Expression of the <i>HOXA</i> gene family and its relationship to prognosis and immune infiltrates in cervical cancer

Ge, Fenfen; Tie, Weiwei; Zhang, Junli; Zhu, Yingying; Fan, Yingying

doi:10.1002/jcla.24015

Cited by 7 publications

(2 citation statements)

References 44 publications

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“…On chromosome 7, we found candidate genes belonging to the HOXA-family that exhibit high sweep support with 11-window mean sweep probabilities ranging from 0.80 to 0.95 (Figure S10H). HOXA genes are part of the homeobox cluster, which encode proteins that play an important part in early development of humans by performing embryo segmentation [Shah N, 2010], and it has been suggested that HOXA-family genes are involved in the inception and development of human cancers [Ge et al, 2021]. Specifically, HOXA9 (Figure S10H) is responsible for the pathogenesis of acute myelogenous leukemia, which is a cancer of the bloods and bones [Chen et al, 2019].…”

Section: Resultsmentioning

confidence: 99%

Tensor decomposition based feature extraction and classification to detect natural selection from genomic data

Amin

Hasan

Arnab

et al. 2023

Preprint

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Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy, the ability of organisms to survive at extreme environments, and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are typically grounded in simple theoretical models that may limit the complexity of settings that they can explore, running the risk of inaccurate predictions as the summary statistics are hand engineered. Due to the renaissance in artificial intelligence, machine and deep learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes across sampled individuals to simultaneously extract important genomic features and achieve high classification accuracy and power for distinguishing selection from neutrality. Yet, limitations of such techniques include difficulty in estimating the number of model parameters and identification of features without regard to their location within an image. As a complementary approach, we consider an alternative feature extraction method, termed tensor decomposition, which falls within a class of dimensionality reduction techniques to extract features from multidimensional data while preserving the latent structure of the data. We present a novel approach titledT-REx, in which we apply tensor decomposition to images of haplotypes across sampled individuals, and then use these extracted features as input to classical linear and nonlinear machine learning methods. As a proof of concept, we explore the performance ofT-RExon simulated neutral and selective sweep scenarios, and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to missing data, and easy visualization of underlying low-dimensional features uncovered by tensor decomposition. Therefore,T-RExis a powerful addition to the toolkit for detecting adaptive processes from genomic data.

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Section: Resultsmentioning

confidence: 99%

Tensor decomposition based feature extraction and classification to detect natural selection from genomic data

Amin

Hasan

Arnab

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…S17H , Supplementary Material online). HOXA genes are part of the homeobox cluster, which encode proteins that play an important part in early development of humans by performing embryo segmentation ( Shah and Sukumar 2010 ), and it has been suggested that HOXA-family genes are involved in the inception and development of human cancers ( Ge et al 2021 ). Specifically, HOXA9 (supplementary Fig.…”

Section: Resultsmentioning

confidence: 99%

Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data

Amin,

Hasan,

Arnab

et al. 2023

Molecular Biology and Evolution

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Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under non-convex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data while preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.

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Expression of the HOXA gene family and its relationship to prognosis and immune infiltrates in cervical cancer

Tie

Zhang

et al. 2021

Clinical Laboratory Analysis

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Cancer has been the leading cause of death worldwide. Cervical cancer is the most common gynecological malignancy, the third most common malignant tumor in women worldwide, and the most common malignant tumor among Chinese women. According to a statistical report, a total of 65,620 patients were newly diagnosed with cervical cancer in 2020. 1 The largest preventable cancer contributor in China is chronic infection, which is responsible for approximately 17% of all cancers in China 2 and is predominantly comprised

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Expression of the HOXA gene family and its relationship to prognosis and immune infiltrates in cervical cancer

Cited by 7 publications

References 44 publications

Tensor decomposition based feature extraction and classification to detect natural selection from genomic data

Tensor decomposition based feature extraction and classification to detect natural selection from genomic data

Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data

Expression of the HOXA gene family and its relationship to prognosis and immune infiltrates in cervical cancer

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