. A number of useful bioinformatics tools including BLAST and tools for phylogenetic analysis are integrated with the database. With the rapidly burgeoning interest in the pathogenesis mechanisms of protozoans and ongoing genome sequencing projects, we anticipate that the database will be a useful tool for the research community. AVAILABILITY: http://bioinfo.icgeb.res.in/protvirdb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Functional annotation of rapidly amassing nucleotide and protein sequences presents a challenging task for modern bioinformatics. This is particularly true for protein families sharing extremely low sequence identity, as for lipocalins, a family of proteins with varied functions and great diversity at the sequence level, yet conserved structures. RESULTS: In the present study we propose a SVM based method for identification of lipocalin protein sequences. The SVM models were trained with the input features generated using amino acid, dipeptide and secondary structure compositions as well as PSSM profiles. The model derived using both PSSM and secondary structure emerged as the best model in the study. Apart from achieving a high prediction accuracy (>90% in leave-one-out), lipocalinpred correctly differentiates closely related fatty acid-binding proteins and triabins as non-lipocalins. CONCLUSION: The method offers a promising approach as a lipocalin prediction tool, complementing PROSITE, Pfam and homology modelling methods.

BACKGROUND: Prediction of bacterial virulent protein sequences has implications for identification and characterization of novel virulence-associated factors, finding novel drug/vaccine targets against proteins indispensable to pathogenicity, and understanding the complex virulence mechanism in pathogens. RESULTS: In the present study we propose a bacterial virulent protein prediction method based on bi-layer cascade Support Vector Machine (SVM). The first layer SVM classifiers were trained and optimized with different individual protein sequence features like amino acid composition, dipeptide composition (occurrences of the possible pairs of ith and i+1th amino acid residues), higher order dipeptide composition (pairs of ith and i+2nd residues) and Position Specific Iterated BLAST (PSI-BLAST) generated Position Specific Scoring Matrices (PSSM). In addition, a similarity-search based module was also developed using a dataset of virulent and non-virulent proteins as BLAST database. A five-fold cross-validation technique was used for the evaluation of various prediction strategies in this study. The results from the first layer (SVM scores and PSI-BLAST result) were cascaded to the second layer SVM classifier to train and generate the final classifier. The cascade SVM classifier was able to accomplish an accuracy of 81.8%, covering 86% area in the Receiver Operator Characteristic (ROC) plot, better than that of either of the layer one SVM classifiers based on single or multiple sequence features. CONCLUSION: VirulentPred is a SVM based method to predict bacterial virulent proteins sequences, which can be used to screen virulent proteins in proteomes. Together with experimentally verified virulent proteins, several putative, non annotated and hypothetical protein sequences have been predicted to be high scoring virulent proteins by the prediction method. VirulentPred is available as a freely accessible World Wide Web server - VirulentPred, at http://bioinfo.icgeb.res.in/virulent/.

BACKGROUND: Genome wide and cross species comparisons of amino acid repeats is an intriguing problem in biology mainly due to the highly polymorphic nature and diverse functions of amino acid repeats. Innate protein repeats constitute vital functional and structural regions in proteins. Repeats are of great consequence in evolution of proteins, as evident from analysis of repeats in different organisms. In the post genomic era, availability of protein sequences encoded in different genomes provides a unique opportunity to perform large scale comparative studies of amino acid repeats. ProtRepeatsDB http://bioinfo.icgeb.res.in/repeats/ is a relational database of perfect and mismatch repeats, access to which is designed as a resource and collection of tools for detection and cross species comparisons of different types of amino acid repeats. DESCRIPTION: ProtRepeatsDB (v1.2) consists of perfect as well as mismatch amino acid repeats in the protein sequences of 141 organisms, the genomes of which are now available. The web interface of ProtRepeatsDB consists of different tools to perform repeat s; based on protein IDs, organism name, repeat sequences, and keywords as in FASTA headers, size, frequency, gene ontology (GO) annotation IDs and regular expressions (REGEXP) describing repeats. These tools also allow formulation of a variety of simple, complex and logical queries to facilitate mining and large-scale cross-species comparisons of amino acid repeats. In addition to this, the database also contains sequence analysis tools to determine repeats in user input sequences. CONCLUSION: ProtRepeatsDB is a multi-organism database of different types of amino acid repeats present in proteins. It integrates useful tools to perform genome wide queries for rapid screening and identification of amino acid repeats and facilitates comparative and evolutionary studies of the repeats. The database is useful for identification of species or organism specific repeat markers, interspecies variations and polymorphism.