Deep learning for computational biology

By Christof Angermueller, Tanel Pärnamaa, Leopold Parts, Oliver Stegle. 2016

1,263 citations recorded by the source corpus.

Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.

Subfields: Biophysics, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Generative Counterfactual Introspection for Explainable Deep Learning (2019, background): “The recent success of deep neural networks has lead to many breakthroughs in various application domains [1]–[3].”
Opportunities and obstacles for deep learning in biology and medicine (2017, background): “In image analysis, previous examples of deep transfer learning applications proved large-scale natural image sets [42] to be useful for pre-training models that serve as generic feature extractors for various types of biological images [14,206,414,415].”
Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation (2022, background): “Deep neural networks have achieved outstanding results in various applications including computer vision [13, 29, 31, 32, 38], natural language processing [45, 47], speech recognition [5, 10], robotics [25], bioinformatics [6], and many others.”
Biological Sequence Modeling with Convolutional Kernel Networks (2017, neutral): “This has been the case for computer vision [22], natural language processing [10], audio signals [23], and more recently biological sequences [46, 1, 2].”
Deep Learning for Health Informatics (2017, methodology): “just one genome, a large scale of DNA sequences and interactions mediated by various distant regulatory factors should be used [122].”
Development and evaluation of a deep learning model for protein–ligand binding affinity prediction (2017, methodology): “Deep learning has been relatively widely used by the bioinformatics (Leung et al. , 2014; Alipanahi et al. , 2015; Park and Kellis, 2015; Jurtz et al. , 2017; Jim´enez et al. , 2017) and computational biology community (Angermueller et al. , 2016).”
Fooling Neural Network Interpretations via Adversarial Model Manipulation (2019, positive): “, 2017) , policy-making (Brennan & Oliver, 12; Goodman & Flaxman, 2016), and science (Angermueller et al., 2016).”
Deep learning for cellular image analysis (2019, background): “It builds on prior reviews of the intersection of deep learning and the life sciences 42,45–47 by incorporating a discussion of our labs’ joint experiences in applying these methods to cellular imaging data, and is meant to make these methods less opaque to new adopters.”
ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples (2019, positive): “CNNs are most widely used in image processing[39, 41], but have been successfully applied in various fields[42, 43], including analysis of biological sequences such as DNA and amino acid sequences[44, 45, 46].”
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Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis (2017, background): “While other biomedical data such as medical images or genomic information exist and are covered in recent relevant articles [25], [27], [28], in this”
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