MG-NET: Leveraging Pseudo-imaging for Multi-modal Metagenome Analysis

Aakur, Sathyanarayanan N.; Narayanan, Sai; Indla, Vineela; Bagavathi, Arunkumar; Ramnath, Vishalini Laguduva; Ramachandran, Akhilesh

doi:10.1007/978-3-030-87240-3_57

Cited by 6 publications

(1 citation statement)

References 29 publications

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“…Several different methods for self-supervised representation learning have been proposed, e.g., in the fields of natural language processing (NLP) [4][5][6][7][8] and computer vision (CV) 3,[9][10][11] . However, only a limited number of SSL methods have been developed for bioinformatics, and even fewer for omics data [12][13][14][15] . Thus, SSL has not yet seen such widespread adoption and remains an important and challenging endeavor in this field.…”

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confidence: 99%

A self-supervised deep learning method for data-efficient training in genomics

Gündüz,

Binder,

et al. 2023

Commun Biol

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Deep learning in bioinformatics is often limited to problems where extensive amounts of labeled data are available for supervised classification. By exploiting unlabeled data, self-supervised learning techniques can improve the performance of machine learning models in the presence of limited labeled data. Although many self-supervised learning methods have been suggested before, they have failed to exploit the unique characteristics of genomic data. Therefore, we introduce Self-GenomeNet, a self-supervised learning technique that is custom-tailored for genomic data. Self-GenomeNet leverages reverse-complement sequences and effectively learns short- and long-term dependencies by predicting targets of different lengths. Self-GenomeNet performs better than other self-supervised methods in data-scarce genomic tasks and outperforms standard supervised training with ~10 times fewer labeled training data. Furthermore, the learned representations generalize well to new datasets and tasks. These findings suggest that Self-GenomeNet is well suited for large-scale, unlabeled genomic datasets and could substantially improve the performance of genomic models.

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confidence: 99%

A self-supervised deep learning method for data-efficient training in genomics

Gündüz,

Binder,

et al. 2023

Commun Biol

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Multi-modal Genotype and Phenotype Mutual Learning to Enhance Single-Modal Input Based Longitudinal Outcome Prediction

Ganjdanesh

Zhang

Chen

et al. 2022

Lecture Notes in Computer Science

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Visualizations for universal deep-feature representations: survey and taxonomy

Skopal,

Peška,

Hoksza

et al. 2023

Knowl Inf Syst

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In data science and content-based retrieval, we find many domain-specific techniques that employ a data processing pipeline with two fundamental steps. First, data entities are represented by some visualizations, while in the second step, the visualizations are used with a machine learning model to extract deep features. Deep convolutional neural networks (DCNN) became the standard and reliable choice. The purpose of using DCNN is either a specific classification task or just a deep feature representation of visual data for additional processing (e.g., similarity search). Whereas the deep feature extraction is a domain-agnostic step in the pipeline (inference of an arbitrary visual input), the visualization design itself is domain-dependent and ad hoc for every use case. In this paper, we survey and analyze many instances of data visualizations used with deep learning models (mostly DCNN) for domain-specific tasks. Based on the analysis, we synthesize a taxonomy that provides a systematic overview of visualization techniques suitable for usage with the models. The aim of the taxonomy is to enable the future generalization of the visualization design process to become completely domain-agnostic, leading to the automation of the entire feature extraction pipeline. As the ultimate goal, such an automated pipeline could lead to universal deep feature data representations for content-based retrieval.

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MG-NET: Leveraging Pseudo-imaging for Multi-modal Metagenome Analysis

Cited by 6 publications

References 29 publications

A self-supervised deep learning method for data-efficient training in genomics

A self-supervised deep learning method for data-efficient training in genomics

Multi-modal Genotype and Phenotype Mutual Learning to Enhance Single-Modal Input Based Longitudinal Outcome Prediction

Visualizations for universal deep-feature representations: survey and taxonomy

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