Ramkumar T

Bio: Ramkumar T is an academic researcher. The author has contributed to research in topics: Computational intelligence & Protein structure prediction. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

Distributed Machine Learning Algorithms to classify Protein secondary structures for Drug Design – A Survey

Abstract: The arena of secondary structure based drug design is an evolving area in recent years. Structures help us to understand how the protein functions, and will help to design chemicals to be used pharmaceutically as modifiers of enzyme activity. X-Ray structures usually provide us with a quite static picture of the protein which is considered as expensive and time consuming. The recent modern drug design and development using knowledge of proteomics must rely on computational intelligence based machine learning model structures using efficient structure prediction techniques. In recent years, Machine learning, emerging on the basis of parallel and distributed computing for handling big data, is making huge advances in many areas. In this paper, we have captured a comprehensive review of protein structure prediction methods using Machine Learning approach in Distributed environment. Overall, the results are good and depicts that the accuracy and performance of protein secondary structure prediction methods are achieved using Machine Learning techniques and these would become a powerful aid, while implemented in Distributed environment like Hadoop or Spark. This research will be helpful for the recent medicine researchers, which aids in understanding the relation between protein sequences, structure and thereby determine the function to develop various drugs and designing novel enzymes and this is considered as one of the major focused areas in recent bioinformatics research.

A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems

Abstract: The objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining and machine learning to healthcare engineering systems.,A systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors and content.,From the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field.,The use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors' previous knowledge and the nature of the publications were used to select different platforms.,To the best of the authors' knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining and machine learning applied to healthcare engineering systems.

Prediction of Chronic and Infectious Diseases using Machine Learning Classifiers- A Systematic Approach

Abstract: Infectious and chronic diseases devastate millions of people across the world each year. Nonetheless, each type of disease substantiates differently. According to the National Centre for Health Statistics, USA, Infectious diseases or communicable diseases are the ones based on the cause, which spreads from person to person or animal to person caused by microorganisms such as bacteria or parasite and can be cured. Chronic diseases are based on the effect, which may have the origin of infectious disease, prolonged to three or more months, doesn’t spread from one person to another and cannot be cured. Some chronic diseases such as cervical cancer and liver cancer have originated from infectious diseases such as human papillomavirus (HPV) and hepatitis B, C virus. This paper focuses on various machine learning classification techniques in predicting chronic diseases such as Cardio Vascular Disease (CVD), Chronic Kidney Disease (CKD), lung cancer, and infectious diseases such as hepatitis and dengue serotypes. In the analysis, ABC4.5 classifier outperformed with accuracy of 92.76 % than the other classifiers in predicting Chronic Kidney Disease (CKD), Random Forest classifier achieved an accuracy of 90.32% which is higher than Logistic regression of accuracy 83.87% in predicting hepatitis. Hoeffding classifier achieves an accuracy of 88.56% which is higher than the other classifier in predicting Cardio Vascular Disease. Multi swarm optimized Multilayer perceptron achieved an accuracy of 85.18% which is higher than the particle swarmed optimized multilayer perceptron in predicting dengue serotypes. Artificial Neural Network (ANN) classifier outperformed all the classifiers under analysis with an accuracy of 93.00 % in predicting lung cancer.

Combination Vs. Multi-target drugs: The Clash of the titans in the arena of drug discovery; An overview and in silico evaluation

Abstract: Over the last few decades, either the single-pill therapy or the combinations of pill therapy were at the cutting edge in drug design and development processes, which not only proved to have treated innumerable complications but also have proved its drastic need in the past and even in the present watch. This review has a briefing on the comparison of the different aspects of prescriptions as polypharmacy, combination therapy along with multi targets drugs. As the time passed, the need for an alternative to overcome the several possible ill-effects like drug-drug interaction, non-adherence, fixed-dose medications for all patients, etc., also have paved the way to showcase the in-efficiency in treating the complex nexus cascade that has raised a thought amongst for a newer brand new perspective to take a turn and transmute the concept of drug design. From a single targeted therapy to a multi-targeted approach in this drug design pattern. It also mentions the colligation of different pharmacophores entities to obtain a better potent drug with better ADMET. An overview of how 'efficient, relevant, and important multi-target drug therapy would be over a combination of pills' further have been sketched and depicted based on past prototype examples and present scenarios. The feasibility of the concept was investigated by an in silico approach where a colligated molecule of tranexamic acid and Meloxicam was analysed for its binding affinity towards the parent receptors. The newly designed, the colligated molecule was found to be more active than the parent drugs to multiple targets. The evaluation of the concept leads to a new horizon of MTD, which can be adopted through several approaches, including the colligation of the already existing drugs.

An approach for Protein Secondary Structure prediction using prediction-based language models

Abstract: Prediction based language models are considered as one of the major concepts in Natural Language processing which gains knowledge from unstructured text data. Extracting insights from sequential data such as biological sequences is an important problem in genomics, proteomics and classifying the secondary structures of protein, helps the researchers in aiding to understand protein functions. This is considered as one of the important preliminaries of Drug development. Traditional techniques such as sequential models, probabilistic techniques and statistical approaches were widely applied in structure prediction which extracts insights from sequence of amino acid. However, handheld feature extraction becomes a tedious task, which eventually leads to less accuracy. Our novel approach creates vectors using word embeddings which is assumed to consider contextual information of amino acids thereby improving the accuracy of secondary structure prediction approach. This is considered as an optimistic solution for secondary structure prediction problem. In this approach a variation of word embeddings - Continuous Bag of Words (CBOW) method is proposed which retains the sequential information of all amino acid in the protein chain. This vector is used as input features of Deep Neural Network classifier and class labels are classified into Helix, Sheet, Coil. We have tested this NLP based approach on GenBank dataset. The infrastructure required for this analysis was leveraged from Google Colab.

An approach to predict protein secondary structure using Deep Learning in Spark based Big Data computing framework

Abstract: Computational Intelligence techniques are playing and continue to play a major role in Data Analytics including gaining knowledge from unstructured sequence data. Identifying and transforming valuable insights from biological sequences is an important problem in Computational Biology and classifying the structure of the protein, especially secondary structures from its sequence is crucial as the structure in turn identifies the function, which is considered as an important problem definition in proteomics. Earlier approaches such as Machine Learning, Statistical and Probabilistic techniques were widely applied in proteomics to extract knowledge from amino acid sequence. However, handmade feature extraction becomes a tiresome task, which may degrade the accuracy. Our approach focuses on Deep Learning implemented in Distributed Framework for improved accuracy and performance, which provides an efficient solution for structure prediction problem. We used Skip-Gram method to translate the amino acid sequence into words, without losing the position information of each amino acid. This vector is then fed into Stacked Auto Encoder for classification and the classifier output predicts the presence of secondary structures. This approach is tested on GenBank proteins and the entire experiment is implemented in SPARK framework.