Search

In the present day, there are many diseases which need to be identified at their early stages to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason, there is a need of analysing complex medical data, medical reports, and medical images at a lesser time but with greater accuracy. There are even some instances where certain abnormalities cannot be directly recognized by humans. In healthcare for computational decision making, machine learning approaches are being used in these types of situations where a crucial data analysis needs to be performed on medical data to reveal hidden relationships or abnormalities which are not visible to humans. Implementing algorithms to perform such tasks itself is difficult, but what makes it even more challenging is to increase the accuracy of the algorithm while decreasing the required time for the algorithm to execute. In the early days, processing of large amount of medical data was an important task which resulted in machine learning being adapted in the biological domain. Since this happened, the biology and biomedical fields have been reaching higher levels by exploring more knowledge and identifying relationships which were never observed before. Reaching to its peak now the concern is being diverted towards treating patients not only based on the type of disease but also their genetics, which is known as precision medicine. Modifications in machine learning algorithms are being performed and tested daily to improve the performance of the algorithms in analysing and presenting more accurate information. In the healthcare field, starting from information extraction from medical documents until the prediction or diagnosis of a disease, machine learning has been involved. Medical imaging is a section that was greatly improved with the integration of machine learning algorithms to the field of computational biology. Nowadays, many disease diagnoses are being performed by medical image processing using machine learning algorithms. In addition, patient care, resource allocation, and research on treatments for various diseases are also being performed using machine learning-based computational decision making. Throughout this paper, various machine learning algorithms and approaches that are being used for decision making in the healthcare sector will be discussed along with the involvement of machine learning in healthcare applications in the current context. With the explored knowledge, it was evident that neural network-based deep learning methods have performed extremely well in the field of computational biology with the support of the high processing power of modern sophisticated computers and are being extensively applied because of their high predicting accuracy and reliability. When giving concern towards the big picture by combining the observations, it is noticeable that computational biology and biomedicine-based decision making in healthcare have now become dependent on machine learning algorithms, and thus they cannot be separated from the field of artificial intelligence. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases result in millions of deaths around the globe annually, most of which are avoidable if identified early. Preventive healthcare has a major role in the fight against cardiovascular diseases. Primary, secondary, and tertiary prevention have their own applications along with benefits and drawbacks. This paper aims to elevate the sensitivity of “secondary prevention of cardiovascular diseases.” Firstly, it discusses common types of cardiovascular diseases around the globe and their causes. Secondly, it analyzes different risk factors associated with cardiovascular diseases and then discusses incoming technological trends in cardiovascular disease prediction and finally provides an insight into the importance of secondary prevention of cardiovascular diseases and commonly prescribed interventions for high risk patients. [ABSTRACT FROM AUTHOR]

Diseases resulting from alterations in gene expressions through mutations in the genes or through changes in the gene expression regulation could be identified through the analysis of RNA expressions. ncRNAs play a significant role in regulation of the gene expression by controlling the expression levels of the coding RNAs and other cellular processes. Discoveries have shown that the human genome is encoded with sequences responsible for the transcription of thousands of ncRNAs. Even though the studies conducted on ncRNAs are still at initial stages, facts established so far display biomarkers that confirm their relationship with certain diseases such as cancers, cardiovascular diseases, and insulin resistance. These studies have been facilitated with high throughput modern sequencing techniques such as microarrays and RNA sequencing. The data obtained through the above analysis are processed with the aid of existing databases, to deduce conclusions on different diagnostic biomarkers and therapeutic targets for specific diseases. This review focuses on the association of ncRNAs in disease prediction, focusing mainly on cardiovascular diseases and disorders caused by insulin resistance. The report also analyzes regulatory functions of ncRNAs and novel approaches used in disease therapeutics. [ABSTRACT FROM AUTHOR]

With the exponential growth in the capacity of information generated and the emerging need for data to be stored for prolonged period of time, there emerges a need for a storage medium with high capacity, high storage density, and possibility to withstand extreme environmental conditions. DNA emerges as the prospective medium for data storage with its striking features. Diverse encoding models for reading and writing data onto DNA, codes for encrypting data which addresses issues of error generation, and approaches for developing codons and storage styles have been developed over the recent past. DNA has been identified as a potential medium for secret writing, which achieves the way towards DNA cryptography and stenography. DNA utilized as an organic memory device along with big data storage and analytics in DNA has paved the way towards DNA computing for solving computational problems. This paper critically analyzes the various methods used for encoding and encrypting data onto DNA while identifying the advantages and capability of every scheme to overcome the drawbacks identified priorly. Cryptography and stenography techniques have been analyzed in a critical approach while identifying the limitations of each method. This paper also identifies the advantages and limitations of DNA as a memory device and memory applications. [ABSTRACT FROM AUTHOR]

Predicting disease genes for a particular genetic disease is very challenging in bioinformatics. Based on current research studies, this challenge can be tackled via network-based approaches. Furthermore, it has been highlighted that it is necessary to consider disease similarity along with the protein’s proximity to disease genes in a protein-protein interaction (PPI) network in order to improve the accuracy of disease gene prioritization. In this study we propose a new algorithm called proximity disease similarity algorithm (ProSim), which takes both of the aforementioned properties into consideration, to prioritize disease genes. To illustrate the proposed algorithm, we have conducted six case studies, namely, prostate cancer, Alzheimer’s disease, diabetes mellitus type 2, breast cancer, colorectal cancer, and lung cancer. We employed leave-one-out cross validation, mean enrichment, tenfold cross validation, and ROC curves to evaluate our proposed method and other existing methods. The results show that our proposed method outperforms existing methods such as PRINCE, RWR, and DADA. [ABSTRACT FROM AUTHOR]

With the knowledge of molecular biology it is highlighting that long non-coding RNAs (lncRNAs) play a critical role in many important biological processes, such as imprinting control, cell differentiation, immune responses, human diseases, tumorigenesis and other biological processes. This study proposes a novel computational method, named KRWRH, to infer disease-lincRNA associations with the influence of phenotype information and tissue expression details of lincRNA. Gaussian interaction profile kernel is calculated for diseases and lincRNAs and random walk with restart method is used for final prediction. The proposed method KRWRH is compared with four existing methods: LRLSLDA, TslncRNA, NRWRH, and RWRH. The experimental results based on the leave-one-out cross validation, ROC curves, and mean enrichment show that the proposed method KRWRH is able to predict known and unknown disease-lincRNA associations more effectively. [ABSTRACT FROM PUBLISHER]

Background: Tissue specificity is an important aspect of many genetic diseases in the context of genetic disorders as the disorder affects only few tissues. Therefore tissue specificity is important in identifying disease-gene associations. Hence this paper seeks to discuss the impact of using tissue specificity in predicting new disease-gene associations and how to use tissue specificity along with phenotype information for a particular disease. Methods: In order to find out the impact of using tissue specificity for predicting new disease-gene associations, this study proposes a novel method called tissue-specified genes to construct tissues-specific gene-gene networks for different tissue samples. Subsequently, these networks are used with phenotype details to predict disease genes by using Katz method. The proposed method was compared with three other tissue-specific network construction methods in order to check its effectiveness. Furthermore, to check the possibility of using tissue-specific gene-gene network instead of generic protein-protein network at all time, the results are compared with three other methods. Results: In terms of leave-one-out cross validation, calculation of the mean enrichment and ROC curves indicate that the proposed approach outperforms existing network construction methods. Furthermore tissues-specific gene-gene networks make a more positive impact on predicting disease-gene associations than generic proteinprotein interaction networks. Conclusions: In conclusion by integrating tissue-specific data it enabled prediction of known and unknown disease-gene associations for a particular disease more effectively. Hence it is better to use tissue-specific genegene network whenever possible. In addition the proposed method is a better way of constructing tissue-specific gene-gene networks. [ABSTRACT FROM AUTHOR]