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Computational Neuroscience Commons™

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Selected Works

Arshad M. Khan, Ph.D.

Articles 1 - 2 of 2

Full-Text Articles in Computational Neuroscience

Mapping Molecular Datasets Back To The Brain Regions They Are Extracted From: Remembering The Native Countries Of Hypothalamic Expatriates And Refugees, Arshad M. Khan, Alice H. Grant, Anais Martinez, Gully Apc Burns, Brendan S. Thatcher, Vishwanath T. Anekonda, Benjamin W. Thompson, Zachary S. Roberts, Daniel H. Moralejo, James E. Blevins Jun 2018

Mapping Molecular Datasets Back To The Brain Regions They Are Extracted From: Remembering The Native Countries Of Hypothalamic Expatriates And Refugees, Arshad M. Khan, Alice H. Grant, Anais Martinez, Gully Apc Burns, Brendan S. Thatcher, Vishwanath T. Anekonda, Benjamin W. Thompson, Zachary S. Roberts, Daniel H. Moralejo, James E. Blevins

Arshad M. Khan, Ph.D.

This article, which includes novel unpublished data along with commentary and analysis,
focuses on approaches to link transcriptomic, proteomic, and peptidomic datasets mined from
brain tissue to the original locations within the brain that they are derived from using digital atlas
mapping techniques. We use, as an example, the transcriptomic, proteomic and peptidomic
analyses conducted in the mammalian hypothalamus. Following a brief historical overview, we
highlight studies that have mined biochemical and molecular information from the hypothalamus
and then lay out a strategy for how these data can be linked spatially to the mapped locations in a
canonical brain atlas …

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Tools And Approaches For The Construction Of Knowledge Models From The Neuroscientific Literature, Gully Apc Burns, Arshad M. Khan, Shahram Ghandeharizadeh, Mark O'Neill, Yi-Shin Chen Dec 2002

Tools And Approaches For The Construction Of Knowledge Models From The Neuroscientific Literature, Gully Apc Burns, Arshad M. Khan, Shahram Ghandeharizadeh, Mark O'Neill, Yi-Shin Chen

Arshad M. Khan, Ph.D.

Within this paper, we describe a neuroinformatics project (called "NeuroScholar," http://www.neuroscholar.org/) that enables researchers to examine, manage, manipulate, and use the information contained within the published neuroscientific literature. The project is built within a multi-level, multi-component framework constructed with the use of software engineering methods that themselves provide code-building functionality for neuroinformaticians. We describe the different software layers of the system. First, we present a hypothetical usage scenario illustrating how NeuroScholar permits users to address large-scale questions in a way that would otherwise be impossible. We do this by applying NeuroScholar to a "real-world" neuroscience question: How is stress-related information …

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