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<article id="post-23" class="post-23 page type-page status-publish hentry">
    <header class="entry-header">
        <h1 class="entry-title">Data-Driven Precision Pharmacology</h1>
    </header><!-- .entry-header -->

    <div class="entry-content">
        <p>We are making drugs safer through the analysis of data. Everyday millions of us
            or our loved ones take medications to manage our health. We trust in these
            prescriptions to improve our lives and give us hope for a healthier future.
            Often, however, these drugs have harmful side effects or dangerous interactions.
            Adverse drug reactions are experienced by millions of patients each year and
            cost the healthcare industry billions of dollars. In the Tatonetti Lab we use
            advanced data science methods, including artificial intelligence and machine
            learning, to investigate these medicines. Using emerging resources, such as
            electronic health records (EHR) and genomics databases, we are working to
            identify for whom these drugs will be safe and effective and for whom they will
            not. <a href="#projects">Browse</a> our databases, <a
                href="#positions">contribute</a> to our projects,
            and <a href="#people">join us</a> on this journey to
            make precision pharmacology a reality.</p>
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<div class="news">
    <h3>New Publication: Automating Cancer Staging with BB-TEN</h3>
    <div class="date">Oct 17, 2024</div>
    <div class="news-content">

        <p>We’re thrilled to announce our new publication in <i>Nature Communications</i>: 
            <b>"Generalizable and Automated Classification of TNM Stage from Pathology Reports with External Validation".</b>
        </p>
        <p>
            Led by recent PhD grad, Dr. Jenna Kefeli, we present BB-TEN, a transformer-based model designed to automatically 
            classify cancer stages (TNM) from pathology reports. TNM staging is crucial for cancer prognosis and treatment but 
            is often unstructured in medical records, leading to delays. Our model tackles this by accurately extracting staging 
            information from pathology reports across 23 cancer types.
        </p>
        <p>
            BB-TEN has been trained on nearly 7,000 reports and externally validated on 8,000 more, achieving impressive 
            performance (AU-ROC 0.815–0.942). It allows institutions to quickly and accurately extract cancer stage 
            information without fine-tuning, speeding up clinical trial recruitment and patient care.
        </p>

        <p><a href="https://doi.org/10.1038/s41467-024-53190-9">Read full paper here.</a></p>
    </div>
</div>

<div class="news">
    <h3>Introducing the PhD Program in Health AI at Cedars-Sinai</h3>
    <div class="date">April 25, 2024</div>
    <div class="news-content">
        <p>
            We are thrilled to announce the launch of the groundbreaking PhD program in Health 
            Artificial Intelligence at Cedars-Sinai. This program offers a curriculum that includes 
            courses such as Artificial Intelligence, Ethical AI, Machine Learning, and more, along 
            with extensive clinical rotations and research opportunities. Under the guidance of 
            leading experts, students will gain hands-on experience and contribute to innovations in 
            healthcare. For more details and to apply, visit our <a href="https://www.cedars-sinai.edu/education/graduate-school/phd-health-artifical-intelligence.html">program page</a>.
        </p>
    </div>
</div>

<div class="news">
    <h3>Translational Bio Year-in-Review 2024</h3>
    <div class="date">March 19, 2024</div>
    <div class="news-content">

        <p>
            Wow! What a great year for translational bionformatics. This year the 
            AMIA TBI Year-in-Review committee screened 1,085 articles and ranked 
            462 papers by their innovation and biomedical impact.
        </p>
        <p>
            I featured the very BEST 29 papers plus 9 additional shout outs. Special
            thanks to everyone on the Year-in-Review review committee this year, especially
            Humayera Islam and Nick Reid for their amazing leadership. 
        </p>
        <p><a href="https://tatonettilab-resources.s3.amazonaws.com/TBI-Year-in-Review/2024-TBI-Year-in-Review-Tatonetti.pdf">
            AMIA Informatics Summit 2024 TBI Year-in-Review Slides</a></p>
        <p>
            Access to previous years is 
            <a href="http://tatonettilab-resources.s3-website-us-west-1.amazonaws.com/?p=TBI-Year-in-Review/">available here</a>.
        </p>
    </div>
</div>
<div class="news">
    <h3>TLab moves to LA 🏖️</h3>
    <div class="date">January 9, 2023</div>
    <div class="news-content">

        <p>I’m excited to share that I have joined the faculty at Cedars-Sinai in Los
            Angeles as Vice Chair of Computational Biomedicine and Associate Director of the
            Cancer Center! My group will continue to advance state-of-the-art of drug side
            effect and DDI research, biomedical data mining and AI, and translational
            bioinformatics. In addition, these leadership positions will bring opportunities
            to implement DS/AI into the clinical and research workflow at one of our
            nation’s top hospitals.</p>



        <p>I am so grateful to Columbia DBMI and all that we have accomplished over the past
            10 years. Read the <a href="https://www.linkedin.com/pulse/tlab-moves-los-angeles-nicholas-tatonetti">full
                story</a> of heartfelt highlights.</p>
    </div>
</div>