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

Hu, Yongli; Hase, Takeshi; Li, Hui Peng; Prabhakar, Shyam; Kitano, Hiroaki; Ng, See Ket; Ghosh, Samik; Wee, Lionel

doi:10.1186/s12864-016-3317-7

Cited by 38 publications

(31 citation statements)

References 50 publications

(61 reference statements)

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“…Xu et al (34) used differentially expressed genes (DEGs) and protein-protein interaction (PPI) network-based neighborhood scoring to select features and trained a SVM model of a 15-gene signature for prediction of colon cancer recurrence and prognosis. Hu et al (35) built an SVM algorithm based on the structural risk minimization principle for the identification of thirty-eight markers involved in brain development from single-cell transcriptomic data. An SVM feature selection based on profiling of urinary RNA metabolites was applied to predict breast cancer (36).…”

Section: Biomarker/signature Discoverymentioning

confidence: 99%

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

2018

CGP

685

347

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Abstract. Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that hasMachine learning (ML) "learns" a model from past data in order to predict future data (1). The key process is the learning which is one of the artificial intelligences. Many different statistical, probabilistic, and optimization techniques can be implemented as the learning methods such as the logistic regression, artificial neural networks (ANN), K-nearest neighbor (KNN), decision trees (DT) and Naive Bayes. There are two main types of ML learning -supervised learning and unsupervised learning. The supervised learning builds a model by learning from known classes (labeled training data). In contrast, unsupervised learning methods learn the common features from unknown class data (unlabeled training data).ML algorithms have been used for key feature training and recognition and for group classification. The strength of ML methods is it could detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to complex genomic data, especially in cancer studies. For example, ANN and DT have been used in cancer detection and diagnosis for nearly 20 years (2-3). The clinical implication of cancer heterogeneity and various cancer genomic data available motivate the applications of ML for cancer classification using genomic data.SVM learning is one of many ML methods. Compared to the other ML methods SVM is very powerful at recognizing subtle patterns in complex datasets (4). SVM can be used to recognize handwriting, recognize fraudulent credit cards, identify a speaker, as well as detect face (5). Cancer is a genetic disease where the genomic feature patterns or feature function patterns may represent the cancer subtypes, the outcome prognosis, drug benefit prediction, tumorigenesis drivers, or a tumor-specific biological process. Therefore, the Artificial Intelligence of SVM can help us in recognizing these patterns in a variety of applications. SVM ModelSVM is a powerful method for building a classifier. It aims to create a decision boundary between two classes that enables the prediction of labels from one or more feature vectors (6). This decision boundary, known as the hyperplane, is orientated in such a way that it is as far as 41

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Section: Biomarker/signature Discoverymentioning

confidence: 99%

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

2018

CGP

685

347

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“…Thereby, we expect that therapies for psychiatric disorders over the next few years must take into consideration of the interactions between multi-omics and neuroimaging datasets as well as gene-environment interactions and epigenetics [68][69][70]. The recent advancements in data-intensive health sciences and single cell sequencing technologies could assuredly trigger new artificial intelligence and machine learning software frameworks, such as deep learning algorithms [71], for population health, public health, and global health in the up-coming decade [72,73]. Furthermore, individual-oriented results will be progressively generated towards the fields of population health, public health, and global health in light of the pressing needs of innovative diagnostics in precision psychiatry and pharmacogenomics for psychiatric disorders [74,75].…”

Section: Discussionmentioning

confidence: 99%

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

Lin

Lane

2020

IJMS

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A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.

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“…Supervised learning has also been applied to single-cell transcriptome data. For example, supervised learning has been applied to detect marker genes in neocortical cells (45). An NN-based approach can also be used to predict cellular state and cell type (46).…”

Section: Initial Successes Of Supervised Machine Learning Applied To mentioning

confidence: 99%

“…We sequentially compress the input data into various bottleneck dimensions (k) from 2 dimensions to 200 dimensions. We use k = 2, 3, 4, 5,6,7,8,9,10,12,14,16,18,20,25,30,35,40,45,50,60,70,80,90,100,125,150, and 200 for a total of 28 different dimensions. For each model, we train five independent times using five different random seed initializations.…”

Section: Evaluating Model Stability and Similarity Within And Across mentioning

confidence: 99%

Discovering Pathway and Cell Type Signatures in Transcriptomic Compendia with Machine Learning

Way

Greene

2019

Annu. Rev. Biomed. Data Sci.

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Pathway and cell type signatures are patterns present in transcriptome data that are associated with biological processes or phenotypic consequences. These signatures result from specific cell type and pathway expression but can require large transcriptomic compendia to detect. Machine learning techniques can be powerful tools for signature discovery through their ability to provide accurate and interpretable results. In this review, we discuss various machine learning applications to extract pathway and cell type signatures from transcriptomic compendia. We focus on the biological motivations and interpretation for both supervised and unsupervised learning approaches in this setting. We consider recent advances, including deep learning, and their applications to expanding bulk and single-cell RNA data. As data and computational resources increase, there will be more opportunities for machine learning to aid in revealing biological signatures.

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

Cited by 38 publications

References 50 publications

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

Discovering Pathway and Cell Type Signatures in Transcriptomic Compendia with Machine Learning

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