This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
Research Article
Yousof Gheisari
Isfahan University of Medical Sciences
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Despite enormous achievements in production of high throughput datasets, constructing comprehensive maps of interactions remains a major challenge. The lack of sufficient experimental evidence on interactions is more significant for heterogeneous molecular types. Hence, developing strategies to predict inter-omics connections is essential to construct holistic maps of disease. Here, Data Integration with Deep Learning (DIDL), a novel nonlinear deep learning method is proposed to predict inter-omics interactions. It consists of an encoder that automatically extracts features for biomolecules according to existing interactions, and a decoder that predicts novel interactions. The applicability of DIDL is assessed with different networks namely drug-target protein, transcription factor-DNA element, and miRNA-mRNA. Also, the validity of novel predictions is assessed by literature surveys. Furthermore, DIDL outperformed state-of-the-art methods. Area under the curve, and area under the precision-recall curve for all three networks were more than 0.85 and 0.83, respectively. DIDL has several advantages like automatic feature extraction from raw data, end-to-end training, and robustness to sparsity. In addition, tensor decomposition structure, predictions solely based on existing interactions and biochemical data independence makes DIDL applicable for a variety of biological networks. DIDL paves the way to understand the underlying mechanisms of complex disorders through constructing integrative networks.
Keywords
deep learning, integrative networks, datasets
No competing interests reported.
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
View the published article at BMC Bioinformatics.
Version 1
Posted 09 Jun, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Learn more about our company.
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
Yousof Gheisari
Isfahan University of Medical Sciences
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
View the published article at BMC Bioinformatics.
Version 1
Posted 09 Jun, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Bioinformatics.
Despite enormous achievements in production of high throughput datasets, constructing comprehensive maps of interactions remains a major challenge. The lack of sufficient experimental evidence on interactions is more significant for heterogeneous molecular types. Hence, developing strategies to predict inter-omics connections is essential to construct holistic maps of disease. Here, Data Integration with Deep Learning (DIDL), a novel nonlinear deep learning method is proposed to predict inter-omics interactions. It consists of an encoder that automatically extracts features for biomolecules according to existing interactions, and a decoder that predicts novel interactions. The applicability of DIDL is assessed with different networks namely drug-target protein, transcription factor-DNA element, and miRNA-mRNA. Also, the validity of novel predictions is assessed by literature surveys. Furthermore, DIDL outperformed state-of-the-art methods. Area under the curve, and area under the precision-recall curve for all three networks were more than 0.85 and 0.83, respectively. DIDL has several advantages like automatic feature extraction from raw data, end-to-end training, and robustness to sparsity. In addition, tensor decomposition structure, predictions solely based on existing interactions and biochemical data independence makes DIDL applicable for a variety of biological networks. DIDL paves the way to understand the underlying mechanisms of complex disorders through constructing integrative networks.
No competing interests reported.
Loading...