Informatics Publishing Limited

S. Ramraj ¹, Soumyajyoti Bhattachary ¹, Karthikeyan ¹, Gohula Krishnan ¹

Affiliations
1 SRM University, Kancheepuram _603203, Chennai, Tamil Nadu, IN

Abstract

Objectives: The paper documents a comparative analytical study of the two prominent De Novo Algorithms (DNA) namely Velvet and SSAKE, both are being pipelined by CloudRS. Methods/Statistical Analysis: The Research process conducted in this project primarily utilized Next-Generation Sequencing data results. These data sets were further error corrected by pipelining them with CloudRS. Upon error correction, the data sets were assembled separately by VELVET and SSAKE; the data from the analysis were then analyzed as per the mathematical results produced in order to statistically compare the two algorithms for a similar environment. Findings: On assembling the error corrected genome, the data produced sets of values. These values were tabulated and noted in order to ensure effective comparison. The values being compared were the N50 and corrected lengths of the assembled genes. The general genome analysis comparison metrics were then utilized to compare the documented data. This showed that a higher N50 value with a better assembled error corrected length read ensured more effectiveness of an algorithm. This result allowed for the first comparison between two prominent DNA algorithms, which hadn’t been compared before, to ensure better understanding Applications/Improvements: The applications of these results are endless, primarily, to ensure that work which involves assembled genome reads proceed with the utmost effectiveness. Any further improved algorithms, if created down the line, can aid in improving the entire process of the same. Thus, in the uniqueness of the results lies the novelty of the entire project.

Keywords

De Novo assembly, De Bruijn graphs, ReadStack algorithm, Map Reduce, Hadoop, ALLPATHS-LG, CloudRS, VELVET, SSAKE, comparison.

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Nimai Chand Das Adhikari ¹, Arpana Alka ¹, Vamshi Kumar Kurva ¹, S. Suhas ¹, Hitesh Nayak ¹, Rishav Kumar ¹, Ashish Kumar Nayak ¹, Sankalp Kumar Nayak ¹, Vaisakh Shaj ¹, Karthikeyan ¹, Srikant Nayak ¹

Affiliations
1 Analytic Labs Research Group, IN

Abstract

Intelligent Models for predicting diseases whether building a model to help the doctor or even preventing its spread in an area globally, is increasing day by day. Here we present a noble approach to predict the disease prone area using the power of Text Analysis and Machine Learning. Epidemic Search model using the power of the social network data analysis and then using this data to provide a probability score of the spread and to analyse the areas whether going to suffer from any epidemic spread-out, is the main focus of this work. We have tried to analyse and showcase how the model with different kinds of pre-processing and algorithms predict the output. We have used the combination of words-n grams, word embeddings and TFIDF with different data mining and deep learning algorithms like SVM, Naïve Bayes and RNN-LSTM. Naïve Bayes with TF-IDF performed better in comparison to others.

Keywords

Natural Language Processing, Text Mining, Text Analysis, Support Vector Machines, LSTM, Naive Bayes, Text Blob, Tweet Sentiment Analysis.

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P. Shreya Rajkumar ¹, C. T. Darwin ¹, Karthikeyan ²

Affiliations
1 Sree Balaji Medical College and Hospital, Bharath Institute of Higher Education and Research, Chennai-73, IN
2 Department of General Surgery, Sree Balaji Medical College and Hospital, Bharath Institute of Higher Education and Research, Chennai-73, IN

Abstract

Breast malignancy has fast overtaken ovarian malignancy as the leading cause of death in women. A number of factors attribute to the rise in breast cancer,including obesity, food habits and carcinogen exposure.

This article is a review of breast malignancy, and its pathological nature,and highlights the significance of axillary staging as a prognostic factor,and hence the importance of axillary investigation.

Keywords

Malignancy, Prognostic.

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