Abstract:
Cardiovascular Disease (CVD) is a major global health issue, with thrombosis, or blood clot
formation, as a key factor. A disintegrin-like and metalloproteinase with thrombospondin type 1
repeats 13 (ADAMTS13), a protease that cleaves the ultra-large von Willebrand factor (ULVWF),
regulates thrombosis by preventing plug development. Despite its importance, ADAMTS13
regulation remains poorly understood. This study explores platelet plug formation, focusing on
ADAMTS13's role in clot regulation. We developed a knowledge-driven biological regulatory
network (BRN) and built four classification models using protein data from STRING and
DISGENET to distinguish proteins linked to ADAMTS13, CVD, and thrombosis from those
related only to CVD and thrombosis. The models, including support vector classifier, random
forest, logistic regression, and ANN, were optimized using GridSearchCV. The logistic regression
and ANN models showed strong performance, with the accuracy rates of 87.05% and 88.82%,
respectively. The ANN model demonstrated a balanced performance between precision (83.82%)
and recall (87.69%). Thrombin and plasmin were identified as ADAMTS13 inhibitors from BRN,
offering insights into regulation and potential therapeutic targets. ADAMTS13 mRNA secondary
structure was predicted using RNAfold, though reliability was limited by mRNA dynamics.
This study investigates ADAMTS13 regulation and its role in thrombosis and CVD, using
computational approaches to deepen understanding of the molecular mechanisms. Future work
should aim to probe the regulatory mechanistic of ADAMTS13, enhance classification models
performance, and predict its full-length protein structure for insights into its functional mechanism.
