Software | Feature learning | Technology: Tools, Hardware, and Software | All Resources

Multifactor Dimensionality Reduction (scikit-MDR)

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A scikit-learn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction. This project is still under active development and we encourage you to check back on this repository regularly for updates. MDR is an effective feature construction algorithm that is capable of modeling higher-order interactions and capturing complex patterns in data sets. MDR currently only works with categorical features and supports both binary classification and regression problems. We are working on expanding the algorithm to cover more problem types and provide more convenience features.

 

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HealthCare | Data | Publicly available biomedical data

National Archive of Computerized Data on Aging (NACDA)

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The National Archive of Computerized Data on Aging (NACDA) is a data archive concerned with the process of aging, health-related subjects, and the attitudes and behavior of the aged population. It consists of over sixteen hundred datasets relevant to gerontological research. NACDA operates under the auspices of the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan and is sponsored by the National Institute of Aging. The mission of NACDA is to advance research on aging by helping researchers to profit from the under-exploited potential of a broad range of datasets. 

 

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ADRD | Data | Publicly available biomedical data

National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS)

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The National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS) is a national data repository that facilitates access of genetic data to qualified investigators for the study of the genetics of early onset Alzheimer’s Disease (EOAD), late-onset Alzheimer’s disease (LOAD), and Alzheimer’s Disease Related Dementias (ADRD). Collaborations with large consortia such as the Alzheimer’s Disease Genetics Consortium (ADGC), Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, and the Alzheimer’s Disease Sequencing Project (ADSP) allow NIAGADS to lead the effort in managing large AD genetic datasets that can be easily accessed by the research community. All data derived from NIA funded Alzheimer’s Disease (AD) genetic studies are expected to be deposited in NIAGADS or another NIA approved site.

Principal investigators can request DSS distributed data through the Data Access Request Management (DARM) system by logging in using their eRA Commons ID. Once an application is approved by the NIAGADS ADRD Data Access Committee (NADAC) and Data Use Committee (DUC), the data can be accessed through the Data Portal and downloaded directly or through Amazon EC2. Investigators must be permanent employees of their institution at a level equivalent to a full-time assistant, associate, or full professor senior scientist with responsibilities that most likely include laboratory administration and oversight. Laboratory staff and trainees such as graduate students, and postdoctoral fellows are not permitted to submit project requests.

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HealthCare | Data | Publicly available biomedical data

National Social Life, Health, and Aging Project (NSHAP)

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The National Social Life, Health, and Aging Project (NSHAP) is a longitudinal, population-based study of health and social factors, aiming to understand the well-being of older, community-dwelling Americans by examining the interactions among physical health and illness, medication use, cognitive function, emotional health, sensory function, health behaviors, social connectedness, sexuality, and relationship quality. This study is conducted by a team of researchers from several academic institutions, led by the University of Chicago, and is funded by NIA and National Opinion Research Center (NORC) at the University of Chicago. The study has two stages. The first stage was conducted between 2005 and 2006 and collected data from 3,005 adults aged 57-85 nationally. The second stage was conducted between 2010 and 2011, including 1,400 participants from the first stage.

 

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Ontologies | Data | All Resources

NCBO BioPortal

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BioPortal is an open repository of biomedical ontologies that stores ontologies developed in various formats, that provides for automatic updates by user submissions of new versions, and that provides access via Web browsers and through Web services. This is a great place to explore and search for ontologies related to different types of data and fields of biomedical study.

 

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Machine Learning | External | Training Resources | All Resources

Neural Networks/ Deep Learning (StatQuest)

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Neural Networks are one of the most popular Machine Learning algorithms, but they are also one of the most poorly understood. Everyone says Neural Networks are “black boxes”, but that’s not true at all. In this video I break each piece down and show how it works, step-by-step, using simple mathematics that is still true to the algorithm. By the end of this video you will have a deep understanding of what Neural Networks do.

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HealthCare | Data | Publicly available biomedical data | All Resources

OASIS

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OASIS is a key component of the Centers for Medicare and Medicaid (CMS) partnership with the home care industry to foster and monitor improved home health care outcomes.  It is also proposed to become an integral part of the revised Conditions of Participation for Medicare-certified home health agencies (HHAs). The Outcome and Assessment Information Set-C (OASIS-C) is a group of data elements that: (1) Represent core items of a comprehensive assessment for an adult home care patient; and (2) Form the basis for measuring patient outcomes for the purposes of outcome-based quality improvement (OBQI)

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HealthCare | Publicly available biomedical data | Data

Panel Study of Income Dynamics (PSID)

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The Panel Study of Income Dynamics (PSID) is a longitudinal study that began in 1968 with a nationally representative sample of over 18,000 individuals living in 5,000 families in the United States. In this study, information on these participants and their descendants has been collected continuously, including data covering employment, income, wealth, expenditures, health, marriage, childbearing, child development, philanthropy, education, and numerous other topics. The PSID is directed by faculty at the University of Michigan, and the data are available on this website without cost to researchers and analysts. The study has three stages: Core Stage, Child Development Supplement (CDS), and Transition into Adulthood Supplement (TAS). 

 

Core Stage is the primary stage of data collection and involves a survey of all sample members and their families. The survey collects information on income, employment, education, health, and family structure.

 

Child Development Supplement focuses on the development of children in PSID participant families. It collects information on these children’s health, education, behavior, and other relevant factors.

 

Transition into Adulthood Supplement focuses on the transition of PSID family members into adulthood. It collects information on educational and career paths, family formation, health, and other relevant factors.

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