scholarly journals High-throughput functional annotation and data mining with the Blast2GO suite

2008 ◽  
Vol 36 (10) ◽  
pp. 3420-3435 ◽  
Author(s):  
S. Gotz ◽  
J. M. Garcia-Gomez ◽  
J. Terol ◽  
T. D. Williams ◽  
S. H. Nagaraj ◽  
...  
Keyword(s):  
Data Mining ◽  
High Throughput ◽  
Functional Annotation

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
Data Mining ◽  
Refractive Index ◽  
High Throughput ◽  
First Principles ◽  
High Throughput Screening ◽  
Large Scale ◽  
Computational Study ◽  
High Refractive Index ◽  
Structural Features ◽  
Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

Deriving Knowledge through Data Mining High-Throughput Screening Data

2004 ◽  
Vol 47 (25) ◽  
pp. 6373-6383 ◽  
Author(s):  
David J. Diller ◽  
Doug W. Hobbs
Keyword(s):  
Data Mining ◽  
High Throughput ◽  
High Throughput Screening

High-throughput and data mining with ab initio methods

2004 ◽  
Vol 16 (1) ◽  
pp. 296-301 ◽  
Author(s):  
Dane Morgan ◽  
Gerbrand Ceder ◽  
Stefano Curtarolo
Keyword(s):  
Data Mining ◽  
Ab Initio ◽  
High Throughput ◽  
Ab Initio Methods

scholarly journals Genomic data mining for functional annotation of human long noncoding RNAs

2019 ◽  
Vol 20 (6) ◽  
pp. 476-487 ◽  
Author(s):  
Brian L. Gudenas ◽  
Jun Wang ◽  
Shu-zhen Kuang ◽  
An-qi Wei ◽  
Steven B. Cogill ◽  
...  
Keyword(s):  
Data Mining ◽  
Functional Annotation ◽  
Noncoding Rnas ◽  
Genomic Data ◽  
Long Noncoding Rnas ◽  
Genomic Data Mining

scholarly journals dbCRSR: a manually curated database for regulation of cancer radiosensitivity

Database ◽  
2018 ◽  
Vol 2018 ◽  
Author(s):  
Pengbo Wen ◽  
Junfeng Xia ◽  
Xianbin Cao ◽  
Bin Chen ◽  
Yinping Tao ◽  
...  
Keyword(s):  
Data Mining ◽  
Functional Annotation ◽  
Relevant Literature ◽  
Chemical Compounds ◽  
Multiple Strategies ◽  
The Past ◽  
Intrinsic Radiosensitivity ◽  
Pubmed Database ◽  
Non Coding Rnas ◽  
Radiotherapy Treatment

AbstractRadiotherapy is used to treat approximately 50% of all cancer patients, with varying prognoses. Intrinsic radiosensitivity is an important factor underlying the radiotherapeutic efficacy of this precise treatment. During the past decades, great efforts have been made to improve radiotherapy treatment through multiple strategies. However, invaluable data remains buried in the extensive radiotherapy literature, making it difficult to obtain an overall view of the detailed mechanisms leading to radiosensitivity, thus limiting advances in radiotherapy. To address this issue, we collected data from the relevant literature contained in the PubMed database and developed a literature-based database that we term the cancer radiosensitivity regulation factors database (dbCRSR). dbCRSR is a manually curated catalogue of radiosensitivity, containing multiple radiosensitivity regulation factors (395 coding genes, 119 non-coding RNAs and 306 chemical compounds) with appropriate annotation. To illustrate the value of the data we collected, data mining was performed including functional annotation and network analysis. In summary, dbCRSR is the first literature-based database to focus on radiosensitivity and provides a resource to better understand the detailed mechanisms of radiosensitivity. We anticipate dbCRSR will be a useful resource to enrich our knowledge and to promote further study of radiosensitivity.Database URL: http://bioinfo.ahu.edu.cn:8080/dbCRSR/

scholarly journals A prioritization analysis of disease association by data-mining of functional annotation of human genes

Genomics ◽  
2012 ◽  
Vol 99 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Takayuki Taniya ◽  
Susumu Tanaka ◽  
Yumi Yamaguchi-Kabata ◽  
Hideki Hanaoka ◽  
Chisato Yamasaki ◽  
...  
Keyword(s):  
Data Mining ◽  
Functional Annotation ◽  
Disease Association ◽  
Human Genes
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