For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients’ lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.
To recognize the basis of disease, it is essential to determine its underlying genes. Understanding the association between underlying genes and genetic disease is a fundamental problem regarding human health. Identification and association of genes with the disease require time consuming and expensive experimentations of a great number of potential candidate genes. Therefore, the alternative inexpensive and rapid computational methods have been proposed that can identify the candidate gene associated with a disease. Most of these methods use phenotypic similarities due to the fact that genes causing same or similar diseases have less variation in their sequence or network properties of proteinprotein interactions based on-premises that genes lie closer in protein interaction network that causes the similar or same disease. However, these methods use only basic network properties or topological features and gene sequence information or biological features as a prior knowledge for identification of gene-disease association, which restricts the identification process to a single gene-disease association. In this study, we propose and analyze some novel computational methods for the identification of genes associated with diseases. Some advance topological and biological features that are overlooked currently are introducing for identifying candidate genes. We evaluate different computational methods on disease-gene association data from DisGeNET in a 10-fold cross-validation mode based on TP rate, FP rate, precision, recall, F-measure, and ROC curve evaluation parameters. The results reveal that various computational methods with advanced feature set outperform previous state-of-the-art techniques by achieving precision up to 93.8%, recall up to 93.1%, and F-measure up to 92.9%. Significantly, we apply our methods to study four major diseases: Thalassemia, Diabetes, Malaria, and Asthma. Simulation results show that the proposed Deep Extreme Learning Machine (DELM) gives more accurate results as compared to previously published approaches.
Background:
Complex prediction from interaction network of proteins has become a
challenging task. Most of the computational approaches focus on topological structures of protein
complexes and fewer of them consider important biological information contained within amino
acid sequences.
Objective:
To capture the essence of information contained within protein sequences we have
computed sequence entropy and length. Proteins interact with each other and form different sub
graph topologies.
Methods:
We integrate biological features with sub graph topological features and model complexes
by using a Logistic Model Tree.
Results:
The experimental results demonstrated that our method out performs other four state-ofart
computational methods in terms of the number of detecting known protein complexes correctly.
Conclusion:
In addition, our framework provides insights into future biological study and might
be helpful in predicting other types of sub graph topologies.
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