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Evaluation of inductive and transductive inference in the context of translation initiation site

The prediction of Translation Initiation Site (TIS) from a mRNA (Ribonucleic Acid Messenger) is a relevant and latent problem of molecular biology, which has benefited from the evolution of computational techniques of machine learning (ML). There are some machine learning scenarios where the dataset either does not have enough classified sequences to train a precise model, or it does not have an upstream region, such as Caenorhabditis elegans. In this article, we compare the inductive and transductive approaches for TIS prediction, using a methodology that disregards the upstream region. With the proposed methodology, we achieved 95% training accuracy, using only 2.5% of sequences belonging to the Caenorhabditis elegans class, which has many available sequences but does not have the upstream region, and 75% for the Rattus norvegicus class, which has fewer sequences available, using a transductive approach. Our results demonstrate the viability of the transductive approach for scenarios with fewer sequences, a common situation for organisms with incomplete gene sequencing.