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In this paper, we explore the use of statistical learning approaches to predict drug-target like proteins from their primary sequences in order to facilitate the rapid discovery of new potential therapeutic targets from the large quantity of sequences in human genome. It was found that the Support Vector Machine (SVM) algorithm with a fine-tuned Gaussian kernel was able to make reasonably accurate prediction, which showed its potential to be used in the genome scale rapid drug target discovery, as a novel in silico approach supplementary to the conventional experimental approaches.

The task of classifying news manually requires in-depth knowledge of the domain and expertise to spot anomalies in the text. During this research, we discussed the matter of classifying fake news articles using machine learning models and ensemble techniques. The info we used in our work is collected from the World Wide Web and contains news articles from various domains to cover most of the news rather than specifically classifying political news. The first aim of the research is to identify patterns in text that differentiate fake articles from true news. Within the proposed system we will extract different textual features from the articles using a machine learning tool and used the feature set as an input to the models. the training models were trained and parameter-tuned to obtain optimal accuracy. Some models have achieved relatively higher correctness than others. we'll use multiple performance metrics to compare the results for each algorithm. The ensemble learners have shown an whole better score on all presentation metrics as related to the separable beginners.