Translation Initiation Sites (TIS) are critical for protein synthesis, and accurately identifying non-canonical sites can advance our understanding of gene expression regulation across different species. This project leverages machine learning to predict non-canonical TIS, emphasizing scalability and accuracy.
TISCalling is an open-source project dedicated to predicting non-canonical Translation Initiation Sites (TIS) in transcriptomic data across various species. The project uses advanced machine learning techniques to develop a robust classifier that accurately identifies non-canonical TIS locations based on sequence data and other relevant features.
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