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Article ; Online: Sorafenib induces cardiotoxicity through RBM20-mediated alternative splicing of sarcomeric and mitochondrial genes.

Liu, Songming / Yue, Shanshan / Guo, Yuxuan / Han, Jing-Yan / Wang, Huan

2023 Volume 198, Page(s) 107017

Abstract: Sorafenib, a multi-targeted tyrosine kinase inhibitor, is a first-line treatment for advanced solid tumors, but it induces many adverse cardiovascular events, including myocardial infarction and heart failure. These cardiac defects can be mediated by ... ...

Abstract	Sorafenib, a multi-targeted tyrosine kinase inhibitor, is a first-line treatment for advanced solid tumors, but it induces many adverse cardiovascular events, including myocardial infarction and heart failure. These cardiac defects can be mediated by alternative splicing of genes critical for heart function. Whether alternative splicing plays a role in sorafenib-induced cardiotoxicity remains unclear. Transcriptome of rat hearts or human cardiomyocytes treated with sorafenib was analyzed and validated to define alternatively spliced genes and their impact on cardiotoxicity. In rats, sorafenib caused severe cardiotoxicity with decreased left ventricular systolic pressure, elongated sarcomere, enlarged mitochondria and decreased ATP. This was associated with alternative splicing of hundreds of genes in the hearts, many of which were targets of a cardiac specific splicing factor, RBM20. Sorafenib inhibited RBM20 expression in both rat hearts and human cardiomyocytes. The splicing of RBM20's targets, SLC25A3 and FHOD3, was altered into fetal isoforms with decreased function. Upregulation of RBM20 during sorafenib treatment reversed the pathogenic splicing of SLC25A3 and FHOD3, and enhanced the phosphate transport into mitochondria by SLC25A3, ATP synthesis and cell survival.We envision this regulation may happen in many drug-induced cardiotoxicity, and represent a potential druggable pathway for mitigating sorafenib-induced cardiotoxicity.
MeSH term(s)	Rats ; Animals ; Humans ; Alternative Splicing ; Sorafenib ; Cardiotoxicity/genetics ; Cardiotoxicity/metabolism ; Sarcomeres/metabolism ; Genes, Mitochondrial ; RNA-Binding Proteins/genetics ; RNA-Binding Proteins/metabolism ; Myocytes, Cardiac/metabolism ; Adenosine Triphosphate/metabolism ; Formins/genetics ; Formins/metabolism
Chemical Substances	Sorafenib (9ZOQ3TZI87) ; RNA-Binding Proteins ; Adenosine Triphosphate (8L70Q75FXE) ; FHOD3 protein, human ; Formins ; RBM20 protein, rat
Language	English
Publishing date	2023-11-23
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1003347-6
ISSN	1096-1186 ; 0031-6989 ; 1043-6618
ISSN (online)	1096-1186
ISSN	0031-6989 ; 1043-6618
DOI	10.1016/j.phrs.2023.107017
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Article: A Comprehensive Survey of Genomic Mutations in Breast Cancer Reveals Recurrent Neoantigens as Potential Therapeutic Targets.

Zhou, Si / Liu, Songming / Zhao, Lijian / Sun, Hai-Xi

Frontiers in oncology

2022 Volume 12, Page(s) 786438

Abstract: Neoantigens are mutated antigens specifically generated by cancer cells but absent in normal cells. With high specificity and immunogenicity, neoantigens are considered as an ideal target for immunotherapy. This study was aimed to investigate the ... ...

Abstract	Neoantigens are mutated antigens specifically generated by cancer cells but absent in normal cells. With high specificity and immunogenicity, neoantigens are considered as an ideal target for immunotherapy. This study was aimed to investigate the signature of neoantigens in breast cancer. Somatic mutations, including SNVs and indels, were obtained from cBioPortal of 5991 breast cancer patients. 738 non-silent somatic variants present in at least 3 patients for neoantigen prediction were selected.
Language	English
Publishing date	2022-03-21
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2022.786438
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Book ; Online: Preconditioning for Physics-Informed Neural Networks

Liu, Songming / Su, Chang / Yao, Jiachen / Hao, Zhongkai / Su, Hang / Wu, Youjia / Zhu, Jun

2024

Abstract: Physics-informed neural networks (PINNs) have shown promise in solving various partial differential equations (PDEs). However, training pathologies have negatively affected the convergence and prediction accuracy of PINNs, which further limits their ... ...

Abstract	Physics-informed neural networks (PINNs) have shown promise in solving various partial differential equations (PDEs). However, training pathologies have negatively affected the convergence and prediction accuracy of PINNs, which further limits their practical applications. In this paper, we propose to use condition number as a metric to diagnose and mitigate the pathologies in PINNs. Inspired by classical numerical analysis, where the condition number measures sensitivity and stability, we highlight its pivotal role in the training dynamics of PINNs. We prove theorems to reveal how condition number is related to both the error control and convergence of PINNs. Subsequently, we present an algorithm that leverages preconditioning to improve the condition number. Evaluations of 18 PDE problems showcase the superior performance of our method. Significantly, in 7 of these problems, our method reduces errors by an order of magnitude. These empirical findings verify the critical role of the condition number in PINNs' training.
Keywords	Computer Science - Machine Learning ; Mathematics - Numerical Analysis
Subject code	518
Publishing date	2024-02-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Recurrent Neoantigens in Colorectal Cancer as Potential Immunotherapy Targets.

Chen, Chao / Liu, Songming / Qu, Ruokai / Li, Bo

BioMed research international

2020 Volume 2020, Page(s) 2861240

Abstract: This study was aimed at investigating the mutations in colorectal cancer (CRC) for recurrent neoantigen identification. A total of 1779 samples with whole exome sequencing (WES) data were obtained from 7 published CRC cohorts. Common HLA genotypes were ... ...

Abstract	This study was aimed at investigating the mutations in colorectal cancer (CRC) for recurrent neoantigen identification. A total of 1779 samples with whole exome sequencing (WES) data were obtained from 7 published CRC cohorts. Common HLA genotypes were used to predict the probability of neoantigens at high-frequency mutants in the dataset. Based on the WES data, we not only obtained the most comprehensive CRC mutation landscape so far but also found 1550 mutations which could be identified in at least 5 patients, including
MeSH term(s)	Amino Acid Sequence ; Antigens, Neoplasm/immunology ; Cohort Studies ; Colorectal Neoplasms/genetics ; Colorectal Neoplasms/immunology ; Colorectal Neoplasms/therapy ; Female ; Humans ; INDEL Mutation/genetics ; Immunotherapy ; Male ; Middle Aged ; Mutation/genetics ; Mutation Rate ; Peptides/chemistry ; Polymorphism, Single Nucleotide/genetics
Chemical Substances	Antigens, Neoplasm ; Peptides
Language	English
Publishing date	2020-07-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	2698540-8
ISSN	2314-6141 ; 2314-6133
ISSN (online)	2314-6141
ISSN	2314-6133
DOI	10.1155/2020/2861240
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Book ; Online: Task Aware Dreamer for Task Generalization in Reinforcement Learning

Ying, Chengyang / Hao, Zhongkai / Zhou, Xinning / Su, Hang / Liu, Songming / Yan, Dong / Zhu, Jun

2023

Abstract: A long-standing goal of reinforcement learning is to acquire agents that can learn on training tasks and generalize well on unseen tasks that may share a similar dynamic but with different reward functions. A general challenge is to quantitatively ... ...

Abstract	A long-standing goal of reinforcement learning is to acquire agents that can learn on training tasks and generalize well on unseen tasks that may share a similar dynamic but with different reward functions. A general challenge is to quantitatively measure the similarities between these different tasks, which is vital for analyzing the task distribution and further designing algorithms with stronger generalization. To address this, we present a novel metric named Task Distribution Relevance (TDR) via optimal Q functions of different tasks to capture the relevance of the task distribution quantitatively. In the case of tasks with a high TDR, i.e., the tasks differ significantly, we show that the Markovian policies cannot differentiate them, leading to poor performance. Based on this insight, we encode all historical information into policies for distinguishing different tasks and propose Task Aware Dreamer (TAD), which extends world models into our reward-informed world models to capture invariant latent features over different tasks. In TAD, we calculate the corresponding variational lower bound of the data log-likelihood, including a novel term to distinguish different tasks via states, to optimize reward-informed world models. Extensive experiments in both image-based control tasks and state-based control tasks demonstrate that TAD can significantly improve the performance of handling different tasks simultaneously, especially for those with high TDR, and demonstrate a strong generalization ability to unseen tasks.
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2023-03-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: MultiAdam

Yao, Jiachen / Su, Chang / Hao, Zhongkai / Liu, Songming / Su, Hang / Zhu, Jun

Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks

2023

Abstract: Physics-informed Neural Networks (PINNs) have recently achieved remarkable progress in solving Partial Differential Equations (PDEs) in various fields by minimizing a weighted sum of PDE loss and boundary loss. However, there are several critical ... ...

Abstract	Physics-informed Neural Networks (PINNs) have recently achieved remarkable progress in solving Partial Differential Equations (PDEs) in various fields by minimizing a weighted sum of PDE loss and boundary loss. However, there are several critical challenges in the training of PINNs, including the lack of theoretical frameworks and the imbalance between PDE loss and boundary loss. In this paper, we present an analysis of second-order non-homogeneous PDEs, which are classified into three categories and applicable to various common problems. We also characterize the connections between the training loss and actual error, guaranteeing convergence under mild conditions. The theoretical analysis inspires us to further propose MultiAdam, a scale-invariant optimizer that leverages gradient momentum to parameter-wisely balance the loss terms. Extensive experiment results on multiple problems from different physical domains demonstrate that our MultiAdam solver can improve the predictive accuracy by 1-2 orders of magnitude compared with strong baselines.
Keywords	Computer Science - Machine Learning ; Mathematics - Numerical Analysis
Subject code	006
Publishing date	2023-06-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: NUNO

Liu, Songming / Hao, Zhongkai / Ying, Chengyang / Su, Hang / Cheng, Ze / Zhu, Jun

A General Framework for Learning Parametric PDEs with Non-Uniform Data

2023

Abstract: The neural operator has emerged as a powerful tool in learning mappings between function spaces in PDEs. However, when faced with real-world physical data, which are often highly non-uniformly distributed, it is challenging to use mesh-based techniques ... ...

Abstract	The neural operator has emerged as a powerful tool in learning mappings between function spaces in PDEs. However, when faced with real-world physical data, which are often highly non-uniformly distributed, it is challenging to use mesh-based techniques such as the FFT. To address this, we introduce the Non-Uniform Neural Operator (NUNO), a comprehensive framework designed for efficient operator learning with non-uniform data. Leveraging a K-D tree-based domain decomposition, we transform non-uniform data into uniform grids while effectively controlling interpolation error, thereby paralleling the speed and accuracy of learning from non-uniform data. We conduct extensive experiments on 2D elasticity, (2+1)D channel flow, and a 3D multi-physics heatsink, which, to our knowledge, marks a novel exploration into 3D PDE problems with complex geometries. Our framework has reduced error rates by up to 60% and enhanced training speeds by 2x to 30x. The code is now available at https://github.com/thu-ml/NUNO.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2023-05-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Identification of shared neoantigens in esophageal carcinoma by the combination of comprehensive analysis of genomic data and in silico neoantigen prediction.

Yuan, Yuan / Chen, Chao / Liu, Songming / Xiong, Heng / Huang, Ying / Zhang, Xi / Zhang, Xiuqing / Li, Bo

Cellular immunology

2022 Volume 377, Page(s) 104537

Abstract: Neoantigens are attractive targets for cancer immunotherapy. The identification of neoantigens shared by different patients could promote the broad application of neoantigen-based immunotherapy. This study aimed to investigate shared neoantigens in ... ...

Abstract	Neoantigens are attractive targets for cancer immunotherapy. The identification of neoantigens shared by different patients could promote the broad application of neoantigen-based immunotherapy. This study aimed to investigate shared neoantigens in esophageal carcinoma. By combining a comprehensive analysis of mutation data of 722 patients with esophageal carcinoma (EC) and in silico neoantigen prediction, we obtained 216 recurrent neoantigen candidates predicted to bind to high-frequency class I human leukocyte antigen (HLA) alleles. We further performed immunogenicity validation tests on five high-frequency HLA-A*0201 binding neoantigens derived from TP53 mutations. The results demonstrated that the peptides p53 H193R
MeSH term(s)	Antigens, Neoplasm/genetics ; CD8-Positive T-Lymphocytes ; Carcinoma/metabolism ; Genomics ; Humans ; Immunotherapy/methods ; Mutation/genetics ; Neoplasms ; Peptides
Chemical Substances	Antigens, Neoplasm ; Peptides
Language	English
Publishing date	2022-05-14
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	80094-6
ISSN	1090-2163 ; 0008-8749
ISSN (online)	1090-2163
ISSN	0008-8749
DOI	10.1016/j.cellimm.2022.104537
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Book ; Online: Physics-Informed Machine Learning

Hao, Zhongkai / Liu, Songming / Zhang, Yichi / Ying, Chengyang / Feng, Yao / Su, Hang / Zhu, Jun

A Survey on Problems, Methods and Applications

2022

Abstract: Recent advances of data-driven machine learning have revolutionized fields like computer vision, reinforcement learning, and many scientific and engineering domains. In many real-world and scientific problems, systems that generate data are governed by ... ...

Abstract	Recent advances of data-driven machine learning have revolutionized fields like computer vision, reinforcement learning, and many scientific and engineering domains. In many real-world and scientific problems, systems that generate data are governed by physical laws. Recent work shows that it provides potential benefits for machine learning models by incorporating the physical prior and collected data, which makes the intersection of machine learning and physics become a prevailing paradigm. By integrating the data and mathematical physics models seamlessly, it can guide the machine learning model towards solutions that are physically plausible, improving accuracy and efficiency even in uncertain and high-dimensional contexts. In this survey, we present this learning paradigm called Physics-Informed Machine Learning (PIML) which is to build a model that leverages empirical data and available physical prior knowledge to improve performance on a set of tasks that involve a physical mechanism. We systematically review the recent development of physics-informed machine learning from three perspectives of machine learning tasks, representation of physical prior, and methods for incorporating physical prior. We also propose several important open research problems based on the current trends in the field. We argue that encoding different forms of physical prior into model architectures, optimizers, inference algorithms, and significant domain-specific applications like inverse engineering design and robotic control is far from being fully explored in the field of physics-informed machine learning. We believe that the interdisciplinary research of physics-informed machine learning will significantly propel research progress, foster the creation of more effective machine learning models, and also offer invaluable assistance in addressing long-standing problems in related disciplines.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Mathematics - Numerical Analysis
Subject code	006
Publishing date	2022-11-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs

Liu, Songming / Hao, Zhongkai / Ying, Chengyang / Su, Hang / Zhu, Jun / Cheng, Ze

2022

Abstract: We present a unified hard-constraint framework for solving geometrically complex PDEs with neural networks, where the most commonly used Dirichlet, Neumann, and Robin boundary conditions (BCs) are considered. Specifically, we first introduce the "extra ... ...

Abstract	We present a unified hard-constraint framework for solving geometrically complex PDEs with neural networks, where the most commonly used Dirichlet, Neumann, and Robin boundary conditions (BCs) are considered. Specifically, we first introduce the "extra fields" from the mixed finite element method to reformulate the PDEs so as to equivalently transform the three types of BCs into linear equations. Based on the reformulation, we derive the general solutions of the BCs analytically, which are employed to construct an ansatz that automatically satisfies the BCs. With such a framework, we can train the neural networks without adding extra loss terms and thus efficiently handle geometrically complex PDEs, alleviating the unbalanced competition between the loss terms corresponding to the BCs and PDEs. We theoretically demonstrate that the "extra fields" can stabilize the training process. Experimental results on real-world geometrically complex PDEs showcase the effectiveness of our method compared with state-of-the-art baselines. Comment: NeurIPS 2022
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2022-10-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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