Deep Learning in Mining Biological Data

By Mufti Mahmud, M. S. Kaiser, T. Mcginnity, Amir Hussain. Cognitive Computation, 2020

333 citations recorded by the source corpus.

Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems. To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/892eb1f9972f5b1a94973066dabf35f6f14963b7

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In Molecular Biology

1. Amir Hussain
2. Mufti Mahmud
3. Xiaodi Huang

Citations in the corpus

• A review of epileptic seizure detection using machine learning classifiers (2020, background): “2 Role of data scientists in epileptic seizure detection Applications of machine learning are significantly seen on health and biological data sets for better outcomes [15, 16].”
• Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023, background): “It is essential to note that the highly efficient predictions of AI models are derived from Deep Neural Networks (DNNs) which originate from extremely complex non-linear statistical models and innumerable parameters, thus compromising the aforementioned algorithms’ transparency [16, 17].”
• Deep Learning Approach for Early Detection of Alzheimer’s Disease (2021, neutral): “…of three layers: (i) the convolution layer performs the feature extraction process, (ii) the pooling layer performs the dimensionality reduction, and (iii) the fully connected layer performs the classification and converts from the two-dimensional matrices into a one-dimensional vector [42].”