A survival model generalized to regression learning algorithms

Guan, Yuanfang; Li, Hongyang; Yi, Daiyao; Zhang, Dongdong; Yin, Changchang; Li, Keyu; Zhang, Ping

doi:10.1038/s43588-021-00083-2

Cited by 7 publications

(8 citation statements)

References 16 publications

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“…We simulated drug termination data of a population following a survival study [ 13 ] (Figure 1b). We generated a population of total n individuals, where the termination rate for each individual is drawn from a population of p = N(p mean , σ ), and we force the minimal termination rate to be zero.…”

Section: Resultsmentioning

confidence: 99%

A Machine Learning Approach to Real‐World Time to Treatment Discontinuation Prediction

Meng

Zhang²,

et al. 2023

Advanced Intelligent Systems

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Real‐world time to treatment discontinuation (rwTTD) is an important endpoint measurement of drug efficacy evaluated using real‐world observational data. rwTTD, represented as a set of metrics calculated from a population‐wise curve, cannot be predicted by existing machine learning approaches. Herein, a methodology that enables predicting rwTTD is developed. First, the robust performance of the model in predicting rwTTD across populations of similar or distinct properties with simulated data using a variety of commonly used base learners in machine learning is demonstrated. Then, the robust performance of the approach both within‐cohort and cross‐disease using real‐world observational data of pembrolizumab for advanced lung cancer and head neck cancer is demonstrated. This study establishes a generic pipeline for real‐world time on treatment prediction, which can be extended to any base machine learners and drugs. Currently, there is no existing machine learning approach established for predicting population‐wise rwTTD, despite that it is an essential metric to report real‐world drug efficacy. Therefore, we believe our study opens a new investigation area of rwTTD prediction, and provides an innovative approach to probe this problem and other problems involving population‐wise predictions. An interactive preprint version of the article can be found at: https://doi.org/10.22541/au.166065465.59798123/v1.

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Section: Resultsmentioning

confidence: 99%

A Machine Learning Approach to Real‐World Time to Treatment Discontinuation Prediction

Meng

Zhang²,

et al. 2023

Advanced Intelligent Systems

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“…Second, scAB adopts the penalty term of phenotype scores, that is the relative survival score in survival analysis. Instead of directly using the survival data of patients like in Scissor, we transformed the survival data to relative survival score of patients, which showed better performance than the Cox model in a previous study ( 12 ). Compared with the loss function of the Cox model, the relative survival score does not require the proportional hazard assumption and importantly includes two additional survival cases (i.e.…”

Section: Discussionmentioning

confidence: 99%

“…In the scenario where the phenotype is survival information, we require the user to input a two-column table including the survival time and survival state of each bulk sample. To develop a unified computational framework, we transform the survival information to a single phenotype score of each patient, which is defined as follow, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$$\begin{equation*}{s_{ij}} = \left\{ \begin{array}{ll} 1 - \displaystyle\frac{{score(sampl{e_i})}}{{\max (score(sampl{e_i}))}}, & \quad i = j \\ 0 , & \quad {\rm{otherwise}} \end{array} \right.. \end{equation*}$$\end{document} where score(sample i ) is the relative survival score of sample i that is computed using a generalized survival model ( 12 ). Compared to the Cox model, the relative survival score includes two additional survival cases, including early-censored–late-uncensored pairs and early-censored–late-censored pairs ( Supplementary Table S1 ).…”

Section: Methodsmentioning

confidence: 99%

scAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing data

Zhang

Jin

Zou

2022

Nucleic Acids Research

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Although single-cell sequencing has provided a powerful tool to deconvolute cellular heterogeneity of diseases like cancer, extrapolating clinical significance or identifying clinically-relevant cells remains challenging. Here, we propose a novel computational method scAB, which integrates single-cell genomics data with clinically annotated bulk sequencing data via a knowledge- and graph-guided matrix factorization model. Once combined, scAB provides a coarse- and fine-grain multiresolution perspective of phenotype-associated cell states and prognostic signatures previously not visible by single-cell genomics. We use scAB to enhance live cancer single-cell RNA-seq data, identifying clinically-relevant previously unrecognized cancer and stromal cell subsets whose signatures show a stronger poor-survival association. The identified fine-grain cell subsets are associated with distinct cancer hallmarks and prognosis power. Furthermore, scAB demonstrates its utility as a biomarker identification tool, with the ability to predict immunotherapy, drug responses and survival when applied to melanoma single-cell RNA-seq datasets and glioma single-cell ATAC-seq datasets. Across multiple single-cell and bulk datasets from different cancer types, we also demonstrate the superior performance of scAB in generating prognosis signatures and survival predictions over existing models. Overall, scAB provides an efficient tool for prioritizing clinically-relevant cell subsets and predictive signatures, utilizing large publicly available databases to improve prognosis and treatments.

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“…Deep learning algorithms have been widely used to address biological and biomedical imaging problems in recent years [1][2][3][4]. Common image-related tasks include detection of cell nuclei [5][6][7], semantic segmentation of tumors [8][9][10][11], and diagnosis of diseases [12][13][14][15][16][17]. In general, standard computer vision tasks are straightforward, such as object detection, segmentation, or classification.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Modeling of Joint Damage in Rheumatoid Arthritis

Li¹,

Guan²

2022

Preprint

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While most deep learning approaches are developed for single images, in real world applications, images are often obtained as a series to inform decision making. Due to hardware (memory) and software (algorithm) limitations, few methods have been developed to integrate multiple images so far. In this study, we present an approach that seamlessly integrates deep learning and traditional machine learning models, to study multiple images and score joint damages in rheumatoid arthritis. This method allows the quantification of joining space narrowing to approach the clinical upper limit. Beyond predictive performance, we integrate the multilevel interconnections across joints and damage types into the machine learning model and reveal the cross-regulation map of joint damages in rheumatoid arthritis. Corresponding author(s) Email: hyangl@umich.edu or gyuanfan@umich.edu

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A survival model generalized to regression learning algorithms

Cited by 7 publications

References 16 publications

A Machine Learning Approach to Real‐World Time to Treatment Discontinuation Prediction

A Machine Learning Approach to Real‐World Time to Treatment Discontinuation Prediction

scAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing data

Multilevel Modeling of Joint Damage in Rheumatoid Arthritis

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