Deep Learning in Mining Biological Data

Recent technological advancements in data acquisition tools allowed life to acquire multimodal data from different biological application. Broadly categorized in three types (i. e. , sequences, images, and), these data are huge in amount and complex in nature. Mining such an amount of data for pattern recognition is a big challenge and requires data-intensive machine learning techniques. Artificial neural-based learning systems are well known for their pattern recognition and lately their deep architectures - known as deep learning (DL) - have been successfully applied to solve many complex pattern recognition. Highlighting the role of DL in recognizing patterns in biological, this article provides - applications of DL to biological sequences, , and signals data; overview of open access sources of these data; of open source DL tools applicable on these data; and comparison of tools from qualitative and quantitative perspectives. At the end, it some open research challenges in mining biological data and puts a number of possible future perspectives.