Incorporated model of deep features fusion

Abdelaziz, A., & Mahmoud, A. N. (2022). Skin Cancer Detection Using Deep Learning and Artificial Intelligence: Incorporated model of deep features fusion. Fusion: Practice and Applications, 8(2), 8-15. https://doi.org/10.54216/FPA.080201 © 2022, American Scientific Publishing Group (ASPG). All rights reserved.Among the most frequent forms of cancer, skin cancer accounts for hundreds of thousands of fatalities annually throughout the globe. It shows up as excessive cell proliferation on the skin. The likelihood of a successful recovery is greatly enhanced by an early diagnosis. More than that, it might reduce the need for or the frequency of chemical, radiological, or surgical treatments. As a result, savings on healthcare expenses will be possible. Dermoscopy, which examines the size, form, and color features of skin lesions, is the first step in the process of detecting skin cancer and is followed by sample and lab testing to confirm any suspicious lesions. Deep learning AI has allowed for significant progress in image-based diagnostics in recent years. Deep neural networks known as convolutional neural networks (CNNs or ConvNets) are essentially an extended form of multi-layer perceptrons. In visual imaging challenges, CNNs have shown the best accuracy. The purpose of this research is to create a CNN model for the early identification of skin cancer. The backend of the CNN classification model will be built using Keras and Tensorflow in Python. Different network topologies, such as Convolutional layers, Dropout layers, Pooling layers, and Dense layers, are explored and tried out throughout the model's development and validation phases. Transfer Learning methods will also be included in the model to facilitate early convergence. The dataset gathered from the ISIC challenge archives will be used to both tests and train the model.publishersversionpublishe

Context-specific subcellular localization prediction: Leveraging protein interaction networks and scientific texts

Zhu L. Context-specific subcellular localization prediction: Leveraging protein interaction networks and scientific texts. Bielefeld: Universität Bielefeld; 2018.One essential task in proteomics analysis is to explore the functions of proteins in conducting and regulating the activities at the subcellular level. Compartmentalization of cells allows proteins to perform their activities efficiently. A protein functions correctly only if it occurs at the right place, at the right time, and interacts with the right molecules. Therefore, the knowledge of protein subcellular localization (SCL) can provide valuable insights for understanding protein functions and related cellular mechanisms. Thus, the systematic study of the subcellular distribution of human proteins is an essential task for fully characterizing the human proteome. The context-specific analysis is an important and challenging task in systems biology research. Proteins may perform different functions at different subcellular compartments (SCCs). Hence, the dynamic and context-specific alterations of the subcellular spatial distribution of proteins are essential in identifying cellular function. While this important feature is well-known in molecular and cell biology, most large-scale protein annotation studies to-date have ignored it. Tissue is one particularly crucial biological context for human biology. Proteins show their tissue specificity at the subcellular level by localizing to different SCCs in different tissues. For example, glutamine synthetase localizes in mitochondria in liver cells while in the cytoplasm in brain cells. The knowledge of the tissue-specific SCLs can enrich the human protein annotation, and thus will increase our understanding of human biology. Conventional wet-lab experiments are used to determine the SCL of proteins. Due to the expense and low-throughput of wet-lab experimental approaches, various algorithms and tools have been developed for predicting protein SCLs by integrating biological background knowledge into machine learning methods. Most of the existing approaches are designed for handling general genome-wide large-scale analysis. Thus, they cannot be used for context-specific analysis of protein SCL. The focus of this work is to develop new methods to perform tissue-specific SCL prediction. (1) First, we developed Bayesian collective Markov Random Fields (BCMRFs) to address the general multi-SCL problem. BCMRFs integrate both protein-protein interaction network (PPIN) features and the protein sequence features, consider the spatial adjacency of SCCs, and employ transductive learning on imbalanced SCL data sets. Our experimental results show that BCMRFs achieve higher performance in comparison with the state-of-art PPI-based method in SCL prediction. (2) We then integrated BCMRFs into a novel end-to-end computational approach to perform tissue-specific SCL prediction on tissue-specific PPINs. In total, 1314 proteins which SCLs were previously proven cell lines dependent were successfully localized based on nine tissue-specific PPINs. Furthermore, 549 new tissue-specific localized candidate proteins were predicted and confirmed by scientific literature. Due to the high performance of BCMRFs on known tissue-specific proteins, these are excellent candidates for further wet-lab experimental validation. (3) In addition to the proteomics data, the existing scientific literature contains an abundance of tissue-specific SCL data. To collect these data, we developed a scoring-based text mining system and extracted tissue-specific SCL associations from the abstracts of a large number of biomedical papers. The obtained data are accessible from the web based database TS-SCL DB. (4) We concluded the study with an application case study of the tissue-specific subcellular distribution of human argonaute-2 (AGO2) protein. We demonstrated how to perform tissue-specific SCL prediction on AGO2-related PPINs. Most of the resulting tissue-specific SCLs are confirmed by literature results available in TS-SCL DB

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions