• Structural Biology & Bioinformatics Software Tools:
• Procheck $\to$ (II): Assesses stereochemical quality and dihedral angles ($\\phi, \\psi$) of a protein model via Ramachandran plot analysis.
• ClustalW $\to$ (I): Classic progressive multiple sequence alignment (MSA) software.
• Swiss-Model $\to$ (V): Premier automated homology/comparative protein modeling server.
• Cn3D $\to$ (III): NCBI's helper molecular visualization tool displaying 3D macromolecular structures and sequence alignments simultaneously.
• Affinity $\to$ (IV): Computational molecular docking software that predicts binding modes and ligand-receptor interactions.
• Matching Result: (A) - (II), (B) - (I), (C) - (V), (D) - (III), (E) - (IV) (Option 1).

Correct option: (1) (A) - (Il), (B) - (1), (C) - (V), (D) - (ill), (E)- (IY)

• Perl Array Functions:
• splice(): General-purpose function that modifies array elements by removing, replacing, or inserting items at any index. splice(@array, offset, length, list) is versatile for all array manipulations. (True)
• push(): Appends elements to the end of an array (not the beginning). To add items to the beginning, unshift() is used. (False)
• unshift(): Prepends elements to the beginning of an array (not the end). To append items to the end, push() is used. (False)
• Conclusion: Only (A) is true (Option 1).

Correct option: (1) Only (A) is true

• Supervised vs. Unsupervised Machine Learning:
• Supervised Learning: Models are trained on labeled datasets where inputs map to known target outputs:
• Support Vector Machine (SVM): Supervised classification/regression.
• Decision Tree: Supervised classification/regression.
• Linear Regression: Supervised continuous target regression.
• Unsupervised Learning: Models identify hidden patterns, clusters, or intrinsic groupings within unlabeled data without predefined ground truth.
• K-means clustering: Classic unsupervised partitioning algorithm that clusters data points into $K$ centroids based on distance metrics.
• Conclusion: K-means clustering is NOT a supervised learning method (Option 2).

Correct option: (2) K-means clustering

• Ensemble Modeling Techniques:
• Bagging (Bootstrap Aggregation): Trains multiple models independently on bootstrapped subsamples in parallel (e.g., Random Forest).
• Boosting: Trains models sequentially, where each successor model focuses on correcting residual errors of predecessor models (e.g., AdaBoost, XGBoost).
• Stacking (Stacked Generalization): Trains diverse base learners and uses a meta-model trained on cross-validated predictions to combine their outputs.
• Blending: A simplified heuristic variation of stacking that uses a single hold-out validation set rather than $k$-fold cross-validation. In standard academic machine learning curricula and competitive examinations, Blending is not classified as one of the fundamental theoretical ensemble architectures.
• Conclusion: Blending (Option 3).

Correct option: (3) Blending