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The task of finding transcription start sites (TSSs) can be modeled as a classification problem. Semi-Supervised Support Vector Machines (S3VMs) are an appealing method for using unlabeled data in classification. Based incorporation prior biological knowledge for recognizing TSSs, propose a Self-Training S3VMs (ST-S3VMs) algorithm. ST-S3VM builds a SVM classifier based small amounts of labeled data...
We present a classification algorithm built on our adaptation of the Generalized Lotka-Volterra model, well-known in mathematical ecology. The training algorithm itself consists only of computing several scalars, per each training vector, using a single global user parameter and then solving a linear system of equations. Construction of the system matrix is driven by our model and based on kernel...
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