Performance, achieving an accuracy of 99.8\%. TCR sequences significantly outperforms the baselines in terms of predictive Our proposed embedding method on a benchmark dataset of Non-linear) classifiers on the embeddings of TCR sequences for the purpose of We then train different machine learning (linear and To improve the predictive performance of theįinal embeddings, we integrate domain knowledge regarding different types ofĬancer properties. K-mers from the TCR sequences and then apply sparse coding to capture theĮssential features of the data. Sequence that might be missed by low-dimensional methods. The representation of data with a set of informative features and can captureĬomplex relationships between amino acids and identify subtle patterns in the What are the 3 types of sequences The most common types of sequences include the arithmetic sequences, geometric sequences, and Fibonacci sequences. Sparse coding is a popular technique in machine learning that enables This formula states that each term of the sequence is the sum of the previous two terms. Study, we investigate the use of sparse coding for the multi-classĬlassification of TCR protein sequences with cancer categories as target This has led to the discovery of TCRs with potentĪnti-cancer activity and the development of TCR-based immunotherapies. Specificity of TCRs make them ideal for targeting cancer cells, and recentĪdvancements in sequencing technologies have enabled the comprehensive In the immune response against diseases, including cancer. Immune system, and their specific recognition of antigens plays a crucial role T cell receptors (TCRs) are essential proteins for the adaptive Download a PDF of the paper titled T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification, by Zahra Tayebi and 3 other authors Download PDF Abstract: Cancer is a complex disease characterized by uncontrolled cell growth and
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