Literature DB >> 29993643

Why Deep Learning Is Changing the Way to Approach NGS Data Processing: A Review.

Fabrizio Celesti, Antonio Celesti, Jiafu Wan, Massimo Villari.   

Abstract

Nowadays, big data analytics in genomics is an emerging research topic. In fact, the large amount of genomics data originated by emerging next-generation sequencing (NGS) techniques requires more and more fast and sophisticated algorithms. In this context, deep learning is re-emerging as a possible approach to speed up the DNA sequencing process. In this review, we specifically discuss such a trend. In particular, starting from an analysis of the interest of the Internet community in both NGS and deep learning, we present a taxonomic analysis highlighting the major software solutions based on deep learning algorithms available for each specific NGS application field. We discuss future challenges in the perspective of cloud computing services aimed at deep learning based solutions for NGS.

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Year:  2018        PMID: 29993643     DOI: 10.1109/RBME.2018.2825987

Source DB:  PubMed          Journal:  IEEE Rev Biomed Eng        ISSN: 1937-3333


  3 in total

1.  How Can Law and Policy Advance Quality in Genomic Analysis and Interpretation for Clinical Care?

Authors:  Barbara J Evans; Gail Javitt; Ralph Hall; Megan Robertson; Pilar Ossorio; Susan M Wolf; Thomas Morgan; Ellen Wright Clayton
Journal:  J Law Med Ethics       Date:  2020-03       Impact factor: 1.718

2.  Privacy and ethical challenges in next-generation sequencing.

Authors:  Nicole Martinez-Martin; David Magnus
Journal:  Expert Rev Precis Med Drug Dev       Date:  2019-04-08

3.  Multiplexed molecular profiling of lung cancer with malignant pleural effusion using next generation sequencing in Chinese patients.

Authors:  Xingya Ruan; Yonghua Sun; Wei Wang; Jianwei Ye; Daoyun Zhang; Ziying Gong; Mingxia Yang
Journal:  Oncol Lett       Date:  2020-03-05       Impact factor: 2.967

  3 in total

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