中文版 | English
Title

Deep Neural Networks on Genetic Motif Discovery: the Interpretability and Identifiability Issues

Author
Name pinyin
ZHANG Yu
School number
11756001
Degree
博士
Discipline
计算机科学
Supervisor
唐珂
Mentor unit
计算机科学与工程系
Tutor of External Organizations
Peter Tino
Tutor units of foreign institutions
伯明翰大学
Publication Years
2022-03-30
Submission date
2022-07-01
University
伯明翰大学
Place of Publication
伯明翰
Abstract

Deep neural networks have made great success in a wide range of research fields and real-world applications. However, as a black-box model, the drastic advances in the performance come at the cost of model interpretability. This becomes a big concern especially for domains that are safety-critical or have ethical and legal requirements (e.g., avoiding algorithmic discrimination). In other situations, interpretability might be able to help scientists gain new ``knowledge'' that is learnt by the neural networks (e.g., computational genomics), and neural network based genetic motif discovery is such a field. It naturally leads us to another question: Can current neural network based motif discovery methods identify the underlying motifs from the data? How robust and reliable is it? In other words, we are interested in the motif identifiability problem.

In this thesis, we first conduct a comprehensive review of the current neural network interpretability research, and propose a novel unified taxonomy which, to the best of our knowledge, provides the most comprehensive and clear categorisation of the existing approaches. Then we formally study the motif identifiability problem in the context of neural network based motif discovery (i.e., if we only have access to the predictive performance of a neural network, which is a black-box, how well can we recover the underlying ``true'' motifs by interpreting the learnt model). Systematic controlled experiments show that although accurate models tend to recover the underlying motifs better, the motif identifiability (a measure of the similarity between true motifs and learnt motifs) still varies in a large range. Also, the over-complexity (without overfitting) of a high-accuracy model (e.g., using 128 kernels while 16 kernels are already good enough) may be harmful to the motif identifiability. We thus propose a robust neural network based motif discovery workflow addressing above issues, which is verified on both synthetic and real-world datasets. Finally, we propose probabilistic kernels in place of conventional convolutional kernels and study whether it would be better to directly learn probabilistic motifs in the neural networks rather than post hoc interpretation. Experiments show that although probabilistic kernels have some merits (e.g., stable output), their performance is not comparable to classic convolutional kernels under the same network setting (the number of kernels).

Keywords
Language
English
Training classes
联合培养
Enrollment Year
2017
Year of Degree Awarded
2022-07
References List

[1] Julius Adebayo et al. “Sanity Checks for Saliency Maps”. Advances in Neural Information Processing Systems. Vol. 31. 2018.
[2] Philip Adler et al. “Auditing black-box models for indirect influence”. Knowledge and Information Systems 54.1 (2018), pp. 95–122.
[3] Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey. “Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning”. Nature Biotechnology 33.8 (2015), pp. 831–838.
[4] Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross. “Towards better understanding of gradient-based attribution methods for Deep Neural Networks”. International Conference on Learning Representations. 2018.
[5] Marco Ancona, Cengiz Oztireli, and Markus Gross. “Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation”. Proceedings of the 36th International Conference on Machine Learning. Vol. 97. 2019.
[6] Robert Andrews, Joachim Diederich, and Alan B Tickle. “Survey and critique of techniques for extracting rules from trained artificial neural networks”. Knowledge-based systems 8.6 (1995), pp. 373–389.
[7] Floriane Anstett-Collin, Lilianne Denis-Vidal, and Gilles Millérioux. “A priori identifiability: An overview on definitions and approaches”. Annual Reviews in Control(2020).
[8] Alejandro Barredo Arrieta et al. “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI”. Information Fusion58 (2020), pp. 82–115.
[9] Sebastian Bach et al. “On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation”. PloS one 10.7 (2015), e0130140.
[10] David Baehrens et al. “How to explain individual classification decisions”. The Journal of Machine Learning Research 11 (2010), pp. 1803–1831.
[11] Timothy L Bailey. “DREME: motif discovery in transcription factor ChIP-seq data”.Bioinformatics 27.12 (2011), pp. 1653–1659.
[12] Timothy L Bailey, Mikael Boden, et al. “MEME SUITE: tools for motif discovery and searching”. Nucleic acids research (2009).
[13] Timothy L Bailey and Charles Elkan. “Fitting a mixture model by expectation maximization to discover motifs in biopolymers”. Proceedings of the Second InternationalConference on Intelligent Systems for Molecular Biology. 1994.
[14] Pierre Baldi, Peter Sadowski, and Daniel Whiteson. “Searching for exotic particles in high-energy physics with deep learning”. Nature communications 5.1 (2014), pp. 1–9.
[15] Andrew R Barron. “Approximation and estimation bounds for artificial neural networks”. Machine learning 14.1 (1994), pp. 115–133.
[16] David Bau, Bolei Zhou, et al. “Network dissection: Quantifying interpretability of deep visual representations”. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017, pp. 6541–6549.
[17] David Bau, Jun-Yan Zhu, et al. “GAN Dissection: Visualizing and Understanding Generative Adversarial Networks”. Proceedings of the International Conference on Learning Representations (ICLR). 2019.
[18] Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal. “Reconciling modern machine-learning practice and the classical bias–variance trade-off”. Proceedings of theNational Academy of Sciences 116.32 (2019), pp. 15849–15854.
[19] J. M. Benitez, J. L. Castro, and I. Requena. “Are artificial neural networks black boxes?” IEEE Transactions on Neural Networks 8 (1997).
[20] Jacob Bien and Robert Tibshirani. “Prototype selection for interpretable classification”. The Annals of Applied Statistics 5.4 (2011), pp. 2403–2424.
[21] Francesco Bodria et al. “Benchmarking and survey of explanation methods for black box models”. arXiv preprint arXiv:2102.13076 (2021).
[22] Thierry Bouwmans, Sajid Javed, Maryam Sultana, and Soon Ki Jung. “Deep neural network concepts for background subtraction: A systematic review and comparative evaluation”. Neural Networks 117 (2019), pp. 8–66.
[23] Olcay Boz. “Extracting decision trees from trained neural networks”. Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. 2002, pp. 456–461.
[24] Tom Brown et al. “Language Models are Few-Shot Learners”. Advances in Neural Information Processing Systems. Vol. 33. 2020, pp. 1877–1901.
[25] Joan Bruna and Stéphane Mallat. “Invariant scattering convolution networks”. IEEE transactions on pattern analysis and machine intelligence 35.8 (2013), pp. 1872–1886.
[26] Rich Caruana et al. “Case-based explanation of non-case-based learning methods.” Proceedings of the AMIA Symposium. 1999, p. 212.
[27] J. L. Castro, C. J. Mantas, and J. M. Benitez. “Interpretation of artificial neural networks by means of fuzzy rules”. IEEE Transactions on Neural Networks 13 (2002)...
[232] Luisa M Zintgraf, Taco S Cohen, Tameem Adel, and Max Welling. “Visualizing Deep Neural Network Decisions: Prediction Difference Analysis”. International Conference on Learning Representations. 2017.

Data Source
人工提交
Document TypeThesis
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/347870
DepartmentDepartment of Computer Science and Engineering
Recommended Citation
GB/T 7714
Zhang Y. Deep Neural Networks on Genetic Motif Discovery: the Interpretability and Identifiability Issues[D]. 伯明翰. 伯明翰大学,2022.
Files in This Item:
File Name/Size DocType Version Access License
11756001-张宇-计算机科学与工程(11669KB) Restricted Access--Fulltext Requests
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Export to Excel
Export to Csv
Altmetrics Score
Google Scholar
Similar articles in Google Scholar
[张宇]'s Articles
Baidu Scholar
Similar articles in Baidu Scholar
[张宇]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[张宇]'s Articles
Terms of Use
No data!
Social Bookmark/Share
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.