Program Co-Directors:

Doug Ayers, Ph.D.
dayers@uab.edu

Lisa Schwiebert, Ph.D.
lschwieb@uab.edu

The mission of the Business Certificate Program in Life Sciences Entrepreneurship is three-fold:

  • To provide knowledge in the areas of business and entrepreneurship to UAB graduate students, Postdocs, and faculty
  • To further cross-institutional collaborations among UAB schools
  • To promote interactions between UAB and the Birmingham business community

The program includes three required courses in Business Planning (MBA 673), Understanding the Biotech Industry (MBA 681), and Innovation (MBA 690).  Each of these courses is 3 credit hours. There are no pre-requisites and these courses may be taken in any order.

Current UAB graduate students may register directly.  If you are not a current graduate student, you will need to apply to the UAB Graduate School as a non-degree seeking student. Non-degree seeking students will need to contact Christy Manning at (205) 934-8815 or cmanning@uab.edu for more information.
 

Postdocs in UAB News

  • NIH awards nearly $34 million to UAB Center for Clinical and Translational Science
    This renewing of UAB’s prestigious Center for Translational Science Award will bolster research and workforce development at UAB and throughout its regional partner network in the Southeast.

    Written by Christina Crowe

    The National Institutes of Health has awarded the University of Alabama at Birmingham Center for Clinical and Translational Science $33.59 million over four years to continue the center’s programs advancing translational research.

    Since its initial funding in 2008 through Alabama’s only Center for Translational Science Award to work toward innovative discoveries for better health, the UAB CCTS has nurtured UAB research, accelerating the process of translating laboratory discoveries into treatments for patients, training a new generation of clinical and translational researchers, and engaging communities in clinical research efforts.

    The CCTS will continue to advance its mission to accelerate the delivery of new drugs, methodologies and practices to patients at UAB and throughout a partner network of 11 institutions in the Southeast.

    “We are excited by the capacity to continue to enhance our institution’s and our region’s innovative research and medical care,” said Robert Kimberly, M.D., UAB CCTS director. “Through internal and external partnerships, as well as a robust clinical environment and cutting-edge informatics and clinical trial resources, we look forward to working with our patients over the course of their lifespan.”

    Congress launched the CTSA program in 2006, which is overseen by the National Center for Advancing Translational Sciences.

    The amount of this award, more than double its previous funding awarded in 2008 and one of the largest at UAB, reflects an unmatched enthusiasm for the CCTS and its affiliated programs. It includes funding for 10 annual pre-doctoral training awards, 10 summer training awards, and eight career development awards for senior postdoctoral fellows or faculty-level candidates.

    “Our training programs continue to foster a culture of responsible, ethical practice among students, faculty and clinicians conducting human subjects research,” Kimberly said. “The NIH’s support of our expansive partner network, encompassing 11 regional academic and medical institutions throughout Alabama, Louisiana and Mississippi, will allow us to further grow our scope of practices and research resources as we look to tackle health disparities in the Southeast.”

    Through One Great Community, the CCTS’ community engagement enterprise, and the Community Health Innovation Awards, the CCTS engages Greater Birmingham­­-area residents in innovative programs designed by community members to improve their neighborhoods.

    “UAB is fully committed to the goals of the CCTS and to its continued development as a hub for clinical and translational research in the Southeast,” said UAB President Ray L. Watts. “This significant renewal speaks to the tremendous work and vision of our CCTS leadership and team, as well as our clinical infrastructure, scientific strengths, informatics expertise, training programs, and biostatistical and research design assistance.

    “The CCTS touches researchers in all UAB schools and across the partner network, and we are thrilled that this important work will continue with the confidence and support of the NIH.”

    Click to enlargeState and regional impact

    “The growth of the Center for Clinical and Translational Science at UAB will foster economic development in the state and throughout the region,” said Senator Richard Shelby. “With a history of providing optimal clinical care and innovation in human health, UAB’s receipt of this prestigious award enables the continued development of the workforce that is necessary to meet the needs of future research advancement.”

    Alabama Governor Robert Bentley, himself a physician, voiced his appreciation for the CCTS’ initiatives. “The center has been highly effective in providing assistance in the state’s efforts to eliminate the health disparities seen throughout our region,” Bentley said. “Whether across the life course or in underserved groups disproportionately affected by cancer, stroke, heart conditions and other diseases prevalent in our state, the center has been exemplary in reaching out to our citizens.”

    UAB Vice President, Research and Economic Development Richard Marchase, Ph.D., says he is particularly pleased that the CCTS is building on UAB’s history of serving populations burdened by health disparities through its partnerships with other state and regional institutions committed to advancing health through translational research. “It is through this culture of commitment and collaboration,” he said, “that we have become a national leader in biomedical research.”

  • When computers learn to understand doctors' notes, the world will be a better place
    By training computers to pick out timing clues in medical records, UAB machine learning expert Steven Bethard, Ph.D., aims to help individual physicians visualize patient histories, and researchers recruit for clinical trials.

    Written by Matt Windsor

    Train a computer to read medical records, and you could do a world of good. Doctors could use it to look for dangerous trends in their patients’ health. Researchers could speed drugs to market by quickly finding appropriate patients for clinical trials. They could also find previously overlooked associations. By keeping track of data points across tens of thousands, or millions, of medical records, computer models could find patterns that would never occur to individual researchers. Maybe Asian women in their 40s with type 2 diabetes respond well to a certain combination of medications, while white men in their 60s do not, for example.

    Machine learning, in particular a branch called natural language processing, has had plenty of successes recently. It’s the secret sauce behind IBM’s “Jeopardy”-winning Watson computer and Apple’s Siri personal assistant, for instance. But computers still have a tough time following medical narratives.

    “We take it for granted how easy it is for us to understand language,” said Steven Bethard, Ph.D., a machine learning expert and linguist in the UAB College of Arts and Sciences Department of Computer and Information Sciences. “When I’m having a conversation, I can use all kinds of crazy constructions and pauses between words, and you would still understand me. All these things make language very difficult for computers, however. They like rules and an order that is followed every time, but languages aren’t like that.”

    Timing is everything

    So Bethard, the director of UAB’s Computational Representation and Analysis of Language Lab, builds models that help computers catch our drift. In one ongoing project, he is working with colleagues at the Mayo Clinic and Boston Children’s Hospital “to extract timelines from clinical work,” Bethard said. Using text from clinical notes taken at the Mayo Clinic, “we’re working to find all the clinical events mentioned in those notes — things like ‘asthma’ and ‘CT scan,’ for example — and link them to the proper time,” he said. If the computer sees “the patient has a history of asthma,” it should know that’s in the past. If it sees “planning a CT scan,” that’s in the future. “Sometimes you have explicit dates, such as ‘on Sept. 15, the patient had a colonoscopy,’” Bethard said. “But the computer still has to figure out whether that means Sept. 15, 2014, or Sept. 15, 2015.’”

    The diagram above illustrates how a computer could extract timeline information out of an entry in a medical record.A system like this would help individual doctors keep track of their patients’ progress. “If you have had a patient for 15 years, you see so many things,” Bethard said. “Looking at a visual of all the conditions and procedures over that time is extremely useful.” The system could also identify patients for clinical trials. “Say you wanted to find someone who had liver toxicity after they started taking methotrexate,” Bethard said. “The sequence of events is important; you only want to find people who have taken the drug and had liver toxicity in the appropriate order.” Another use: finding new associations between drugs or procedures and adverse events. “If you have a large number of patients, you can say, ‘How often do you see a certain side effect?’ for example,” Bethard said. “You can generate new hypotheses about causality.”

    Learning to spot cancer

    One of Bethard’s graduate students, John David Osborne, has built a machine-learning model that is already having an impact on the practice of health care at UAB. By day, Osborne is a research associate in the biomedical informatics group of UAB’s Center for Clinical and Translational Science. He and his colleagues were called in to help UAB’s Cancer Registry with a Big Data challenge: tracking and cataloguing cancer diagnoses and treatment outcomes.

    Every hospital is responsible for reporting new cases of many different types of diseases to the federal government. “Cancer is one of those diseases, but not all cancers are reportable,” Osborne said. “Lots of skin cancers aren’t, but melanoma is; anything malignant or in the central nervous system is reportable.” Identifying and tracking these cases in pathology reports — and determining whether they are or are not reportable — can be quite challenging at a health care system as large as UAB, Osborne notes. A year and a half ago, the biomedical informatics team at the CCTS created the Cancer Registry Control Panel, which uses natural language processing to detect possible cancer cases in the pathology reports. As an additional research project, Osborne recently designed a machine-learning algorithm that provides additional assistance to the human registrars. “It scans through the records and says, ‘This is a likely case, and here’s why I think that,’” Osborne said. “Humans are still going through every record, but you can speed it up and show them where to look.”

    Language matters

    Bethard and Osborne build their models using the Unstructured Information Management Architecture — an open-source version of the code IBM used to create Watson.

    The first step in building a machine-learning model is to decide what kind of training material to use. “The machine-learning models we create for health information extraction look at gold-standard models that humans have created,” Bethard said. “They say, ‘I see all these patterns in the human timelines, so this is what I’ll look for.’”

    Some of these decisions are relatively simple. “Cancer is always a condition of interest,” Bethard said. “Anything related to cancer is something you want to include. The harder pattern to learn is how to link together time and events. A date and then a colon tells you they are describing something that happened on that date. Verb phrases, noun phrases and linguistic structure in time can be very predictive.”

    As that description makes clear, natural language processing requires a deep knowledge of English grammar as well as computer code. “The most successful people in this field are hybrids,” adept at linguistics and computer science, Bethard said. He has a bachelor’s degree in linguistics. He shares his interest in language with his wife, who is now completing a postdoctoral fellowship in the cognitive neuroscience of language at the University of South Carolina.

    Bethard came to Birmingham in 2013, attracted by ongoing research in natural language processing in UAB’s computer science department. “For me, it makes a lot of sense to be at a place with a major medical school,” Bethard said. He is looking forward to collaborations with James Cimino, M.D., Ph.D., the inaugural director of the School of Medicine’s new Informatics Institute and a renowned expert in the creation and manipulation of electronic medical records. “He’s famous, very well-known and well-respected,” Bethard said of Cimino. “He knows about all the range of problems: getting information from the text that doctors write, how to input this data, how to store it — the whole spectrum.”

    Teaching computers to navigate the ambiguity of the English language can be trying, but the opportunities at UAB are exciting, Bethard says. “There is plenty of data available here, and clear challenges for these models to address.”

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UAB Research News

  • Roberson wins prestigious Denny-Brown award from American Neurological Association

    UAB’s Roberson wins young investigator award from the American Neurological Association.

    Erik Roberson, M.D., Ph.D., associate professor in the Department of Neurology at the University of Alabama at Birmingham, is the winner of the 2015 Derek Denny-Brown Young Neurological Scholar Award from the American Neurological Association.

    The award, considered the ANA’s highest and most prestigious, recognizes early- to mid-career neurologists and neuroscientists who have made outstanding basic and clinical scientific advances toward the prevention, diagnosis, treatment and cure of neurological diseases.

    Roberson’s primary research focus is Alzheimer’s disease, in particular the role of tau reduction in protection against memory loss. Roberson and his colleagues were also the first to show that tau plays a critical role in regulating neuronal excitability, which could have applications in the treatment of many neurological conditions with seizures. He has also contributed new insights into mechanisms and therapeutic approaches to frontotemporal dementia.

    Roberson graduated summa cum laude from Princeton University and completed his M.D./Ph.D. training at Baylor College of Medicine. He was chief resident in neurology at the University of California at San Francisco. He joined the faculty at UAB in 2008 with appointments to the departments of Neurology and Neurobiology. He holds the Spencer Endowed Professorship in Neuroscience. He is a co-director of the UAB Center for Neurodegeneration and Experimental Therapeutics and has recently been appointed co-director of the McKnight Brain Institute at UAB.

    “Dr. Roberson is recognized nationally and internationally for his expertise in Alzheimer’s disease and related disorders,” said David Standaert, M.D., Ph.D., professor and chair of the UAB Department of Neurology. “I think Dr. Roberson is one of the leading neuroscientists of his generation. He is exceptionally bright, very well trained and, most importantly, fully committed to his goals.”

  • Work It Out: NIH Wants Leader For Intellectual Sweat Project
    Applicants will face stiff competition from the University of Alabama at Birmingham, as its own Dr. Marcus Bamman is an applicant, and one of the “key investigators who helped NIH staff do the research for their successful application to the Common Fund,” according to an NIH release.
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