ROC and confusion matrix analysis to evaluate the model performance.

<p><b>(A)</b> ROC curve indicates that with the increase of data, model performance is improved. Rapid fluctuations in the TP and FP of ROC curve have been observed because even slight modifications in the predictions may lead to significant variations in these metrics. It is due t...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Rimsha Bibi (20405012) (author)
مؤلفون آخرون:	Noshaba Qasmi (20405009) (author), Sajid Rashid (277111) (author)
منشور في:	2025
الموضوعات:	Biochemistry Molecular Biology Biotechnology Ecology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified revealing significant similarities peptide pdb files larger peptide datasets incorporating additional datasets identify unique cysteine div >< p cysteine pattern barcoding conserved framework combinations advancing pharmacological research supervised learning technique optimizing feature selection venom peptide therapeutics analyzing 151 known level pattern barcodes derived approved peptide binding pattern analysis significant therapeutic potential peptide therapeutic classification known venom therapeutic potential machine learning feature extraction comprehensive analysis venom peptides known targets binding patterns test ratio rich source random forest potentially broadening performance could novel peptides model ’ individual sequences conus </ cone snails bioactive compounds associated frameworks assisted interpretation 48 %) 430 peptides 30 train
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الوصف
الملخص:	<p><b>(A)</b> ROC curve indicates that with the increase of data, model performance is improved. Rapid fluctuations in the TP and FP of ROC curve have been observed because even slight modifications in the predictions may lead to significant variations in these metrics. It is due to small sample sizes for positive and negative classes. AUC is directly proportional to the model performance. Given that the AUC is 0.97, the model performance is significant, and it favors the randomly selected positive instances more. <b>(B)</b> Confusion matrix reveals a balance between the positive and negative datasets, resulting in a high similarity between TP and TN. Dark blue color indicates TP and TN, while light color shows FP and FN. Only 2 FN instances are predicted by model, indicating that although the sequences belong to a positive class, the model predicts them in the negative class. There is no sequence predicted as FP. 9 sequences are predicted as TN and 10 are identified as TP. Overall, the model generates more FNs than FPs.</p>

ROC and confusion matrix analysis to evaluate the model performance.

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