SOLICITATION NOTICE
R -- Integrated Machine learning analyses
- Notice Date
- 9/24/2015
- Notice Type
- Presolicitation
- NAICS
- 541990
— All Other Professional, Scientific, and Technical Services
- Contracting Office
- Department of Health and Human Services, National Institutes of Health, National Institute on Drug Abuse, Station Support/Simplified Acquisitions, 31 Center Drive, Room 1B59, Bethesda, Maryland, 20892
- ZIP Code
- 20892
- Solicitation Number
- HHS-NIH-NIDA-SSSA-NOI-2015-915
- Archive Date
- 10/10/2015
- Point of Contact
- Hunter A. Tjugum, Phone: 301 435 8780
- E-Mail Address
-
hunter.tjugum@nih.gov
(hunter.tjugum@nih.gov)
- Small Business Set-Aside
- N/A
- Description
- INTRODUCTION THIS IS A PRE-SOLICITATION NON-COMPETITIVE (NOTICE OF INTENT) SYNOPSIS TO AWARD A CONTRACT OR PURCHASE ORDER WITHOUT PROVIDING FOR FULL OR OPEN COMPETITION (INCLUDING BRAND-NAME). The National Institute on Drug Abuse (NIDA), Station Support Contracts and Simplified Acquisitions Branch (SS/SA) on behalf of the National Institute on Aging (NIA) at the National Institues of Health (NIH) intends to negotiate and award a contract without providing for full and open competition (Including brand-name) to Wake Forest School of Medicine at Medical Center Blvd., Wiston-Salem, NC 27157 to perform advanced machine learning analyses on measurements (metabolomics and proteomics) obtained from blood and brain tissue samples as well as longitudinal clinical/laboratory data collected from participants in the Baltimore Longitudinal Study of Aging (BLSA). NORTH AMERICAN INDUSTRY CLASSIFICATION SYSTEM (NAICS) CODE The intended procurement is classified under NAICS code 541990 with a Size Standard of $15.0 M. REGULATORY AUTHORITY The resultant contract will include all applicable provisions and clauses in effect through the Federal Acquisition Circular (FAC) 2005-83-1, September 5, 2014. This acquisition is conducted under the procedures as prescribed in FAR subpart 13-Simplified Acquisition Procedures at an amount not exceeding the simplified acquisition threshold ($150,000). STATUTORY AUTHORITY This acquisition is conducted under the authority of the Federal Acquisition Regulation (FAR) Part 13-Simplified Acquisition Procedures, Subpart 13.106-1 (b) (1), Soliciting from a single source and is not expected to exceed the simplified acquisition threshold. Contracts awarded using FAR Part 13-Simplifeid Acquisition Procedures are exempt from the requirements of FAR Part 6-Competition Requirements. GENERAL INFORMATION 1. Title: Integrated Machine learning analyses of Metabolomic, Proteomic and Longitudinal Clinical Variables for Prediction of Alzheimer's Disease 2. Background Information: The Laboratory of Behavioral Neuroscience (LBN) in the Intramural Research Program (IRP) of the NIA is driven to Conduct basic and clinical research on individual differences in cognition, personality, and affect; Investigate the neural contributions to these individual differences; Investigate the influence of age on these traits and states and their reciprocal influence on cognitive and mental health, well-being, and adaptation; Examine predictors and modifiers of age-related neurodegenerative diseases and age-associated changes in behavior, predispositions, and brain-behavior associations; and Identify early markers of Alzheimer's disease and cognitive decline, and examines factors that promote the maintenance of cognitive health. 3. Purpose or Objective: The Unit of Clinical & Translational Neuroscience in the LBN seeks to identify novel biomarkers that might be predictive of disease before the onset of clinical symptoms, by applying mass spectrometry-based proteomics and metabolomics for the discovery of novel biomarkers predictive of disease before symptom onset; and relating genetic and environmental risk factors to changes in brain structure, function and pathology during aging. In the biomarker discovery studies, the Unit of Clinical & Translational Neuroscience uses archived blood samples collected through the existing research study project, the Baltimore Longitudinal Study on Aging (BLSA), to identify changes over time in the concentrations of proteins and small metabolites that may be predictive of early cognitive decline and neuropathological changes associated with AD. The UCTN needs contractor services to perform Integrated Machine learning analyses of Metabolomic, Proteomic and Longitudinal Clinical Variables for Prediction of Alzheimer's Disease. The purpose of the contractor shall be to perform advanced machine learning analyses on measurements (metabolomics and proteomics) obtained from blood and brain tissue samples as well as longitudinal clinical/laboratory data collected from participants in the Baltimore Longitudinal Study of Aging (BLSA). The final objective is the identification of predictive biomarkers of Alzheimer's disease (AD) and enhanced understanding of the molecular bases of cognitive resilience/susceptibility to Alzheimer's pathology. Contractor will perform integrated machine-learning analyses on high-dimensional metabolomic, proteomic, and longitudinal clinical data for the identification of Predictive biomarkers of AD and Molecular markers associated with resilience and susceptibility to AD pathology. 4. Period of Performance: September 30, 2015 through September 29, 2017 CONTRACTOR REQUIREMENTS (SCOPE OF WORK) In general, the contractor will use machine-learning methods that allow uncovering of relationships between the requirements identified herein. Contractor will provide a detailed analysis of results in a Microsoft Word document and formal written progress reports on a quarterly basis in regards to the following scope of work: 1. Longitudinal changes in serum concentrations of small molecule metabolites and risk of incident AD using global metabolomic profiling as well as targeted metabolomics datasets on approximately 600 serum samples from the BLSA. 2. Metabolomic markers (using both targeted and untargeted metabolomics datasets) of resilience/vulnerability to AD pathology. These data are acquired on approximately 45 brain samples (BLSA autopsy study) from three brain regions. 3. Proteomic markers (using untargeted proteomics data) of resilience/vulnerability to AD pathology. These data are acquired on approximately 45 brain samples (BLSA autopsy study) from two brain regions. 4. Longitudinal clinical/laboratory data and risk of incident AD. These analyses will be performed on longitudinal clinical/laboratory data collected in the BLSA and ‘Biomarkers of Cognitive Decline Among Normal Individuals' (BIOCARD) studies. 5. Final analyses will examine the utility of combining longitudinal metabolomic and clinical/laboratory data for understanding mechanisms of AD pathogenesis and prediction of AD risk. CONTRACTING WITHOUT PROVIDING FOR FULL OR OPEN COMPETITION (INCLUDING BRAND-NAME) DETERMINATION The determination by the Government to award a contract without providing for full and open competition is based upon the market research conducted as prescribed in FAR Part 10-Market Research, specifically the review and evaluation of the responses to the sources sought notice published in the FedBizOpps on September 5, 2015 under notice number HHS-NIH-NIDA-SSSA-SS-2015-864, Integrated Machine Learning Analyses. Wake Forest School of Medicine has the expertise, facilities and experience for performing the requirements of this project. This requirement is closely related to the work performed under purchase order number HHSN311201400232P in which this contractor utilized their customized software code to perofmr cutting-edge machine lear ning analyses of data collected in the BLSA which is currently requiret to be replicated to maintain consistency. Because of the related expertise and knowledge of the project Columbia University is the only known source with the capability of provide the services of this project. CLOSING STATEMENT This synopsis is not a request for competitive proposals. However, interested parties may identify their interest and capability to respond to this notice. Responses to this notice shall contain sufficient information to establish the interested parties' bona-fide capabilities for fulfilling the requirement and include a technical proposal, cost-price proposal, the period of performance, the Dun & Bradstreet Number (DUNS), the Taxpayer Identification Number (TIN), and the certification of business size. All offerors must have an active registration in the System for Award Management (SAM) www.sam.gov." A determination by the Government not to compete this proposed contract based upon responses to this notice is solely within the discretion of the Government. The information received will normally be considered solely for the purposes of determining whether to proceed on a non-competitive basis or to conduct a competitive procurement. All responses must be received by September 25, 2015, 4:30 PM Eastern time and must reference solicitation number HHS-NIH-NIDA-SSSA-NOI-2015-915. Responses may be submitted electronically to Mr. Hunter Tjugum, Contracting Specialist at hunter.tjugum@nih.gov. Fax responses will not be accepted. "All responsible sources may submit a capability statement, proposal, or quotation, which shall be considered by the agency."
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