Relationships + Synergy … Aramco + KAUST

Thanks to a Google® Scholar email alert (key word: Aramco), I found the following Open Access article.
The first thing that jumped out at me was the subject of the article … bioinformatics.
Authors of the article are affiliated with KAUST, King Abduliziz University, and Saudi Aramco.

KAUST and King Abduliziz University I understand. The two universities are engaged in a wide range of academic pursuits.  But why Saudi Aramco?

I don’t know, but for what it’s worth, I can speculate.

Saudi Aramco and the Saudi universities, including KAUST and King Abduliziz University, have deep and synergistic relationships with each other.
Aramco employs massive computing resources to characterize its reservoirs in order to enhance its ability to extract oil from those resources.
The big data acquired as a result of these characterizations must be analyzed. This analysis requires DeepLearning in order to make the data useful.
DeepLearning algorithms created in the bioinformatics field can be adapted for use in the reservoir characterization effort.

Accordingly, it makes sense for Aramco to make available to bioinformatics researchers their vast computing resources. The researchers will benefit from the use of Aramco’s resources, and Aramco will benefit from the results of their research.

So what do you think … does the Aramco/KAUST relationship + synergy theory seem plausible?

TIP #1: Relationship + synergy applies to everything, like ExxonMobil, Chevron, Shell, BP, and the various universities they are associated with.

TIP #2: The logic for the relationship may not be linear. It may be as indirect as the relationship described above.

Here are some details from the article.  The link at the end of this description will take you to the free full text of the article.

///////
Bioinformatics, 2018, 0–0 doi: 10.1093/bioinformatics/xxxxx Advance Access Publication Date: 01 September 2018 Original Paper Sequence analysis
DeepGSR: An optimized deep-learning structure for the recognition of genomic signals and regions
Manal Kalkatawi 1, 2
Arturo Magana-Mora 1, 3
Boris Jankovic 1
Vladimir B. Bajic 1
1 King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal 23955-6900, Saudi Arabia.
2 King Abdulaziz University, Faculty of Computing and Information Technology, Jeddah 21589, Saudi Arabia.
3 Saudi Aramco, EXPEC-ARC, Drilling Technology Team, Dhahran, 31311, Saudi Arabia.
Abstract
Motivation: Recognition of different genomic signals and regions (GSRs) in DNA is crucial for understanding genome organization, gene regulation, and gene function, which in turn generate better genome and gene annotations. Although many methods have been developed to recognize GSRs, their pure computational identification remains challenging. Moreover, various GSRs usually require a specialized set of features for developing robust recognition models. Recently, deep-learning (DL) methods have been shown to generate more accurate prediction models than ‘shallow’ methods without the need to develop specialized features for the problems in question. Here, we explore the potential use of DL for the recognition of GSRs. Results: We developed DeepGSR, an optimized DL architecture for the prediction of different types of GSRs. The performance of the DeepGSR structure is evaluated on the recognition of polyadenylation signals (PAS) and translation initiation sites (TIS) of different organisms: human, mouse, bovine, and fruit fly. The results show that DeepGSR outperformed the state-of-the-art methods, reducing the classification error rate of the PAS and TIS prediction in the human genome by up to 29% and 86%, respectively. Moreover, the cross-organisms and genome-wide analyses we performed, confirmed the robustness of DeepGSR and provided new insights into the conservation of examined GSRs across species. Availability and implementation: DeepGSR is implemented in Python using Keras API; it is available as open-source software and can be obtained at https://doi.org/10.5281/zenodo.1117159G.
Contact: vladimir.bajic@kaust.edu.sa  or manal.kalkatawi@kaust.edu.sa  
Supplementary information: Supplementary data are available at Bioinformatics online.
1 Introduction © The Author(s) 2018. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License
source: https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty752/5089227
Free Full text: https://watermark.silverchair.com/bty752.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAlcwggJTBgkqhkiG9w0BBwagggJEMIICQAIBADCCAjkGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMzsxvWuEXhCUDWHXKAgEQgIICCkJuVRgDn_yeWX5NyUSMOHBHpFSgahMs2YN8jxYEd87_rSm5_UlMoMFxCjPMXynMYpSs4P10Ef4-gOmXldMvy0Dgm9bigMVMGHpgwFqar436r8RoDxZpov5fHYpNeNUcyFSQIlOlW-z4DMFhfXZWYSkdnulBRAIK1-U7vaNhsuuJB4takgTZvPvTrj1PIODBRZHeljlvQcVJ4lyD2bfiXLDv681QwxbJ029_xXDjQTwYaOoHO_TNFBdDx5z8-ZV_0EGemIaImcZ3JhbdDWoD7146HGZVOkLqjML_q7zy4i7E32Gh-0GsbdOO9yfWktXb9SVqydwWt4CIQis0YUPvcSPER50DYWgTw2h2ysutmkFL_guDPL_VQ-Ud7kJXXxcWBDIijUaDTLO_TD6wVXbgTXdIRKRmebEFLmZkF5BUqXkw00Xy-qKmUNPCsmEIsn6RBfRN6Or5L4-jHAi0saVoHICJYcL3b0Jo5NvOWzH9nXCTPB9QFKx4QxcSdn2mUQT0CudzznCy4kGS0K1y8YCmPZm1enbgybdP3jDZev6v3w8hv8htsupOACYwqHb8arhqGKxrT3VzkRnO_ng_R-bxPRXLUGYAUUpt8kSyZF2Fuz7BpiGySwmHWaLl9ni4R3jEopJRLq6lqUZZhLkKvdTS5sSRHFNKdbvXmrFzY6Y_T2tq1yMSKv7xWuiCNg
///////