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Update about.md
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‎_pages/about.md‎

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.page-header {
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margin-bottom: 40px;
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.page-header h1 {
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margin: 0;
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.tagline {
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.section {
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font-weight: 500;
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</style>
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<div class="page-header">
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<h1>Understanding Aging through Computational Biology</h1>
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<div class="tagline">Developing methods to quantify, understand, and prevent age-related decline</div>
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</div>
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<div class="section">
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<h2>Welcome to SinhaLab</h2>
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<h2 class="section-heading">Welcome to SinhaLab</h2>
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<p>We build computational methods to better understand the fundamental process of aging and develop strategies to prevent age-related decline.</p>
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<div class="highlight-text">
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Our lab pioneered new computational approaches that can characterize tissue aging using routine clinical data, offering unprecedented insights into the biological mechanisms of aging.
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</div>
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</div>
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<div class="section">
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<h2>Our Mission</h2>
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<h2 class="section-heading">Our Mission</h2>
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<p>At SinhaLab, our central goal is to understand the fundamental process of aging using big data and advanced machine learning approaches. We leverage multiple data types from image to multi-omics to electronic health records to develop a comprehensive view of tissue aging.</p>
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</div>
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<div class="section">
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<h2>Research Focus</h2>
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<h2 class="section-heading">Research Focus</h2>
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<p>Our current research focuses on three key areas:</p>
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</div>
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<div class="section">
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<h2>Our Innovations</h2>
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<h2 class="section-heading">Our Innovations</h2>
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<ul class="innovation-list">
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<li>
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<span class="innovation-title">ecPath</span>
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<p>We demonstrated for the first time that extrachromosomal DNA (ecDNA) can be detected from histopathology images using a multimodal deep learning approach that integrates both images and gene expression data.</p>
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</li>
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<li>
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<span class="innovation-title">PERCEPTION</span>
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<p>The first computational framework to guide personalized cancer treatment selection using single-cell RNA sequencing for kinase-targeted therapies.</p>
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</li>
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<li>
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<span class="innovation-title">DeepTarget</span>
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<p>A method to identify direct and indirect targets of cancer drugs that drive their in vitro killing efficacy, providing insights into drug mechanisms of action.</p>
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</li>
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</ul>
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<div class="innovation-item">
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<span class="innovation-title">ecPath</span>
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<p>We demonstrated for the first time that extrachromosomal DNA (ecDNA) can be detected from histopathology images using a multimodal deep learning approach that integrates both images and gene expression data.</p>
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</div>
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<div class="innovation-item">
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<span class="innovation-title">PERCEPTION</span>
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<p>The first computational framework to guide personalized cancer treatment selection using single-cell RNA sequencing for kinase-targeted therapies.</p>
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</div>
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<div class="innovation-item">
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<span class="innovation-title">DeepTarget</span>
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<p>A method to identify direct and indirect targets of cancer drugs that drive their in vitro killing efficacy, providing insights into drug mechanisms of action.</p>
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</div>
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<p>Our interdisciplinary approach combines computational biology, machine learning, and biomedical imaging to address complex questions about aging that weren't accessible through traditional methods.</p>
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</div>
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<div class="section">
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<h2>Our Team</h2>
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<h2 class="section-heading">Our Team</h2>
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<p>We are a diverse team with expertise spanning machine learning, bioengineering, computer vision, and omics analysis. <a href="/team/">Meet our full team →</a></p>
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</div>
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<h2>Lab Affiliations</h2>
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<h2 class="section-heading">Lab Affiliations</h2>
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<ul class="affiliations-list">
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<li>Primary Member, Cancer Metabolism and Microenvironment Program</li>

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