**4.1. Use of machine learning algorithms for classification of functional siRNAs**

After many years of research about the guidelines for selection of effective siRNAs, we are a few steps ahead in the process of improving the targeting success rate. But for better targeting success, the siRNA selection parameters provided in various guidelines needs to be optimized. Still there is no reliable guideline for optimization of weights of siRNA selection parameter. Machine learning algorithms like Support vector machine or artificial neural network can serve excellent purpose, when trained with sufficient volume of biologically validated siRNA data sets [11]. Some online siRNA designing tools (like BioPredsi and Genescript siRNA target finder) use machine learning algorithms for classification of effective siRNAs from non-effective ones.
