Tissue specific tumor-gene link prediction through sampling based GNN using a heterogeneous network.

A tissue sample is a valuable resource for understanding a patient's symptoms and health status in relation to tumor growth. Recent research seeks to establish a connection between tissue-specific tumor samples and genetic markers (genes). This breakthrough has paved the way for personalized cancer therapies. With this motivation, the proposed model constructs a heterogeneous network based on tumor sample-gene relation data and gene-gene interaction data. This network also incorporates tissue-specific gene expression and primary site-based gene counts as features, enabling tissue-specific predictions. Graph neural networks (GNNs) have proven effective in modeling complex interactions and predicting links within this network. The proposed model has successfully predicted tumor-gene associations by leveraging sampling-based GNNs and link layer embedding. The model's performance metrics, such as AUC-ROC scores, reached approximately 94%, demonstrating the potential of this heterogeneous network in predicting tissue-specific tumor sample-gene links. This paper's findings highlight the importance of tissue-specific associations in cancer research.

The Role of Bioinformatics and Imaging Models in Tumorigenesis and Treatment Response of Brain and Spinal Cord Neoplasm.

This chapter focuses on the division and location of brain deformities such as tumors in magnetic resonance imaging (MRI) through Chan-Vese active contour segmentation. Brain tumor division and identification is a major test in the area of biomedical picture processing. To detect the size and location of the tumor, various techniques are available, but active contour gives accurate knowledge of the region for segmentation. Chan-Vese Active contour method provides independent, robust and more flexible segmentation. In this chapter, firstly we used preprocessing technique in which noise and unused parts of the brain and skull are removed, for this we proposed the skull stripping method. Then, we applied feature extraction to enhance the image intensity and quality, and lastly, used Chan-Vese active contour with a level set image segmentation technique to detect the tumor. The tumor area was calculated after tumor detection.

Clinical and Molecular Characterization of a Rare Case of BNT162b2 mRNA COVID-19 Vaccine-Associated Myositis.

Initial clinical trials and surveillance data have shown that the most commonly administered BNT162b2 COVID-19 mRNA vaccine is effective and safe. However, several cases of mRNA vaccine-induced mild to moderate adverse events were recently reported. Here, we report a rare case of myositis after injection of the first dose of BNT162b2 COVID-19 mRNA vaccine into the left deltoid muscle of a 34-year-old, previously healthy woman who presented progressive proximal muscle weakness, progressive dysphagia, and dyspnea with respiratory failure. One month after vaccination, BNT162b2 vaccine mRNA expression was detected in a tissue biopsy of the right deltoid and quadriceps muscles. We propose this case as a rare example of COVID-19 mRNA vaccine-induced myositis. This study comprehensively characterizes the clinical and molecular features of BNT162b2 mRNA vaccine-associated myositis in which the patient was severely affected.

Discrimination and quantification of live/dead rat brain cells using a non-linear segmentation model.

The automatic cell analysis method is capable of segmenting the cells and can detect the number of live/dead cells present in the body. This study proposed a novel non-linear segmentation model (NSM) for the segmentation and quantification of live/dead cells present in the body. This work also reveals the aspects of electromagnetic radiation on the cell body. The bright images of the hippocampal CA3 region of the rat brain under the resolution of 60 × objective are used to analyze the effects called NISSL-stained dataset. The proposed non-linear segmentation model segments the foreground cells from the cell images based on the linear regression analysis. These foreground cells further get discriminated as live/dead cells and quantified using shape descriptors and geometric method, respectively. The proposed segmentation model is showing promising results (accuracy, 82.82%) in comparison with the existing renowned approaches. The counting analysis of live and dead cells using the proposed method is far better than the manual counts. Therefore, the proposed segmentation model and quantifying procedure is an amalgamated method for cell quantification that yields better segmentation results and provides pithy insights into the analysis of neuronal anomalies at a microscopic level. Graphical Abstract Resultant View of the overall proposed approach.

Quality assessment of modeled protein structure using physicochemical properties.

Physicochemical properties of proteins always guide to determine the quality of the protein structure, therefore it has been rigorously used to distinguish native or native-like structure from other predicted structures. In this work, we explore nine machine learning methods with six physicochemical properties to predict the Root Mean Square Deviation (RMSD), Template Modeling (TM-score), and Global Distance Test (GDT_TS-score) of modeled protein structure in the absence of its true native state. Physicochemical properties namely total surface area, euclidean distance (ED), total empirical energy, secondary structure penalty (SS), sequence length (SL), and pair number (PN) are used. There are a total of 95,091 modeled structures of 4896 native targets. A real coded Self-adaptive Differential Evolution algorithm (SaDE) is used to determine the feature importance. The K-fold cross validation is used to measure the robustness of the best predictive method. Through the intensive experiments, it is found that Random Forest method outperforms over other machine learning methods. This work makes the prediction faster and inexpensive. The performance result shows the prediction of RMSD, TM-score, and GDT_TS-score on Root Mean Square Error (RMSE) as 1.20, 0.06, and 0.06 respectively; correlation scores are 0.96, 0.92, and 0.91 respectively; R(2) are 0.92, 0.85, and 0.84 respectively; and accuracy are 78.82% (with ± 0.1 err), 86.56% (with ± 0.1 err), and 87.37% (with ± 0.1 err) respectively on the testing data set. The data set used in the study is available as supplement at http://bit.ly/RF-PCP-DataSets.

Hybrid softcomputing model for lesion identification and information combination: some case studies.

Authors present segmentation and information combination of section of human brain images. Improved hybrid algorithm is presented for clustering, which integrates the concept of Rough sets, Fuzzy sets incorporated with probabilistic as well as possibilistic memberships. The segmented images are fused using wavelet and curvelet based techniques. Lower and upper approximations of Rough sets handle uncertainty, vagueness, and incompleteness in class definition. To accelerate the segmentation process, the RFPCM has been equipped with membership suppression mechanism, which creates competition among clusters to speed-up the clustering process using MR T1 and MR T2 images of section of human brain.

Multimodality medical image registration and fusion techniques using mutual information and genetic algorithm-based approaches.

Medical image fusion has been used to derive the useful complimentary information from multimodal images. The prior step of fusion is registration or proper alignment of test images for accurate extraction of detail information. For this purpose, the images to be fused are geometrically aligned using mutual information (MI) as similarity measuring metric followed by genetic algorithm to maximize MI. The proposed fusion strategy incorporating multi-resolution approach extracts more fine details from the test images and improves the quality of composite fused image. The proposed fusion approach is independent of any manual marking or knowledge of fiducial points and starts the procedure automatically. The performance of proposed genetic-based fusion methodology is compared with fuzzy clustering algorithm-based fusion approach, and the experimental results show that genetic-based fusion technique improves the quality of the fused image significantly over the fuzzy approaches.

Computerized decision support system for mass identification in breast using digital mammogram: a study on GA-based neuro-fuzzy approaches.

In the present work, authors have developed a treatment planning system implementing genetic based neuro-fuzzy approaches for accurate analysis of shape and margin of tumor masses appearing in breast using digital mammogram. It is obvious that a complicated structure invites the problem of over learning and misclassification. In proposed methodology, genetic algorithm (GA) has been used for searching of effective input feature vectors combined with adaptive neuro-fuzzy model for final classification of different boundaries of tumor masses. The study involves 200 digitized mammograms from MIAS and other databases and has shown 86% correct classification rate.

Identification of tiny and large calcification in breast: a study on mammographic image analysis.

Breast cancer throughout the world is a significant health problem for women. Small clusters of microcalcifications appearing as collection of white spots on mammograms indicate an early warning of breast cancer. In present work we have initiated computer-aided analysis of mammograms to automate the diagnostic procedures for breast cancer screening using multiresolution and FCM based clustering algorithms. Further region growing approach has been applied to segment considerably larger calcifications and the masses. The advantage of the proposed method is its ability to detect both tiny and comparatively larger calcifications in a single image and to reduce the false-positive appearance of microcalcifications in breast.