XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data

Withnell, Eloise; Zhang, Xiaoyu; Sun, Kai; Guo, Yike

doi:10.1093/bib/bbab315

Cited by 48 publications

(37 citation statements)

References 39 publications

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“…Table 1 provides a summary of the distribution of the examples in the dataset after the splitting before the data augmentation step. The MOT model metric scores are presented in the table 2 alongside with metric scores from OmiVAE (26), OmiEmbed (35), XOmiVAE (36) and Gene-Transfomer (37). OmiEmbed is an extension of OmiVAE that integrated a multi-task aspect to the original model previously introduced.…”

Section: Resultsmentioning

confidence: 99%

MOT: a Multi-Omics Transformer for multiclass classification tumour types predictions

Osseni

Tossou

Laviolette

et al. 2022

Preprint

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Motivation Breakthroughs in high-throughput technologies and machine learning methods have enabled the shift towards multi-omics modelling as the preferred means to understand the mechanisms underlying biological processes. Machine learning enables and improves complex disease prognosis in clinical settings. However, most multi-omic studies primarily use transcriptomics and epigenomics due to their over-representation in databases and their early technical maturity compared to others omics. For complex phenotypes and mechanisms, not leveraging all the omics despite their varying degree of availability can lead to a failure to understand the underlying biological mechanisms and leads to less robust classifications and predictions. Results We proposed MOT (Multi-Omic Transformer), a deep learning based model using the transformer architecture, that discriminates complex phenotypes (herein cancer types) based on five omics data types: transcriptomics (mRNA and miRNA), epigenomics (DNA methylation), copy number variations (CNVs), and proteomics. This model achieves an F1-score of $98.37\%$ among 33 tumour types on a test set without missing omics views and an F1-score of $96.74\%$ on a test set with missing omics views. It also identifies the required omic type for the best prediction for each phenotype and therefore could guide clinical decision-making when acquiring data to confirm a diagnostic. The newly introduced model can integrate and analyze five or more omics data types even with missing omics views and can also identify the essential omics data for the tumour multiclass classification tasks. It confirms the importance of each omic view. Combined, omics views allow a better differentiation rate between most cancer diseases. Our study emphasized the importance of multi-omic data to obtain a better multiclass cancer classification. Availability and implementation: MOT source code is available at \url{https://github.com/dizam92/multiomic_predictions}.

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Section: Resultsmentioning

confidence: 99%

MOT: a Multi-Omics Transformer for multiclass classification tumour types predictions

Osseni

Tossou

Laviolette

et al. 2022

Preprint

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“…Deep learning has become the mainstream of machine learning, and with the expansion of its applications, technical problems from the perspective of real-world problem solving have also emerged, primarily the black box problem: neural network machinelearning in deep learning is a black box [130][131][132][133][134][135][136][137][138][139][140][141][142][143][144]. The learning results are reflected in the node weights, and the obtained regularities and models are not represented in a form that humans can directly understand.…”

Section: Black Box Problemmentioning

confidence: 99%

Applications of Deep Learning for Drug Discovery Systems with BigData

Matsuzaka

Yashiro

2022

BioMedInformatics

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The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process of pharmaceutical research and development, is progressing. By using the ability to process large amounts of data, which is a characteristic of AI, and achieving advanced data analysis and inference, there are benefits such as shortening development time, reducing costs, and reducing the workload of researchers. There are various problems in drug development, but the following two issues are particularly problematic: (1) the yearly increases in development time and cost of drugs and (2) the difficulty in finding highly accurate target genes. Therefore, screening and simulation using AI are expected. Researchers have high demands for data collection and the utilization of infrastructure for AI analysis. In the field of drug discovery, for example, interest in data use increases with the amount of chemical or biological data available. The application of AI in drug discovery is becoming more active due to improvement in computer processing power and the development and spread of machine-learning frameworks, including deep learning. To evaluate performance, various statistical indices have been introduced. However, the factors affected in performance have not been revealed completely. In this study, we summarized and reviewed the applications of deep learning for drug discovery with BigData.

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“…In [ 165 ], a VAE for classifying cancer from the gene expression profiles was modified to identify which genes contributed to the classification. As the VAE learned to reconstruct the data, a classifying neural network that was connected to the bottleneck layer learned the mapping between the input data and the labels.…”

Section: Challengesmentioning

confidence: 99%

Omics Data and Data Representations for Deep Learning-Based Predictive Modeling

Tsimenidis

Vrochidou

Papakostas

2022

IJMS

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Medical discoveries mainly depend on the capability to process and analyze biological datasets, which inundate the scientific community and are still expanding as the cost of next-generation sequencing technologies is decreasing. Deep learning (DL) is a viable method to exploit this massive data stream since it has advanced quickly with there being successive innovations. However, an obstacle to scientific progress emerges: the difficulty of applying DL to biology, and this because both fields are evolving at a breakneck pace, thus making it hard for an individual to occupy the front lines of both of them. This paper aims to bridge the gap and help computer scientists bring their valuable expertise into the life sciences. This work provides an overview of the most common types of biological data and data representations that are used to train DL models, with additional information on the models themselves and the various tasks that are being tackled. This is the essential information a DL expert with no background in biology needs in order to participate in DL-based research projects in biomedicine, biotechnology, and drug discovery. Alternatively, this study could be also useful to researchers in biology to understand and utilize the power of DL to gain better insights into and extract important information from the omics data.

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XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data

Cited by 48 publications

References 39 publications

MOT: a Multi-Omics Transformer for multiclass classification tumour types predictions

MOT: a Multi-Omics Transformer for multiclass classification tumour types predictions

Applications of Deep Learning for Drug Discovery Systems with BigData

Omics Data and Data Representations for Deep Learning-Based Predictive Modeling

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