DeepBCE: Evaluation of deep learning models for identification of immunogenic B-cell epitopes.

B-Cell epitopes (BCEs) can identify and bind with receptor proteins (antigens) to initiate an immune response against pathogens. Understanding antigen-antibody binding interactions has many applications in biotechnology and biomedicine, including designing antibodies, therapeutics, and vaccines. Lab-based experimental identification of these proteins is time-consuming and challenging. Computational techniques have been proposed to discover BCEs, but most lack of significant accomplishments. This work uses classical and deep learning models (DLMs) with sequence-based features to predict immunity stimulator BCEs from proteomics sequences. The proposed convolutional neural network-based model outperforms other models with an accuracy (ACC) of 0.878, an F-measure of 0.871, and an area under the receiver operating characteristic curve (AUC) of 0.945. The proposed strategy achieves 58.7% better results on average than other state-of-the-art approaches based on the Mathews Correlation Coefficient (MCC) results. The established model is accessible through a web application located at http://deeplbcepred.pythonanywhere.com.

Next Generation Infectious Diseases Monitoring Gages via Incremental Federated Learning: Current Trends and Future Possibilities.

Infectious diseases are always alarming for the survival of human life and are a key concern in the public health domain. Therefore, early diagnosis of these infectious diseases is a high demand for modern-era healthcare systems. Novel general infectious diseases such as coronavirus are infectious diseases that cause millions of human deaths across the globe in 2020. Therefore, early, robust recognition of general infectious diseases is the desirable requirement of modern intelligent healthcare systems. This systematic study is designed under Kitchenham guidelines and sets different RQs (research questions) for robust recognition of general infectious diseases. From 2018 to 2021, four electronic databases, IEEE, ACM, Springer, and ScienceDirect, are used for the extraction of research work. These extracted studies delivered different schemes for the accurate recognition of general infectious diseases through different machine learning techniques with the inclusion of deep learning and federated learning models. A framework is also introduced to share the process of detection of infectious diseases by using machine learning models. After the filtration process, 21 studies are extracted and mapped to defined RQs. In the future, early diagnosis of infectious diseases will be possible through wearable health monitoring cages. Moreover, these gages will help to reduce the time and death rate by detection of severe diseases at starting stage.

A Novel Text Mining Approach for Mental Health Prediction Using Bi-LSTM and BERT Model.

With the current advancement in the Internet, there has been a growing demand for building intelligent and smart systems that can efficiently address the detection of health-related problems on social media, such as the detection of depression and anxiety. These types of systems, which are mainly dependent on machine learning techniques, must be able to deal with obtaining the semantic and syntactic meaning of texts posted by users on social media. The data generated by users on social media contains unstructured and unpredictable content. Several systems based on machine learning and social media platforms have recently been introduced to identify health-related problems. However, the text representation and deep learning techniques employed provide only limited information and knowledge about the different texts posted by users. This is owing to a lack of long-term dependencies between each word in the entire text and a lack of proper exploitation of recent deep learning schemes. In this paper, we propose a novel framework to efficiently and effectively identify depression and anxiety-related posts while maintaining the contextual and semantic meaning of the words used in the whole corpus when applying bidirectional encoder representations from transformers (BERT). In addition, we propose a knowledge distillation technique, which is a recent technique for transferring knowledge from a large pretrained model (BERT) to a smaller model to boost performance and accuracy. We also devised our own data collection framework from Reddit and Twitter, which are the most common social media sites. Finally, we employed word2vec and BERT with Bi-LSTM to effectively analyze and detect depression and anxiety signs from social media posts. Our system surpasses other state-of-the-art methods and achieves an accuracy of 98% using the knowledge distillation technique.

LBCEPred: a machine learning model to predict linear B-cell epitopes.

B-cell epitopes have the capability to recognize and attach to the surface of antigen receptors to stimulate the immune system against pathogens. Identification of B-cell epitopes from antigens has a great significance in several biomedical and biotechnological applications, provides support in the development of therapeutics, design and development of an epitope-based vaccine and antibody production. However, the identification of epitopes with experimental mapping approaches is a challenging job and usually requires extensive laboratory efforts. However, considerable efforts have been placed for the identification of epitopes using computational methods in the recent past but deprived of considerable achievements. In this study, we present LBCEPred, a python-based web-tool (http://lbcepred.pythonanywhere.com/), build with random forest classifier and statistical moment-based descriptors to predict the B-cell epitopes from the protein sequences. LBECPred outperforms all sequence-based available models that are currently in use for the B-cell epitopes prediction, with 0.868 accuracy value and 0.934 area under the curve. Moreover, the prediction performance of proposed models compared to other state-of-the-art models is 56.3% higher on average for Mathews Correlation Coefficient. LBCEPred is easy to use tool even for novice users and has also shown the models stability and reliability, thus we believe in its significant contribution to the research community and the area of bioinformatics.

A Fuzzy Rule-Based System for Classification of Diabetes.

Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%. The proposed model has demonstrated great prediction accuracy, suggesting that it can be utilized in the healthcare sector for the accurate diagnose of diabetes.

Deregulation of mitochondrial sirtuins and OGG1-2a acts as a prognostic and diagnostic biomarker in leukemia.

Purpose: The present study was planned to explore the expression variations of mitochondrial sirtuins and the mitochondrial DNA repair enzyme OGG1-2a in leukemia patients. Oxidative stress and deacetylation levels of leukemia patients were measured in the present study. Methods: A total of 200 leukemia patients along with 200 healthy controls were evaluated using quantitative PCR, 8OXOG assay and deacetylation assay. Results: Significant deregulation of SIRT3 (p < 0.0001), SIRT4 (p < 0.0001), SIRT5 (p < 0.0001), Ki-67 (p < 0.0001) and OGG1-2a (p < 0.0001) was detected in patients versus controls. Survival analysis showed that deregulation of said genes was associated with decreased survival of leukemia patients (SIRT3: p < 0.004; SIRT4: p < 0.0009; SIRT5: p < 0.0001; OGG1-2a: p < 0.03). Receiver operating characteristic curve analysis confirmed the diagnostic values of selected genes in leukemia patients. Levels of 8OXOG adducts were measured, and significantly increased 8OXOG adduct levels were observed in patients versus controls. Conclusion: These data suggest that deregulation of SIRT3, SIRT4, SIRT5 and OGG1-2a acts as a diagnostic and prognostic marker in leukemia.

Lay abstract Leukemia is a type of blood cancer that has shown an increased rate of occurrence worldwide. Studies have shown that environmental and genetic factors are involved in the increased rate of this disease. Of the genetic factors, sirtuins (SIRT3, SIRT4 and SIRT5) and OGG1-2a have not been studied in leukemia. In the present study, the authors aimed to study the genetic/epigenetic changes in these genes in leukemia patients. Results of the present study showed involvement of selected gene variations in the increased rate of leukemia, at least in the Pakistani population.

Correction to a novel framework for rapid diagnosis of COVID-19 on computed tomography scans.

[This corrects the article DOI: 10.1007/s10044-020-00950-0.].

A novel framework for rapid diagnosis of COVID-19 on computed tomography scans.

Since the emergence of COVID-19, thousands of people undergo chest X-ray and computed tomography scan for its screening on everyday basis. This has increased the workload on radiologists, and a number of cases are in backlog. This is not only the case for COVID-19, but for the other abnormalities needing radiological diagnosis as well. In this work, we present an automated technique for rapid diagnosis of COVID-19 on computed tomography images. The proposed technique consists of four primary steps: (1) data collection and normalization, (2) extraction of the relevant features, (3) selection of the most optimal features and (4) feature classification. In the data collection step, we collect data for several patients from a public domain website, and perform preprocessing, which includes image resizing. In the successive step, we apply discrete wavelet transform and extended segmentation-based fractal texture analysis methods for extracting the relevant features. This is followed by application of an entropy controlled genetic algorithm for selection of the best features from each feature type, which are combined using a serial approach. In the final phase, the best features are subjected to various classifiers for the diagnosis. The proposed framework, when augmented with the Naive Bayes classifier, yields the best accuracy of 92.6%. The simulation results are supported by a detailed statistical analysis as a proof of concept.

Analytical Study of Hybrid Techniques for Image Encryption and Decryption.

The majority of imaging techniques use symmetric and asymmetric cryptography algorithms to encrypt digital media. Most of the research works contributed in the literature focus primarily on the Advanced Encryption Standard (AES) algorithm for encryption and decryption. This paper propose an analysis for performing image encryption and decryption by hybridization of Elliptic Curve Cryptography (ECC) with Hill Cipher (HC), ECC with Advanced Encryption Standard (AES) and ElGamal with Double Playfair Cipher (DPC). This analysis is based on the following parameters: (i) Encryption and decryption time, (ii) entropy of encrypted image, (iii) loss in intensity of the decrypted image, (iv) Peak Signal to Noise Ratio (PSNR), (v) Number of Pixels Change Rate (NPCR), and (vi) Unified Average Changing Intensity (UACI). The hybrid process involves the speed and ease of implementation from symmetric algorithms, as well as improved security from asymmetric algorithms. ECC and ElGamal cryptosystems provide asymmetric key cryptography, while HC, AES, and DPC are symmetric key algorithms. ECC with AES are perfect for remote or private communications with smaller image sizes based on the amount of time needed for encryption and decryption. The metric measurement with test cases finds that ECC and HC have a good overall solution for image encryption.

Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods.

Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs. The CCPM first removes outliers by using outlier detection methods such as density-based spatial clustering of applications with noise (DBSCAN) and isolation forest (iForest) and by increasing the number of cases in the dataset in a balanced way, for example, through synthetic minority over-sampling technique (SMOTE) and SMOTE with Tomek link (SMOTETomek). Finally, it employs random forest (RF) as a classifier. Thus, CCPM lies on four scenarios: (1) DBSCAN + SMOTETomek + RF, (2) DBSCAN + SMOTE+ RF, (3) iForest + SMOTETomek + RF, and (4) iForest + SMOTE + RF. A dataset of 858 potential patients was used to validate the performance of the proposed method. We found that combinations of iForest with SMOTE and iForest with SMOTETomek provided better performances than those of DBSCAN with SMOTE and DBSCAN with SMOTETomek. We also observed that RF performed the best among several popular machine learning classifiers. Furthermore, the proposed CCPM showed better accuracy than previously proposed methods for forecasting cervical cancer. In addition, a mobile application that can collect cervical cancer risk factors data and provides results from CCPM is developed for instant and proper action at the initial stage of cervical cancer.

Geo-Social Top-k and Skyline Keyword Queries on Road Networks.

The rapid growth of GPS-enabled mobile devices has popularized many location-based applications. Spatial keyword search which finds objects of interest by considering both spatial locations and textual descriptions has become very useful in these applications. The recent integration of social data with spatial keyword search opens a new service horizon for users. Few previous studies have proposed methods to combine spatial keyword queries with social data in Euclidean space. However, most real-world applications constrain the distance between query location and data objects by a road network, where distance between two points is defined by the shortest connecting path. This paper proposes geo-social top-k keyword queries and geo-social skyline keyword queries on road networks. Both queries enrich traditional spatial keyword query semantics by incorporating social relevance component. We formalize the proposed query types and appropriate indexing frameworks and algorithms to efficiently process them. The effectiveness and efficiency of the proposed approaches are evaluated using real datasets.

A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks.

Human face image analysis is an active research area within computer vision. In this paper we propose a framework for face image analysis, addressing three challenging problems of race, age, and gender recognition through face parsing. We manually labeled face images for training an end-to-end face parsing model through Deep Convolutional Neural Networks. The deep learning-based segmentation model parses a face image into seven dense classes. We use the probabilistic classification method and created probability maps for each face class. The probability maps are used as feature descriptors. We trained another Convolutional Neural Network model by extracting features from probability maps of the corresponding class for each demographic task (race, age, and gender). We perform extensive experiments on state-of-the-art datasets and obtained much better results as compared to previous results.

A Unified Framework for Head Pose, Age and Gender Classification through End-to-End Face Segmentation.

Accurate face segmentation strongly benefits the human face image analysis problem. In this paper we propose a unified framework for face image analysis through end-to-end semantic face segmentation. The proposed framework contains a set of stack components for face understanding, which includes head pose estimation, age classification, and gender recognition. A manually labeled face data-set is used for training the Conditional Random Fields (CRFs) based segmentation model. A multi-class face segmentation framework developed through CRFs segments a facial image into six parts. The probabilistic classification strategy is used, and probability maps are generated for each class. The probability maps are used as features descriptors and a Random Decision Forest (RDF) classifier is modeled for each task (head pose, age, and gender). We assess the performance of the proposed framework on several data-sets and report better results as compared to the previously reported results.

Colorization and automated segmentation of human T2 MR brain images for characterization of soft tissues.

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described.

Microalbumiuria and associated risk factors in type II diabetics.

OBJECTIVE: To determine the frequency of microalbuminuria (MA) and its associated medical risk factors in type II diabetic patients. PLACE AND DURATION OF STUDY: This cross-sectional analytical study was conducted during Ist half of 2003 at Combined Military Hospital, Lahore. Non-probability purposive sampling was employed. MATERIALS AND METHODS: Study population included 150 type II diabetic patients (70 women, 80 men) attending outpatient department of the hospital. Patients having clinical albuminuria and with other causes of proteinuria were excluded. RESULTS: Women and men were of comparable ages. Women (26.4 kg/m2) had higher body mass index (BMI) than men (24.3 kg/m2). The frequency of MA was 46.7%, higher in males (50.6%) than females (41.5%). Fasting plasma glucose and HbA1c levels were significantly higher in patients with MA compared to those with normoalbuminuria (p <0.001). The microalbuminuric patients had significantly decreased HDL-c levels compared to normoalbuminuric subjects (p < 0.001). However, no relation of MA with age, gender, known duration of diabetes, BMI, history of smoking, hypertension and serum: total cholesterol, LDL - c, triglyceride, urea and creatinine was found. CONCLUSION: There is a strong association of poor glycaemic control and decreased HDL-c levels with the presence of microalbuminuria.