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Articles by Junren Chen
The prognostic impact of CEBPA mutation in acute myeloid leukemia patients undergoing allogeneic hematopoietic stem cell transplantation
Abstract The presence of mutations in basic leucine zipper (bZIP) region of the CEBPA gene in patients with acute myeloid leukemia (AML) is defined as a favorable risk marker according to the guidelines. A total of 173 CEBPA-mutated AML patients receiving allogeneic hematopoietic stem cell transplantation (allo-HSCT) were retrospectively recruited, compared with 345 control patients selected by propensity score matching.
Mathematics > Numerical Analysis
Quantile randomized Kaczmarz (QRK) has proven to be an efficient solver for corrupted linear systems and has received much attention. It was recently shown by Cai et al. (SIAM J. Matrix Anal. Appl.
A rugged life: how host-microbiome adaptations associated with the semi-feral lifestyle of gayal ( Bos frontalis )
Abstract The semi-domesticated gayal (Bos frontalis) is an endangered browsing ruminant inhabiting the rugged Eastern Himalayan foothills, and maintains an energy-intensive lifestyle on nutrient-poor, fiber-rich feed. However, the dietary, microbial, and host physiological features underlying this adaptation remain poorly understood. Here, we analyzed fecal metagenomes from ten bovine populations (n = 334) to characterize dietary composition.
Receptiveness of physicians towards artificial intelligence-driven drug prescription: a nationwide survey
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Abstract Using artificial intelligence (AI) to prescribe drugs has advanced slowly. Whether a “doctor-in-the-loop” design would increase acceptance of drug-prescribing AI is unknown, as are settings where physicians envision AI-driven drug prescription most likely to be implemented. We surveyed a stratified sample of 2708 physicians throughout China to interrogate their opinions on drug-prescribing AI.
By Wei Zhang, Saibing Qi, YU HU, Sichang Liu, Zhenwei Li, Wei Su, Simiao Liu, Wu Liu, Jie Liu, Lanying Zhang, Ruifen Zhang, Weilin Liu, Qi Li, Ting Li, Chao Liu, Da Gao, Heng He, Fei Long, Xiaoyan Ge, Xiao-xiao Sun, Mengyun Chen, Yahui Feng, Zhen Song, Jinyu Wang, Robert Peter Gale, Xiaowen Gong, Qiujin Shen, Junren Chen, Xiaolin Zhai, Wangsong Zhai, Jiejing Yin, Mengyi Xie, Lianzhong Wu, Yongsheng Meng, Baima Yangjin, Haizhao Ma, Tiansheng Su, Shuhao Du
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Nature
Verified
Using Artificial Intelligence to Prescribe Cancer Drugs and Perform Other Tasks
Junren Chen, MD, PhD, MBA ROBERT PETER GALE, MD, PhD, DSc (hc), FACP, FRCP, FRCPI (hon), FRSM In a recent article in The ASCO Post, we discussed increasing use of artificial intelligence (AI) in oncology and how physician-complementing AI can empower oncologists to be even better at what they do.1The reason AI is needed is that increasingly many variables need to be considered in cancer therapy decision-making,a challenge known as the curse of dimensionality.
Computer Science > Information Theory
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Physician-complementing artificial intelligence in haematology: ushering in a new era - Leukemia
Use of artificial intelligence (AI) in haematology is advancing rapidly. Initially, AI was used for pattern recognition, like analysing blood and bone marrow slide images and radiographs. Now, use has expanded to analysing multi-dimensional clinical data to assist in diagnosis, estimating prognosis, guiding therapy decision-making, and prescribing drugs [1,2,3]. There is considerable debate whether AI should be physician-substituting or physician-complementing [4, 5].
Physician-Complementing Artificial Intelligence in Oncology
Junren Chen, MD, PhD, MBA Robert Peter Gale, MD, PhD, DSc (hc), FACP, FRCP, FRCPI (hon), FRSM Use of artificial intelligence (AI) in oncology is advancing rapidly. AI was first used for reading radiology images and analyzing pathology slides. More recently, use of AI has expanded to analyzing large clinical data sets (big data). The next envisioned role for AI in oncology encompasses many spheres of physician activities such as choosing the best therapy and/or prescribing drugs.
JMIR Research Protocols - Correction: Factors Affecting the Receptiveness of Chinese Internists and Surgeons Toward Artificial Intelligence-Driven Drug Prescription: Protocol for a Systematic Survey Study
JMIR Res Protoc 2025;14:e83537 doi:10.2196/83537 In “Factors Affecting the Receptiveness of Chinese Internists and Surgeons Toward Artificial Intelligence–Driven Drug Prescription: Protocol for a Systematic Survey Study” (JMIR Res Protoc 2025;14:e76009) the authors noted one error. In , under both forced-choice questions, the first option: ≤1 to 3 Has been revised to: ≤1 And the following option has been added underneath the first option: 1.1 to 3 So that now reads as follows: Textbox 1.
JMIR Research Protocols - Factors Affecting the Receptiveness of Chinese Internists and Surgeons Toward Artificial Intelligence-Driven Drug Prescription: Protocol for a Systematic Survey Study
Background Objectives Ethical Considerations Questionnaire Design Dimensions and covariates Questions Personal attributes Demographics Q1. Birth year Q2. Sex Q3. Highest educational degree Q4. Clinical practicing experience Q5. Current rank in the professional ladder Q6. Clinical specialty Knowledge of AIb Q7. I am knowledgeable in AI technology in general. Q8. I am knowledgeable in medical AI technology. Q9. I am experienced with using AI in work and daily life. Q10.
[2507.15185] On Subsample Size of Quantile-Based Randomized Kaczmarz
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
A neuromorphic processor with on-chip learning for beyond-CMOS device integration - Nature Communications
Abstract Recent advances in memory technologies, devices, and materials have shown great potential for integration into neuromorphic electronic systems. However, a significant gap remains between the development of these materials and the realization of large-scale, fully functional systems. One key challenge is determining which devices and materials are best suited for specific functions and how they can be paired with complementary metal-oxide-semiconductor circuitry.
Measurable residual disease (MRD)-testing in haematological and solid cancers - Leukemia
“When you can measure what you are speaking about and express it in numbers, you know something about it.” Lord Kelvin There is considerable interest in and enthusiasm for quantitative tests for residual cancer cells in the context of cancer therapy, a concept referred to as measurable residual disease (MRD)-testing. However, an updated critical evaluation of using MRD-tests to predict cancer recurrence and to direct subsequent cancer therapy(ies) is needed.
Response to Pfirrmann et al.’s comment on How should we interpret conclusions of TKI-stopping studies - Leukemia
To the Editor: We thank Prof. Pfirrmann and colleagues for their Correspondence [1]. Our reply: Enthusiasm does not mitigate the potential problem of survivorship bias. The likelihood of therapy-free remission (TFR) reflects the biology of someone’s leukaemia and efficacy of tyrosine kinase inhibitor (TKI)-therapy. For every person A eligible to enroll in the EURO-SKI trial there was a person B who might never achieve deep molecular response (DMR) regardless of the duration of TKI-therapy [1, 2].
Response to comment on Have we been qualifying measurable residual disease correctly? - Leukemia
To the Editor: Professor Morley was correct in pointing out in our Perspective we focused on analysis of Poisson noise, a type of sampling error [1]. Indeed, it is sometimes useful to distinguish between errors in sampling cells from a sub-population and errors in sampling a sub-population from the entire population of cells. The underlying mechanisms for these two types of sampling errors could be similar.
Junren Chen's research works
What is this page? This page lists the scientific contributions of an author, who either does not have a ResearchGate profile, or has not yet added these contributions to their profile. It was automatically created by ResearchGate to create a record of this author's body of work. We create such pages to advance our goal of creating and maintaining the most comprehensive scientific repository possible.
Siqi Qin's research works
What is this page? This page lists the scientific contributions of an author, who either does not have a ResearchGate profile, or has not yet added these contributions to their profile. It was automatically created by ResearchGate to create a record of this author's body of work. We create such pages to advance our goal of creating and maintaining the most comprehensive scientific repository possible.
Could Cyclosiversioside F Serve as a Dietary Supplement to Prevent Obesity and Relevant Disorders?
1. Introduction Obesity is a metabolic syndrome caused by a complex interaction between environmental and genetic factors, which mainly include, but are not limited to, imbalanced diet, endocrine dyscrasia, genetic and epigenetic regulation, sociodemographic influences, as well as intrauterine condition [1,2]. The incidence of obesity is increasing rapidly worldwide, and without appropriate management, the global prevalence will hit 18% in men and above 20% in women by 2025 [3].
Correction: Are haematopoietic stem cell transplants stem cell transplants, is there a threshold dose of CD34-positive cells and how many are needed for rapid posttransplant granulocyte recovery? | Leukemia
Correction to: Leukemia https://doi.org/10.1038/s41375-023-01973-2, published online 20 July 2023 In the original published online version, “<1.08 × 10E+7” in row #5 of Table 1 (ref. 41) was misquoted as “<1.82 × 10E+7”. Table 1 should have appeared as shown below. The original article has been corrected.
How should we interpret conclusions of TKI-stopping studies - Leukemia
TKI-stopping studies are like the New Yorker magazine; Highly admired but rarely read in detail For most physicians and people with CML the best outcome of tyrosine kinase-inhibitor (TKI)-therapy outcome is achieving therapy-free remission (TFR).Footnote 1 The metric for achieving TFR is complex and needs to consider co-variates such as depth and duration of response and other co-variates known, unknown but knowable (latent) or perhaps unknowable. Chance may also be important.
Are haematopoietic stem cell transplants stem cell transplants, is there a threshold dose of CD34-positive cells and how many are needed for rapid posttransplant granulocyte recovery? | Leukemia
Introduction It is widely-believed recovery of bone marrow function after a haematopoietic cell transplant arises from pluripotent haematopoietic stem cells (HSCs; hence haematopoietic stem cell transplant) [1], that numbers of CD34-positive cells in a graft are an accurate proxy for numbers of HSCs [2, 3], that dose of CD34-positive cells should be quantified by body weight and that there is a threshold dose of CD34-positive cells required for successful posttransplant bone marrow recovery...
Aconitum carmichaelii Debx. Attenuates Heart Failure through Inhibiting Inflammation and Abnormal Vascular Remodeling
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