MicroRNAs (miRNAs) are endogenous ∼23 nt RNAs that play important gene-regulatory roles in animals and plants by pairing to the mRNAs of protein-coding genes to direct their posttranscriptional repression. This review outlines the current understanding of miRNA target recognition in animals and discusses the widespread impact of miRNAs on both the expression and evolution of protein-coding genes.

MicroRNAs: Target Recognition and Regulatory Functions

With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies, and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of different patient populations and these records are difficult to share across hospitals because of their sensitive nature. This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, "big data." Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. The goal of this survey is to provide a review for federated learning technologies, particularly within the biomedical space. In particular, we summarize the general solutions to the statistical challenges, system challenges, and privacy issues in federated learning, and point out the implications and potentials in healthcare.

/pdf/federated-learning-for-healthcare-informatics-10ealruc85.pdf

Federated Learning for Healthcare Informatics

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use-cases. FL can be applicable to multiple domains but applying it to different industries has its own set of obstacles. FL is known as collaborative learning, where algorithm(s) get trained across multiple devices or servers with decentralized data samples without having to exchange the actual data. This approach is radically different from other more established techniques such as getting the data samples uploaded to servers or having data in some form of distributed infrastructure. FL on the other hand generates more robust models without sharing data, leading to privacy-preserved solutions with higher security and access privileges to data. This paper starts by providing an overview of FL. Then, it gives an overview of technical details that pertain to FL enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL to enable data scientists to build better privacy-preserving solutions for industries in critical need of FL. We also provide an overview of key challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore both the challenges and advantages of FL and present detailed service use-cases to illustrate how different architectures and protocols that use FL can fit together to deliver desired results.

/pdf/federated-learning-a-survey-on-enabling-technologies-23luelc16c.pdf

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications.

Small non-coding RNAs, in particular microRNAs(miRNAs), regulate fine-tuning of gene expression and can act as oncogenes or tumor suppressor genes. Differential miRNA expression has been reported to be of functional relevance for tumor biology. Using next-generation sequencing, the unbiased and absolute quantification of the small RNA transcriptome is now feasible. Neuroblastoma(NB) is an embryonal tumor with highly variable clinical course. We analyzed the small RNA transcriptomes of five favorable and five unfavorable NBs using SOLiD next-generation sequencing, generating a total of >188 000 000 reads. MiRNA expression profiles obtained by deep sequencing correlated well with real-time PCR data. Cluster analysis differentiated between favorable and unfavorable NBs, and the miRNA transcriptomes of these two groups were significantly different. Oncogenic miRNAs of the miR17-92 cluster and the miR-181 family were overexpressed in unfavorable NBs. In contrast, the putative tumor suppressive microRNAs, miR-542-5p and miR-628, were expressed in favorable NBs and virtually absent in unfavorable NBs. In-depth sequence analysis revealed extensive post-transcriptional miRNA editing. Of 13 identified novel miRNAs, three were further analyzed, and expression could be confirmed in a cohort of 70 NBs.

Deep sequencing reveals differential expression of microRNAs in favourable versus unfavourable neuroblastoma

The Internet of Things (IoT) applications have grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e., cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data are processed for machine learning analysis and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.

Machine learning and data analytics for the IoT

‘Big data’ is massive amounts of information that can work wonders. It has become a topic of special interest for the past two decades because of a great potential that is hidden in it. Various public and private sector industries generate, store, and analyze big data with an aim to improve the services they provide. In the healthcare industry, various sources for big data include hospital records, medical records of patients, results of medical examinations, and devices that are a part of internet of things. Biomedical research also generates a significant portion of big data relevant to public healthcare. This data requires proper management and analysis in order to derive meaningful information. Otherwise, seeking solution by analyzing big data quickly becomes comparable to finding a needle in the haystack. There are various challenges associated with each step of handling big data which can only be surpassed by using high-end computing solutions for big data analysis. That is why, to provide relevant solutions for improving public health, healthcare providers are required to be fully equipped with appropriate infrastructure to systematically generate and analyze big data. An efficient management, analysis, and interpretation of big data can change the game by opening new avenues for modern healthcare. That is exactly why various industries, including the healthcare industry, are taking vigorous steps to convert this potential into better services and financial advantages. With a strong integration of biomedical and healthcare data, modern healthcare organizations can possibly revolutionize the medical therapies and personalized medicine.

/pdf/big-data-in-healthcare-management-analysis-and-future-25zzu17xyj.pdf

Mohit Sharma

Papers

Big data in healthcare: management, analysis and future prospects

Bioinformatics and Its Application Areas

DNA-RNA Barcoding and Gene Sequencing

DNA Microarray Analysis: From Affymetrix CEL Files to Comparative Gene Expression

Bioinformatics in Cancer Detection