Update templates/about.html

templates/about.html

@@ -5,73 +5,76 @@
   <section class="card about-flex">
 
     <div class="about-text">
-    <h2>About CANLoc</h2>
-
-    <p>
-      CANLoc is a machine-learning system designed to predict the subcellular
-      localization of proteins directly from the protein sequence. It combines
-      transformer-based embeddings from the <b>ESM2</b> model
-      with an optimized <b>XGBoost</b> classifier trained on curated
-      protein datasets.
-    </p>
-        </div>
-
-        <div class="about-image">
-            <img src="{{ url_for('static', filename=cell_diagram.png') }}"
-                 alt="cell diagram showing Subcellular Location">
-        </div>
-
-
-    <h3>Performance & Evaluation</h3>
-
-    <p>
-      CANLoc achieves high accuracy, precision, recall, and F1-scores across all
-      classes. We additionally validate the model using:
-    </p>
-
-    <ul>
-      <li>Train/test split evaluation</li>
-      <li>10-fold stratified cross-validation</li>
-      <li>ROC curves for each class</li>
-      <li>Sensitivity and specificity analysis</li>
-    </ul>
-
-    <p>
-      These evaluations confirm that CANLoc predictions are reliable for academic
-      and research workflows.
-    </p>
-
-    <h3>Intended Use</h3>
-
-    <p>
-      CANLoc is designed for:
-    </p>
-
-    <ul>
-      <li>Functional protein studies</li>
-      <li>Localization-oriented drug delivery strategy</li>
-    </ul>
-
-    <h3>Model Strengths</h3>
-    <ul>
-      <li>Fast and scalable for single or batch prediction</li>
-      <li>Transformer embeddings provide rich biological context</li>
-      <li>High accuracy with interpretable confidence metrics</li>
-      <li>No alignment or preprocessing required beyond the raw sequence</li>
-    </ul>
-
-    <h3>Limitations</h3>
-    <ul>
-      <li>Performance depends on sequence length and quality</li>
-      <li>Ambiguous sequences may reduce confidence</li>
-      <li>Designed for four major classes only</li>
-    </ul>
-
-    <p>
-      CANLoc represents a balance between modern deep learning and classical machine
-      learning methods, producing a system that is both <b>reliable</b> and
-      <b>lightweight enough to deploy</b> in real-world web applications.
-    </p>
+      <h2>About CANLoc</h2>
+
+      <p>
+        CANLoc is a machine-learning system designed to predict the subcellular
+        localization of proteins directly from the protein sequence. It combines
+        transformer-based embeddings from the <b>ESM2</b> model
+        with an optimized <b>XGBoost</b> classifier trained on curated
+        protein datasets.
+      </p>
+
+      <h3>Performance & Evaluation</h3>
+
+      <p>
+        CANLoc achieves high accuracy, precision, recall, and F1-scores across all
+        classes. We additionally validate the model using:
+      </p>
+
+      <ul>
+        <li>Train/test split evaluation</li>
+        <li>10-fold stratified cross-validation</li>
+        <li>ROC curves for each class</li>
+        <li>Sensitivity and specificity analysis</li>
+      </ul>
+
+      <p>
+        These evaluations confirm that CANLoc predictions are reliable for academic
+        and research workflows.
+      </p>
+
+      <h3>Intended Use</h3>
+
+      <ul>
+        <li>Functional protein studies</li>
+        <li>Localization-oriented drug delivery strategy</li>
+      </ul>
+
+      <h3>Model Strengths</h3>
+      <ul>
+        <li>Fast and scalable for single or batch prediction</li>
+        <li>Transformer embeddings provide rich biological context</li>
+        <li>High accuracy with interpretable confidence metrics</li>
+        <li>No alignment or preprocessing required beyond the raw sequence</li>
+      </ul>
+
+      <h3>Limitations</h3>
+      <ul>
+        <li>Performance depends on sequence length and quality</li>
+        <li>Ambiguous sequences may reduce confidence</li>
+        <li>Designed for four major classes only</li>
+      </ul>
+
+      <p>
+        CANLoc represents a balance between modern deep learning and classical
+        machine learning methods, producing a system that is both
+        <b>reliable</b> and <b>lightweight enough to deploy</b>
+        in real-world web applications.
+      </p>
+    </div>
+
+    <div class="about-image">
+      <figure>
+        <img src="{{ url_for('static', filename='cell_diagram.png') }}"
+             alt="Eukaryotic cell diagram showing Subcellular Location">
+        <figcaption>
+          <strong>Figure 1.</strong> Schematic representation of a cell
+          highlighting major subcellular locations relevant to protein
+          localization prediction.
+        </figcaption>
+      </figure>
+    </div>
 
   </section>
 </div>