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A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data
2016
BMC Genomics
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machine (SVM) and Random Forest (RF)). ...
The ability to sequence the transcriptomes of single cells using single-cell RNA-seq sequencing technologies presents a shift in the scientific paradigm where scientists, now, are able to concurrently ...
Methods
Data preparation for Single-cell RNA-Seq Single-cell RNA gene expression profiles of neural cells from Pollen et al. ...
doi:10.1186/s12864-016-3317-7
pmid:28155657
pmcid:PMC5260093
fatcat:z747ov5elrgdxhqyba6qwjfdau
Integrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data
2022
BioData Mining
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Finally, we show how the latent structure learned by the network could be used to visualize and to interpret the composition of human single cell datasets. ...
Background Single-cell RNA sequencing (scRNA-seq) data provide valuable insights into cellular heterogeneity which is significantly improving the current knowledge on biology and human disease. ...
Authors' contributions PG, CL and IN carried out the analysis and prepared the scripts, IAA performed the biological interpretation of the results, IN, CL and JD wrote the manuscript. ...
doi:10.1186/s13040-021-00285-4
pmid:34980200
pmcid:PMC8722116
fatcat:bij2xqwlbjadxivdazk3jnldce
Single cell RNA sequencing reveals cellular diversity of trisomy 21 retina
[article]
2019
bioRxiv
pre-print
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Our analyses identified extensive communication networks between distinct cellular types, among which a few ligand-receptor interactions were associated with the development of retina and immunoregulatory ...
Retina is a crucial tissue for the capturing and processing of light stimulus. Characterization of the retina at single cell level is essential for the understanding of its biological functions. ...
Finally, we trained a machine learning model using random decision forests to make classification of the 21 cells from normal cells. ...
doi:10.1101/614149
fatcat:ttj23shkkfbvbdrkthvizgmtau
Machine Learning Model for Lymph Node Metastasis Prediction in Breast Cancer Using Random Forest Algorithm and Mitochondrial Metabolism Hub Genes
2021
Applied Sciences
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Herein, a new machine learning model based on RNA-seq data using the random forest algorithm and hub genes to estimate the accuracy of breast cancer metastasis prediction. ...
Single-cell breast cancer samples (56 metastatic and 38 non-metastatic samples) were obtained from the Gene Expression Omnibus database, and the Weighted Gene Correlation Network Analysis package was used ...
Conflicts of Interest: The authors declare no conflict of interest. Appl. Sci. 2021, 11, 2897 ...
doi:10.3390/app11072897
fatcat:zyfxikwuvvdb5ozexkcp7ztyla
A single-cell atlas of the human brain in Alzheimer's disease and its implications for personalized drug repositioning
[article]
2022
bioRxiv
pre-print
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Although single-cell RNA sequencing (scRNA-seq) has been performed in different regions of postmortem AD brains, the common and distinct molecular features among different regions remains largely unclear ...
Moreover, we explored the transcriptional heterogeneity of different AD subtypes at the single-cell level. ...
Acknowledgements GP and ZZ conceived the study. GP performed scRNA/snRNA-seq and bulk RNA-seq bioinformatics analysis and random forest model construction. ...
doi:10.1101/2022.06.14.496100
fatcat:367kdxxbx5h2npu2ay26af2tk4
Machine Learning-Based State-Of-The-Art Methods For The Classification Of RNA-Seq Data
[article]
2017
bioRxiv
pre-print
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RNA-Seq measures expression levels of several transcripts simultaneously. The identified reads can be gene, exon, or other region of interest. ...
In this chapter, we are going to discuss various machine learning approaches for RNA-Seq data classification and their implementation. ...
of RNA-Seq data
Random forests (RF) RF is also an example for ensemble method. ...
doi:10.1101/120592
fatcat:frdzqa4awvbuddkp4vxmyvyo2q
Computational dynamic approaches for temporal omics data with applications to systems medicine
2017
BioData Mining
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However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for ...
Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. ...
Availability of data and materials Data sharing not applicable to this article as no datasets were generated or analysed during the current study. ...
doi:10.1186/s13040-017-0140-x
pmid:28638442
pmcid:PMC5473988
fatcat:rscvtjlpgrf53fbwlt6t4i22em
Roles of the Immune/Methylation/Autophagy Landscape on Single-Cell Genotypes and Stroke Risk in Breast Cancer Microenvironment
2021
Oxidative Medicine and Cellular Longevity
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Single cells were genotyped through integrated scRNA-seq of the TNBC samples based on clustering results of BCPRS-related genes. ...
Neural network-based deep learning models using BCPRS-related genes showed that these genes can be used to map the tumor microenvironment. ...
This work was supported by grants from the National Natural Science Foundation of China (8197032698).
Supplementary Materials Supplementary tables and figures. (Supplementary Materials) ...
doi:10.1155/2021/5633514
pmid:34457116
pmcid:PMC8397558
fatcat:ziqhnnum2napll643ml5qmrt2i
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
2020
Genome Biology
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With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. ...
Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. ...
JG supervised by OS processed the real scRNA-seq data. JPP constructed the SCENIC gene regulatory networks. EM and HH provided the PBMC single-cell data and supported the corresponding analysis. ...
doi:10.1186/s13059-020-1949-z
pmid:32051003
fatcat:7q7no5lncrgazg25zw7rpxngui
Machine Learning-Based State-of-the-Art Methods for the Classification of RNA-Seq Data
[chapter]
2017
Lecture Notes in Computational Vision and Biomechanics
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RNA-Seq measures expression levels of several transcripts simultaneously. The identified reads can be gene, exon, or other region of interest. ...
In this chapter, we are going to discuss various machine learning approaches for RNA-Seq data classification and their implementation. ...
classification of RNA-Seq data
Random forests (RF) RF is also an example for ensemble method. ...
doi:10.1007/978-3-319-65981-7_6
fatcat:ybc2r3cx5vdsnexel3bqq3rinm
PIKE-R2P: Protein–protein interaction network-based knowledge embedding with graph neural network for single-cell RNA to protein prediction
2021
BMC Bioinformatics
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Background Recent advances in simultaneous measurement of RNA and protein abundances at single-cell level provide a unique opportunity to predict protein abundance from scRNA-seq data using machine learning ...
Then, we propose a novel method for single-cell RNA to protein prediction named PIKE-R2P, which incorporates protein–protein interactions (PPI) and prior knowledge embedding into a graph neural network ...
The full contents of the supplement are available online at https:// bmcbi oinfo rmati cs. biome dcent ral. com/ artic les/ suppl ements/ volume-22-suppl ement-6. ...
doi:10.1186/s12859-021-04022-w
pmid:34078261
fatcat:r477l2nzvbcepb77btez2zuwbu
Gene Expression Profiling Indicated Diverse Functions and Characteristics of Core Genes in Pea Aphid
2020
Insects
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Furthermore, we found reliable markers using random forest methodology to distinguish different morphs of pea aphids. ...
Moreover, we determined the expression features and co-expression networks of highly variable genes. We also used the ARACNe method to obtain 263 core genes related to different biological pathways. ...
Acknowledgments: We thank those who help us in sample collection and technical assistance.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/insects11030186
pmid:32183501
pmcid:PMC7142545
fatcat:4j3hyktx7ndpll74iqeeqr75ou
PrismExp: Predicting Human Gene Function by Partitioning Massive RNA-seq Co-expression Data
[article]
2021
bioRxiv
pre-print
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In the past, we showed that RNA-seq co-expression data is highly predictive of gene function and protein-protein interactions. ...
With coexpression data from ARCHS4, we apply PrismExp to predict a wide variety of gene functions, including pathway membership, phenotypic associations, and protein-protein interactions. ...
ACKNOWLEDGEMENTS This work was partially supported by the National Institutes of Health (NIH) grants U54-HL127624 (LINCS-DCIC) and U24-CA224260 (IDG-KMC) to AM.
Bibliography ...
doi:10.1101/2021.01.20.427528
fatcat:yipqpmuqobgjtbobsegfya4u7q
Revealing routes of cellular differentiation by single-cell RNA-seq
2018
Current Opinion in Systems Biology
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Differentiation of multipotent stem cells is controlled by the intricate regulatory interactions of thousands of genes. ...
The recent availability of large-scale sensitive single-cell RNAseq protocols has enabled the generation of snapshot data covering the entire spectrum of cell states in a system of interest. ...
Acknowledgements I thank Nina Cabezas-Wallscheid, Roman Sankowski, and Josip Herman for critical reading of the manuscript. The work was financially supported by the Max Planck Society. ...
doi:10.1016/j.coisb.2018.07.006
fatcat:t44u27lsarei7n2osuwvqlubay
Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq
[article]
2022
bioRxiv
pre-print
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Cell-cell interactions are the fundamental building blocks of tissue organization and multicellular life. ...
We developed Neighbor-seq, a method to identify and annotate the architecture of direct cell-cell interactions and relevant ligand-receptor signaling from the undissociated cell fractions in massively ...
Schematic diagram of key Neighbor-seq steps:(1) single-cell sequencing, cell type clustering, and marker gene identification, (2) enumerating neighbor-types, (3) random forest training on a dataset of ...
doi:10.1101/2022.04.15.488517
fatcat:ppramattdzam3nnl7j5vp6orre
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