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Biocomputing and Synthetic Biology in Cells: Cells Special Issue

Feifei Cui, Quan Zou
2020 Cells  
Biocomputing and synthetic biology have been two of the most exciting emerging fields in recent years [...]  ...  [8] proposes a model ensemble of classifiers for the identification of enhancers using deep learning methods.  ...  [1] presents a framework for the detection of the interactive gene groups for scRNA-seq data based on co-expression network analysis and subgraph learning.  ... 
doi:10.3390/cells9112459 pmid:33187277 fatcat:2zpyjpfyr5b6dkwfsgmxpeto7y

Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites

Haoyue Fu, Lianping Yang, Xiangde Zhang
2017 Current Genomics  
Based on the large amount of transcription factor binding sites predicting values in the deep learning models, further computation and analysis have been done to reveal the relationship between the gene  ...  As for the deep learning methods to predict the TFBSs, we discussed the related problems, such as the raw data preprocessing, the structure design of the deep convolution neural network (CNN) and the model  ...  ACKNOWLEDGEMENTS We would like to express our gratitude to all those who helped us during the writing of this paper. HF thanks Xiaojun Lu and Qingsong Tang for useful discussions and advices.  ... 
doi:10.2174/1389202918666170228143619 pmid:29081688 pmcid:PMC5635616 fatcat:dvcgverqbvanfmgqcvi542lxcy

DeepProfile: Deep learning of cancer molecular profiles for precision medicine [article]

Ayse Berceste Dincer, Safiye Celik, Naozumi Hiranuma, Su-In Lee
2018 bioRxiv   pre-print
We present the DeepProfile framework, which learns a variational autoencoder (VAE) network from thousands of publicly available gene expression samples and uses this network to encode a low-dimensional  ...  To our knowledge, DeepProfile is the first attempt to use deep learning to extract a feature representation from a vast quantity of unlabeled (i.e, lacking phenotype information) expression samples that  ...  We present DeepProfile, which uses VAEs to learn an unsupervised neural network model of gene expression from thousands of cancer patients, and then uses this model to encode an LDR to predict complex  ... 
doi:10.1101/278739 fatcat:fmxscskmfjhktlwstmvpzovv5i

A Novel Machine Learning Systematic Framework and Web Tool for Breast Cancer Survival Rate Assessment [article]

Jonathan M. Ji, Wen H. Shen
2022 medRxiv   pre-print
The CNN model demonstrates a powerful ability to be used as a systematic framework for real time prediction by end users.  ...  This machine learning project employs multiple machine learning approaches, including a novel deep learning algorithm, in building models for the detection and visualization of significant prognostic indicators  ...  The CNN model demonstrates a powerful ability to be used as a systematic framework for real time prediction by end users.  ... 
doi:10.1101/2022.09.16.22280052 fatcat:ff4oapkr3jdkhn3ojwktkm7rp4

Learning Deep Attribution Priors Based On Prior Knowledge [article]

Ethan Weinberger, Joseph Janizek, Su-In Lee
2020 arXiv   pre-print
Feature attribution methods, which explain an individual prediction made by a model as a sum of attributions for each input feature, are an essential tool for understanding the behavior of complex deep  ...  Our framework jointly learns a relationship between prior information and feature importance, as well as biases models to have explanations that rely on features predicted to be important.  ...  Acknowledgments and Disclosure of Funding  ... 
arXiv:1912.10065v3 fatcat:n6s3ioxto5e5baelmt37iteb5y

Deep Learning Deepens the Analysis of Alternative Splicing

Xudong Zou, Xin Gao, Wei Chen
2019 Genomics, Proteomics & Bioinformatics  
Now, Yi Xing's lab reported DARTS [1], a novel computational framework that leverages the power of both deep learning and Bayes hierarchical framework for differential alternative splicing (AS) analysis  ...  In recent years, deep learning has been driving the next wave of artificial intelligence and machine learning.  ...  The major innovation of DARTS lies in two aspects. (1) DARTS combines a deep learning model with Bayes hierarchical framework: the former provides the latter a prior based on learned knowledge about each  ... 
doi:10.1016/j.gpb.2019.05.001 pmid:31100357 pmcid:PMC6620263 fatcat:zo4ujnrmqbdp3kt4k7467srtbq

A Generative Adversarial Network Model for Disease Gene Prediction with RNA-seq Data

Xue Jiang, Jingjing Zhao, Wei Qian, Weichen Song, Guan Ning Lin
2020 IEEE Access  
Based on this model, we further designed a framework to predict disease genes with RNA-seq data.  ...  The deep learning model improves the identification accuracy of disease genes over the-state-of-the-art approaches.  ...  The generative adversarial network is a deep learning framework, which first puts the generative model and discriminative model into one learning framework [16] .  ... 
doi:10.1109/access.2020.2975585 fatcat:ublickg45ndbbmdfxkeej77k7i

Emerging Artificial Intelligence Applications in Spatial Transcriptomics Analysis [article]

Yijun Li, Stefan Stanojevic, Lana X. Garmire
2022 arXiv   pre-print
This review provides a comprehensive and up-to-date survey of current AI methods for ST analysis.  ...  Many artificial intelligence (AI) methods have been developed to utilize various machine learning and deep learning techniques for computational ST analysis.  ...  Acknowledgements This work was supported by grants from the National Library of Medicine (NLM; Grant No.  ... 
arXiv:2203.09664v1 fatcat:om6cen2vsrai3jgrkrvolxpg3q

Mining influential genes based on deep learning

Lingpeng Kong, Yuanyuan Chen, Fengjiao Xu, Mingmin Xu, Zutan Li, Jingya Fang, Liangyun Zhang, Cong Pian
2021 BMC Bioinformatics  
Results Here, we propose a computational framework based on deep learning to mine a subset of genes that can cover more genomic information.  ...  Although a set of ~ 1000 carefully chosen landmark genes that can capture ~ 80% of information from the whole genome has been identified for use in L1000, the robustness of using these landmark genes to  ...  Acknowledgements This work was supported by the high-performance computing platform of Bioinformatics Center, Nanjing Agricultural University.  ... 
doi:10.1186/s12859-021-03972-5 pmid:33482718 fatcat:ih5j7g5t75bsrjqtyvb3dejaba

MetastaSite: Predicting metastasis to different sites using deep learning with gene expression data

Somayah Albaradei, Abdurhman Albaradei, Asim Alsaedi, Mahmut Uludag, Maha A. Thafar, Takashi Gojobori, Magbubah Essack, Xin Gao
2022 Frontiers in Molecular Biosciences  
Here we propose a computational framework that takes the gene expression profile of any primary cancer sample and predicts whether patients' samples are primary (localized) or metastasized to the brain  ...  We further designed the model's workflow to provide a second functionality beyond metastasis site prediction, i.e., to identify the biological functions that the DL model uses to perform the prediction  ...  Deep learning framework The first part of our model's framework comprises three key components, namely the AE (Hinton and Salakhutdinov, 2006) , DeepLIFT (Shrikumar et al., 2017) , and the deep neural  ... 
doi:10.3389/fmolb.2022.913602 pmid:35936793 pmcid:PMC9353773 fatcat:upvr2fr24veefj75phjravzfhy

Gene Expression-Assisted Cancer Prediction Techniques

Tanima Thakur, Isha Batra, Monica Luthra, Shanmuganathan Vimal, Gaurav Dhiman, Arun Malik, Mohammad Shabaz, Dmitry Zaitsev
2021 Journal of Healthcare Engineering  
Gene expression is a process that is used to convert deoxyribose nucleic acid (DNA) to ribose nucleic acid (RNA) and then RNA to protein.  ...  There are many techniques available in the literature to predict cancerous and noncancerous genes from gene expression data.  ...  Conflicts of Interest e authors declare that there are no conflicts of interest regarding the publication of this article.  ... 
doi:10.1155/2021/4242646 pmid:34545300 pmcid:PMC8449724 fatcat:6qh3yl3dkjhipiq46v3c2cmq5m

DeepEP: a deep learning framework for identifying essential proteins

Min Zeng, Min Li, Fang-Xiang Wu, Yaohang Li, Yi Pan
2019 BMC Bioinformatics  
We develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins.  ...  We demonstrate that DeepEP improves the prediction performance by integrating multiple deep learning techniques and a sampling method. DeepEP is more effective than existing methods.  ...  DeepEP and comparing models (using gene expression profiles combined with different central indexes (DC, CC, EC, BC, NC, and LAC)) Fig. 4 ROC and PR curves of DeepEP and models which use gene expression  ... 
doi:10.1186/s12859-019-3076-y pmid:31787076 pmcid:PMC6886168 fatcat:m4ub3ui44bcdtfcuw34kv4g3ha

GNE: A deep learning framework for gene network inference by aggregating biological information [article]

Kishan KC, Rui Li, Feng Cui, Anne Haake
2018 biorxiv/medrxiv   pre-print
We propose a scalable and robust deep learning framework to learn embedded representations to unify known gene interactions and gene expression for gene interaction predictions.  ...  We compare the predictive power of our deep embeddings to the state-of-the-art machine learning methods. The results suggest that our deep embeddings achieve significantly more accurate predictions.  ...  The gene expression data was downloaded from DREAM5 Network Challenge https://!Synapse:syn2787209/wiki/70351.  ... 
doi:10.1101/300996 fatcat:qzynkhdnobfwllxvpdxvfed664

Integrative framework of cross-module deep biomarker for the prognosis of clear cell renal cell carcinoma [article]

Zhenyuan Ning, Weihao Pan, Qing Xiao, Yuting Chen, Xinsen Zhang, Jiaxiu Luo, Jian Wang, Yu Zhang
2019 bioRxiv   pre-print
A deep biomarker-based integrative framework was proposed to construct a prognostic model.  ...  Deep features extracted from CT and histopathological images by using deep learning combined with eigengenes generated from functional genomic data were used to predict ccRCC prognosis.  ...  The advantages included the following. 1) We first proposed the integrative framework of a cross-module deep biomarker for ccRCC prognosis, and the framework with core codes is shared. 2) Deep learning  ... 
doi:10.1101/746818 fatcat:zk2ttrhxafhj3alpaktzzguojy

DeepChrome: deep-learning for predicting gene expression from histone modifications

Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi
2016 Bioinformatics  
This paper develops a unified discriminative framework using a deep convolutional neural network to classify gene expression using histone modification data as input.  ...  Computational methods that predict gene expression from histone modification signals are highly desirable for understanding their combinatorial effects in gene regulation.  ...  Discussion We have presented DeepChrome, a deep learning framework that not only accurately classifies gene expression levels using histone modifications as input, but also learns combinatorial relationships  ... 
doi:10.1093/bioinformatics/btw427 pmid:27587684 fatcat:zhxtdzrw2rbt3lmnnbgflinqmq
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