Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Anjun Ma
Understanding clinical reasoning variability in medical large language models: a mechanistic interpretability study
Abstract Medical large language models achieving high benchmark accuracy exhibit unexplained variability in clinical tasks, producing errors that clinicians cannot safeguard against. We evaluated clinical reasoning stability in GPT-5, MedGemma-27B-Text-IT, and OpenBioLLM-Llama3-70B using 3035 systematic perturbations from physician-validated oncology SOAP, brief vignette, and MIMIC cases.
Proteotoxic stress response drives T cell exhaustion and immune evasion - Nature
Abstract Chronic infections and cancer cause T cell dysfunction known as exhaustion. This cell state is caused by persistent antigen exposure, suboptimal co-stimulation and a plethora of hostile factors that dampen protective immunity and limit the efficacy of immunotherapies1,2,3,4. The mechanisms that underlie T cell exhaustion remain poorly understood.
Advancing biological understanding of cellular senescence with computational multiomics - Nature Genetics
|
Abstract Cellular senescence is a complex biological process that plays a pathophysiological role in aging and age-related diseases. The biological understanding of senescence at the cellular and tissue levels remains incomplete due to the lack of specific biomarkers as well as the relative rarity of senescent cells, their phenotypic heterogeneity and dynamic features.
By Michael J. Becich, Elana Fertig, Liangcai Gu, Qianjiang Hu, Zhicheng Ji, Nathan K. LeBrasseur, Anjun Ma, Qin Ma, Vilas Menon, Jacob Mitchell, Laura J. Niedernhofer, Mauricio Rojas, Siyuan Wang, Pei-Hsun Wu, Ming Xu, Miao Yu, Xu Zhang, Peter Adams, Darren Baker, Christopher Benz, Bennett G. Childs, Jeffrey H. Chuang, Dongjun Chung, Mingze Dong, William F. Flynn, David Furman, Stephanie Halene, Ann V Hertzel, Kanako Iwasaki, James Kirkland, Ron Korstanje, Yang Liu Verified, Helene Martini, Simon Melov, João F. Passos, Alexandra Rindone, Paul D. Robbins, Paul Robson, Jhonny Rodriguez-Lopez, Nadia A. Rosenthal, Marissa J. Schafer, Birgit Schilling, Elizabeth Schmidt, Tamar Tchkonia, Marcos G. Teneche, Natalia Vanegas, Rong Fan
|
Nature
Verified
The new microbiome on the block: challenges and opportunities of using human tumor sequencing data to study microbes - Nature Methods
Abstract Microbes within tumors have been recognized and experimentally related to oncogenesis, tumor growth, metastasis and therapeutic responsiveness. Studying the tumor microbiome presents difficulties, as early indications suggest that microbe populations are low in abundance, sparse and highly heterogeneous. Disparate results from computational profiling of the tumor microbiome have cast doubt on the premise of microbes in tumors.
TrimNN: characterizing cellular community motifs for studying multicellular topological organization in complex tissues - Nature Communications
Abstract The spatial organization of cells plays a pivotal role in shaping tissue functions and phenotypes in various biological systems and diseased microenvironments. However, the topological principles governing interactions among cell types within spatial patterns remain poorly understood.
CacPred: a cascaded convolutional neural network for TF-DNA binding prediction - BMC Genomics
The experimental data includes 790 ChIP-seq datasets covering 261 TFs and seven ChIP-nexus datasets (Table. S1) covering seven TFs in this paper [30]. The 790 ChIP-seq datasets contain 690 ENCODE ChIP-seq datasets covering 161 TFs and 100 ChIP-seq datasets of Cistrome database covering 100 TFs [31]. All sequences in each sub-dataset are fixed with 1,001bps around their centers, and ranked in the decreasing order of original signal scores, which are all positive samples with label ‘1’.
Graph Fourier transform for spatial omics representation and analyses of complex organs - Nature Communications
Abstract Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics profiling platforms to generate their interpretable representations.
Graph Fourier transform for spatial omics representation and analyses of complex organs
Culture 11 hours ago Found an arrowhead in Kansas? Archaeologist urges you to leave it alone ksn.com - by: Matthew Self • 11h TOPEKA, Kan.
A single-cell and spatial RNA-seq database for Alzheimer’s disease (ssREAD) - Nature Communications
Abstract Alzheimer’s Disease (AD) pathology has been increasingly explored through single-cell and single-nucleus RNA-sequencing (scRNA-seq & snRNA-seq) and spatial transcriptomics (ST). However, the surge in data demands a comprehensive, user-friendly repository. Addressing this, we introduce a single-cell and spatial RNA-seq database for Alzheimer’s disease (ssREAD). It offers a broader spectrum of AD-related datasets, an optimized analytical pipeline, and improved usability.
MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer - Nature Communications
Abstract Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets. However, their computational identification and analysis often lag behind major cell types. To fill this gap, we introduce MarsGT: Multi-omics Analysis for Rare population inference using a Single-cell Graph Transformer. It identifies rare cell populations using a probability-based heterogeneous graph transformer on single-cell multi-omics data.
ssREAD: A Single-cell and Spatial RNA-seq Database for Alzheimer's Disease
Abstract Alzheimer's Disease (AD) is a neurodegenerative malady predominantly affecting the elderly and exhibits its debilitating effects on a dementia-prone population.
Identification of HPV oncogene and host cell differentiation associated cellular heterogeneity in cervical cancer via single‐cell transcriptomic analysis
1 BACKGROUND Human Papillomavirus (HPV) infection causes the most common sexually transmitted diseases and up to 5% of human cancer, including anogenital cancer, and head and neck cancer.1-3 Specifically, persistent HPV infection in the host epithelium is one of the critical steps of cervical cancer development.4, 5 Current prophylactic HPV vaccines have enabled the prevention of initial infection and HPV-associated malignancies.
Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data
Abstract Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks is crucial in understanding gene regulatory programs in a complex biological system.
MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer
Abstract Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets.However, their computational identification and analysis often lag behind major cell types.
Identification of HPV oncogene and host cell differentiation associated cellular heterogeneity in cervical cancer via single-cell transcriptomic analysis
Abstract Human Papillomaviruses (HPVs) are associated with around 5-10% of human cancer, notably nearly 99% of cervical cancer. The mechanisms HPV interacts with stratified epithelium (differentiated layers) during the viral life cycle, and oncogenesis remain unclear. In this study, we used single-cell transcriptome analysis to study viral gene and host cell differentiation-associated heterogeneity of HPV-positive cervical cancer tissue.
A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset
Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website.
An explainable graph neural framework to identify cancer-associated intratumoral microbial communities
Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website.
A Weighted Two-stage Sequence Alignment Framework to Identify DNA Motifs from ChIP-exo Data
Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website.
Spatial omics representation and functional tissue module inference using graph Fourier transform
Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website.
SpaGFT is a graph Fourier transform for tissue module identification from spatially resolved transcriptomics
Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website.
Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data - Nature Communications
Datasets The GDSC database [https://www.cancerrxgene.org/] is publicly available online. Drug response annotation, including half maximal inhibitory concentration (IC50) and area under the dose-response curve (AUC) are available online [https://www.cancerrxgene.org/downloads/bulk_download]. Gene expression data (RMA-normalized basal expression profiles) for cell lines can be accessed on GDSC [https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources/Home.html].
Inference of disease-associated microbial gene modules based on metagenomic and metatranscriptomic data
Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website.
The use of single-cell multi-omics in immuno-oncology - Nature Communications
Single-cell multi-omics (scMulti-omics) has brought transformative insights into immuno-oncology, demonstrating success in describing novel immune subsets and defining important regulators of antitumor immunity. Here, we give examples of how scMulti-omics has been used in specific tumor studies and discuss how this may develop in the future. The mixture of cell subpopulations in tumors is considered one of the important characteristics for drug resistance, metastasis, and disease relapse.
Author Correction: scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses - Nature Communications
Correction to: Nature Communications https://doi.org/10.1038/s41467-021-22197-x, published online 25 March 2021. In Figure 2, panels (a) and (b) were inadvertently swapped. The correct version of this figure appears below. This has been corrected in the HTML and PDF version of this article. Author information Author notes These authors contributed equally: Juexin Wang, Anjun Ma. Affiliations Department of Electrical Engineering and Computer Science, and Christopher S.
Inference of disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data
Abstract The identification of disease-associated microbial biomarkers is crucial for disease diagnosis and therapy. However, the heterogeneity, high dimensionality, and large amounts of microbial data present tremendous challenges for the discovery of biomarkers. In this paper, we present IDAM, a novel method for disease-associated biomarker inference from metagenomic and metatranscriptomic data, which does not require prior metadata.
DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer
Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website.
Biological network inference from single-cell multi-omics data using heterogeneous graph transformer
Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website.
Single-Cell Techniques and Deep Learning in Predicting Drug Response
Highlights A comprehensive understanding of heterogeneous tumor subpopulations will benefit drug sensitivity prediction and combination drug treatment design. Deep learning models are powerful and extensively used in drug sensitivity prediction and in inferring drug–target interactions. Single-cell sequencing techniques offer precise and accurate profiling of tumor subpopulations and reveal subtle differences in their response to drug treatments.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Anjun
Contact Anjun, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack