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1 |
Intelligent and effective informatic deconvolution of “Big Data” and its future impact on the quantitative nature of neurodegenerative disease therapy
هوش انعطاف پذیر و موثر اطلاعاتی "داده های بزرگ" و تأثیر آن بر ماهیت کمی در درمان بیماری های نورودنژراتیک-2018 Biomedical data sets are becoming increasingly larger and a plethora of high-dimensionality data
sets (“Big Data”) are now freely accessible for neurodegenerative diseases, such as Alzheimer’s
disease. It is thus important that new informatic analysis platforms are developed that allow the
organization and interrogation of Big Data resources into a rational and actionable mechanism for
advanced therapeutic development. This will entail the generation of systems and tools that allow
the cross-platform correlation between data sets of distinct types, for example, transcriptomic, pro
teomic, and metabolomic. Here, we provide a comprehensive overview of the latest strategies,
including latent semantic analytics, topological data investigation, and deep learning techniques
that will drive the future development of diagnostic and therapeutic applications for Alzheimer’s dis
ease. We contend that diverse informatic “Big Data” platforms should be synergistically designed
with more advanced chemical/drug and cellular/tissue-based phenotypic analytical predictive models
to assist in either de novo drug design or effective drug repurposing.
Keywords: Big data; Informatics; High-dimensionality; Alzheimer’s disease; Aging; Molecular signature; Transcriptomics; Metabolomics; Proteomics; Genomics |
مقاله انگلیسی |
2 |
Integrity, standards, and QC-related issues with big data in pre-clinical drug discovery
یکپارچگی، استانداردها و مسائل مربوط به QC با داده های بزرگ در کشف داروهای پیش از درمان-2018 The tremendous expansion of data analytics and public and private big datasets presents an important oppor
tunity for pre-clinical drug discovery and development. In the field of life sciences, the growth of genetic,
genomic, transcriptomic and proteomic data is partly driven by a rapid decline in experimental costs as bio
technology improves throughput, scalability, and speed. Yet far too many researchers tend to underestimate the
challenges and consequences involving data integrity and quality standards. Given the effect of data integrity on
scientific interpretation, these issues have significant implications during preclinical drug development. We
describe standardized approaches for maximizing the utility of publicly available or privately generated bio
logical data and address some of the common pitfalls. We also discuss the increasing interest to integrate and
interpret cross-platform data. Principles outlined here should serve as a useful broad guide for existing analytical
practices and pipelines and as a tool for developing additional insights into therapeutics using big data.
Keywords: Big data ، Genomics ، Transcriptomics ، RNA-seq ، Microarray ، Exome |
مقاله انگلیسی |
3 |
Integration of gel-based and gel-free proteomic data for functional analysis of proteins through Soybean Proteome Database
ادغام داده های مبتنی بر ژل و عاری از ژل برای تحلیل عملکرد پروتئین ها از طریق پایگاه پروتئوم سویا-2017 The Soybean Proteome Database (SPD) stores data on soybean proteins obtained with gel-based and gel-free pro
teomic techniques. The database was constructed to provide information on proteins for functional analyses. The
majority of the data is focused on soybean (Glycine max ‘Enrei’). The growth and yield of soybean are strongly
affected by environmental stresses such as flooding. The database was originally constructed using data on soy
bean proteins separated by two-dimensional polyacrylamide gel electrophoresis, which is a gel-based proteomic
technique. Since 2015, the database has been expanded to incorporate data obtained by label-free mass
spectrometry-based quantitative proteomics, which is a gel-free proteomic technique. Here, the portions of the
database consisting of gel-free proteomic data are described. The gel-free proteomic database contains 39,212
proteins identified in 63 sample sets, such as temporal and organ-specific samples of soybean plants grown
under flooding stress or non-stressed conditions. In addition, data on organellar proteins identified in mitochon
dria, nuclei, and endoplasmic reticulum are stored. Furthermore, the database integrates multiple omics data
such as genomics, transcriptomics, metabolomics, and proteomics. The SPD database is accessible at http://
proteome.dc.affrc.go.jp/Soybean/.
Biological significance: The Soybean Proteome Database stores data obtained from both gel-based and gel-free
proteomic techniques. The gel-free proteomic database comprises 39,212 proteins identified in 63 sample sets,
such as different organs of soybean plants grown under flooding stress or non-stressed conditions in a time
dependent manner. In addition, organellar proteins identified in mitochondria, nuclei, and endoplasmic reticu
lum are stored in the gel-free proteomics database. A total of 44,704 proteins, including 5490 proteins identified
using a gel-based proteomic technique, are stored in the SPD. It accounts for approximately 80% of all predicted
proteins from genome sequences, though there are over lapped proteins. Based on the demonstrated application
of data stored in the database for functional analyses, it is suggested that these data will be useful for analyses of
biological mechanisms in soybean. Furthermore, coupled with recent advances in information and communica
tion technology, the usefulness of this database would increase in the analyses of biological mechanisms.
Keywords: Soybean | Database | Temporal-specific protein profile | Organ-specific protein profile | Proteomics | Abiotic stress |
مقاله انگلیسی |
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Navigating disease phenotypes: A multidimensional single-cell resolution compass leads the way
ردیابی فنوتیپ های بیماری: قطب نمای یکپارچه تک سلولی چند بعدی منجر به راه-2017 Cellular phenotyping, in particular immune cell phenotyping,
has become an integral part of personalized and stratified
medicine approaches in order to facilitate classification of pa
tient cohorts according to proteomic, transcriptomic or
genomic information, with the ultimate goal to increase treat
ment efficiency and outcome. However, choosing the optimal
and most informative phenotyping approach to discover novel
and predictive biomarkers for patient cohorts has become a
major challenge and greatly hampers knowledge gain to suc
cessfully develop and tailor new and existing therapies to
suitable patient collectives [1]. Recent technological in
novations, such as single-cell proteomics (Mass Cytometry)
and single-cell transcriptomics have become available which
possess the power to measure thousands of features for
thousands to millions of cells in parallel, thereby allowing the
deep characterization of complex cellular networks in ho
meostasis as well as perturbations under disease conditions
[2]. These multidimensional approaches dramatically accel
erate the discovery of novel biomarkers for disease prediction
and progression within personalized medicine approaches.
These approaches now allow for the characterization of small
amounts of patient material both on the protein and on the
transcriptome level to allow for an unbiased, high-dimensional,
and bioinformatically supported systems biology approach
which enables discovery, design and implementation of novel
biomarkers into the clinical routine in a rapid fashion. In this
review, we will discuss the available technologies and recent
applications and scientific advances enabled by these tech
nologies highlighting our view of how to integrate these tech
nologies into translational research to achieve a more reliable,
more rapid and better informed approach to molecular
phenotyping ultimately achieving the level of knowledge
needed to implement personalized medicine approaches for a
wider patient base.
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مقاله انگلیسی |
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ماتریس همبستگی برای اﻃﻼﻋﺎت ﺑﺎ ﺑُﻌﺪ ﺑﺎﻻ: ضریبRV تعدیل یافته
سال انتشار: 2009 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 19 انگیزه: ژنومیک تابعی مدرن، مجموعه اﻃﻼﻋﺎتی ﺑﺎ ﺑُﻌﺪ ﺑﺎﻻ ایجاد می نماید. اغلب دارای یک عدد ساده می باشد که ارتباط بین جفت های مجموعه اطلاعات با بعد بالا را به شیوه ای جامع مشخص می نماید. چنین اعداد همبستگی های ماتریس می باشند و به این دلیل جالب می باشند که می توان آنها به شیوه همبستگی های پیرسون که برای زیست شناسان آشناست، تفسیر نمود. با این حال، ابعاد بالای اطلاعات ژنومیک تابعی، برای همبستگی ماتریس موجود مشکل ساز می باشد. انگیزه این مقاله 2 برابر می باشد: (1) ما ایده همبستگی های ماتریس را برای بیو انفورماتیک معرفی می کنیم و (2) ما ضریب همبستگی ماتریس (ضریب RV) را بهبود می بخشیم و مشکلات اطلاعات با بعد بالا را دور می زنیم.
نتایج: می توان از ضریبRV تعدیل یافته در مطالعات تجزیه و تحلیل اطلاعات با ابعاد بالا به عنوان سنجشی آسان برای اطلاعات مشترک در دو مجموعه داده استفاده نمود. این امر با استدلال ها، شبیه سازی ها و برنامه های نظری در دو مثال واقعی از ژنومیک تابعی، یعنی مثال ترانسکریپتومیک و متابولومیک نشان داده شده است.
دسترس پذیری: فایل های m نرم افزار متلب در روش های ارائه شده را می توان از http://www.bdagroup.nl دریافت نمود.
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مقاله ترجمه شده |