Up-Regulated Proteins Have More Protein-Protein Interactions than Down-Regulated Proteins.

Microarray technology has been successfully used in many biology studies to solve the protein-protein interaction (PPI) prediction computationally. For normal tissue, the cell regulation process begins with transcription and ends with the translation process. However, when cell regulation activity goes wrong, cancer occurs. Microarray data can precisely give high accuracy expression levels at normal and cancer-affected cells, which can be useful for the identification of disease-related genes. First, the differentially expressed genes (DEGs) are extracted from the cancer microarray dataset in order to identify the genes that are up-regulated and down-regulated during cancer progression in the human body. Then, proteins corresponding to these genes are collected from NCBI, and then the STRING web server is used to build the PPI network of these proteins. Interestingly, up-regulated proteins have always a higher number of PPIs compared to down-regulated proteins, although, in most of the datasets, the majority of these DEGs are down-regulated. We hope this study will help to build a relevant model to analyze the process of cancer progression in the human body.

Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins.

BACKGROUND: COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 15 million people have already been affected worldwide by COVID-19, resulting in more than 0.6 million deaths. Protein-protein interactions (PPIs) play a key role in the cellular process of SARS-CoV-2 virus infection in the human body. Recently a study has reported some SARS-CoV-2 proteins that interact with several human proteins while many potential interactions remain to be identified. METHOD: In this article, various machine learning models are built to predict the PPIs between the virus and human proteins that are further validated using biological experiments. The classification models are prepared based on different sequence-based features of human proteins like amino acid composition, pseudo amino acid composition, and conjoint triad. RESULT: We have built an ensemble voting classifier using SVMRadial, SVMPolynomial, and Random Forest technique that gives a greater accuracy, precision, specificity, recall, and F1 score compared to all other models used in the work. A total of 1326 potential human target proteins of SARS-CoV-2 have been predicted by the proposed ensemble model and validated using gene ontology and KEGG pathway enrichment analysis. Several repurposable drugs targeting the predicted interactions are also reported. CONCLUSION: This study may encourage the identification of potential targets for more effective anti-COVID drug discovery.

Health risk assessment of heavy metal accumulation in the Buriganga and Turag River systems for Puntius ticto, Heteropneustes fossilis, and Channa punctatus.

This study aimed to assess the effects of major ecotoxic heavy metals accumulated in the Buriganga and Turag River systems on the liver, kidney, intestine, and muscle of common edible fish species Puntius ticto, Heteropneustes fossilis, and Channa punctatus and determine the associated health risks. K was the predominant and reported as a major element. A large concentration of Zn was detected in diverse organs of the three edible fishes compared with other metals. Overall, trace metal analysis indicated that all organs (especially the liver and kidney) were under extreme threat because the maximum permissible limit set by different international health organizations was exceeded. The target hazard quotient and target cancer risk due to the trace metal content were the largest for P. ticto. Thus, excessive intake of P. ticto from the rivers Buriganga and Turag could result in chronic risks associated with long-term exposure to contaminants. Histopathological investigations revealed the first detectable indicators of infection and findings of long-term injury in cells, tissues, and organs. Histopathological changes in various tissue structures of fish functioned as key pointers of connection to pollutants, and definite infections and lesion types were established based on biotic pointers of toxic/carcinogenic effects. The analysis of histopathological alterations is a controlling integrative device used to assess pollutants in the environment.

Canonical PSO Based K-Means Clustering Approach for Real Datasets.

"Clustering" the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different types of indexes are used to solve different types of problems and indices selection depends on the kind of available data. This paper first proposes Canonical PSO based K-means clustering algorithm and also analyses some important clustering indices (intercluster, intracluster) and then evaluates the effects of those indices on real-time air pollution database, wholesale customer, wine, and vehicle datasets using typical K-means, Canonical PSO based K-means, simple PSO based K-means, DBSCAN, and Hierarchical clustering algorithms. This paper also describes the nature of the clusters and finally compares the performances of these clustering algorithms according to the validity assessment. It also defines which algorithm will be more desirable among all these algorithms to make proper compact clusters on this particular real life datasets. It actually deals with the behaviour of these clustering algorithms with respect to validation indexes and represents their results of evaluation in terms of mathematical and graphical forms.