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A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data

Yongli Hu, Takeshi Hase, Hui Peng Li, Shyam Prabhakar, Hiroaki Kitano, See Kiong Ng, Samik Ghosh, Lawrence Jin Kiat Wee
2016 BMC Genomics  
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

Pelin Gundogdu, Carlos Loucera, Inmaculada Alamo-Alvarez, Joaquin Dopazo, Isabel Nepomuceno
2022 BioData Mining  
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]

Fang Chen, Xinghuai Sun, Dongsheng Chen, Jihong Wu, Xiangning Ding, Fangyuan Hu, Zaoxu Xu, Shenghai Zhang, Langchao Liang, Chaochao Chai, Jixing Zhong, Shiyou Wang (+17 others)
2019 bioRxiv   pre-print
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

Byung-Chul Kim, Jingyu Kim, Ilhan Lim, Dong Ho Kim, Sang Moo Lim, Sang-Keun Woo
2021 Applied Sciences  
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]

Guangsheng Pei, Brisa S Fernandes, Yin-Ying Wang, Astrid Manuel, Peilin Jia, Zhongming Zhao
2022 bioRxiv   pre-print
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]

Almas Jabeen, Nadeem Ahmad, Khalid Raza
2017 bioRxiv   pre-print
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

Yulan Liang, Arpad Kelemen
2017 BioData Mining  
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

Jun-yi Wu, Jun Qin, Lei Li, Kun-dong Zhang, Yi-sheng Chen, Yang Li, Tao Jin, Jun-ming Xu, Wen-Jun Tu
2021 Oxidative Medicine and Cellular Longevity  
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

Christian H. Holland, Jovan Tanevski, Javier Perales-Patón, Jan Gleixner, Manu P. Kumar, Elisabetta Mereu, Brian A. Joughin, Oliver Stegle, Douglas A. Lauffenburger, Holger Heyn, Bence Szalai, Julio Saez-Rodriguez
2020 Genome Biology  
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]

Almas Jabeen, Nadeem Ahmad, Khalid Raza
2017 Lecture Notes in Computational Vision and Biomechanics  
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

Xinnan Dai, Fan Xu, Shike Wang, Piyushkumar A. Mundra, Jie Zheng
2021 BMC Bioinformatics  
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

Ruizheng Tian, Yixiao Huang, Balachandar Balakrishnan, Maohua Chen
2020 Insects  
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]

Alexander Lachmann, Kaeli Rizzo, Alon Bartal, Minji Jeon, Daniel J.B. Clarke, Avi Ma'ayan
2021 bioRxiv   pre-print
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

Dominic Grün
2018 Current Opinion in Systems Biology  
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]

Bassel Ghaddar, Subhajyoti De
2022 bioRxiv   pre-print
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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