ABSTRACT
Machine and deep learning are used worldwide. Machine Learning (ML) and Deep Learning (DL) are playing an increasingly important role in the healthcare sector, particularly when combined with big data analytics. Some of the ways that ML and DL are being used in healthcare include Predictive Analytics, Medical Image Analysis, Drug Discovery, Personalized Medicine, and Electronic Health Records (EHR) Analysis. It has become one of the advanced and popular tool for computer science domain.' The advancement of ML and DL for various fields has opened new avenues for research and development. It could revolutionize prediction and decision-making capabilities. Due to increased awareness about the ML and DL in the healthcare, it has become one of the vital approaches for the sector. High-volume of unstructured, and complex medical imaging data from health monitoring devices, gadgets, sensors, etc. Is the biggest trouble for healthcare sector. The current study uses analysis to examine research trends in adoption of machine learning and deep learning approaches in the healthcare sector. The WoS database for SCI/SCI-E/ESCI journals are used as the datasets for the comprehensive analysis. Apart from these various search strategy are utilised for the requisite scientific analysis of the extracted research documents. Bibliometrics R statistical analysis is performed for year-wise, nation-wise, affiliation-wise, research area, sources, documents, and author based analysis. VOS viewer software is used to create author, source, country, institution, global cooperation, citation, co-citation, and trending term co-occurrence networks. ML and DL, combined with big data analytics, have the potential to revolutionize healthcare by improving patient outcomes, reducing costs, and accelerating the development of new treatments, so the current study will help academics, researchers, decision-makers, and healthcare professionals understand and direct research.
ABSTRACT
Water buffalo (Bubalus bubalis), belonging to the Bovidae family, is an economically important animal as it is the major source of milk, meat, and drought in numerous countries. It is mainly distributed in tropical and subtropical regions with a global population of approximately 202 million. The advent of low cost and rapid sequencing technologies has opened a new vista for global buffalo researchers. In this study, we utilized the genomic data of five commercially important buffalo breeds, distributed globally, namely, Mediterranean, Egyptian, Bangladesh, Jaffrarabadi, and Murrah. Since there is no whole-genome sequence analysis of these five distinct buffalo breeds, which represent a highly diverse ecosystem, we made an attempt for the same. We report the first comprehensive, holistic, and user-friendly web genomic resource of buffalo (BuffGR) accessible at http://backlin.cabgrid.res.in/buffgr/, that catalogues 6028881 SNPs and 613403 InDels extracted from a set of 31 buffalo tissues. We found a total of 7727122 SNPs and 634124 InDels distributed in four breeds of buffalo (Murrah, Bangladesh, Jaffarabadi, and Egyptian) with reference to the Mediterranean breed. It also houses 4504691 SSR markers from all the breeds along with 1458 unique circRNAs, 37712 lncRNAs, and 938 miRNAs. This comprehensive web resource can be widely used by buffalo researchers across the globe for use of markers in marker trait association, genetic diversity among the different breeds of buffalo, use of ncRNAs as regulatory molecules, post-transcriptional regulations, and role in various diseases/stresses. These SNPs and InDelscan also be used as biomarkers to address adulteration and traceability. This resource can also be useful in buffalo improvement programs and disease/breed management.
ABSTRACT
Water buffalo (Bubalus bubalis) are an important animal resource that contributes milk, meat, leather, dairy products, and power for plowing and transport. However, mastitis, a bacterial disease affecting milk production and reproduction efficiency, is most prevalent in populations having intensive selection for higher milk yield, especially where the inbreeding level is also high. Climate change and poor hygiene management practices further complicate the issue. The management of this disease faces major challenges, like antibiotic resistance, maximum residue level, horizontal gene transfer, and limited success in resistance breeding. Bovine mastitis genome wide association studies have had limited success due to breed differences, sample sizes, and minor allele frequency, lowering the power to detect the diseases associated with SNPs. In this work, we focused on the application of targeted gene panels (TGPs) in screening for candidate gene association analysis, and how this approach overcomes the limitation of genome wide association studies. This work will facilitate the targeted sequencing of buffalo genomic regions with high depth coverage required to mine the extremely rare variants potentially associated with buffalo mastitis. Although the whole genome assembly of water buffalo is available, neither mastitis genes are predicted nor TGP in the form of web-genomic resources are available for future variant mining and association studies. Out of the 129 mastitis associated genes of cattle, 101 were completely mapped on the buffalo genome to make TGP. This further helped in identifying rare variants in water buffalo. Eighty-five genes were validated in the buffalo gene expression atlas, with the RNA-Seq data of 50 tissues. The functions of 97 genes were predicted, revealing 225 pathways. The mastitis proteins were used for protein-protein interaction network analysis to obtain additional cross-talking proteins. A total of 1,306 SNPs and 152 indels were identified from 101 genes. Water Buffalo-MSTdb was developed with 3-tier architecture to retrieve mastitis associated genes having genomic coordinates with chromosomal details for TGP sequencing for mining of minor alleles for further association studies. Lastly, a web-genomic resource was made available to mine variants of targeted gene panels in buffalo for mastitis resistance breeding in an endeavor to ensure improved productivity and the reproductive efficiency of water buffalo.
