Article
Oncology
Leonie W. Wahjudi, Stephan Bernhardt, Khalid Abnaof, Peter Horak, Simon Kreutzfeldt, Christoph Heining, Simone Borgoni, Corinna Becki, Daniela Berg, Daniela Richter, Barbara Hutter, Sebastian Uhrig, Katrin Pfuetze, Jonas Leichsenring, Hanno Glimm, Benedikt Brors, Christof von Kalle, Albrecht Stenzinger, Ulrike Korf, Stefan Froehling, Stefan Wiemann
Summary: The study demonstrates that integrated analysis of DNA, RNA, and protein data can optimize the therapeutic stratification of individual patients, potentially increasing the success rate of precision cancer therapy. Prospective validation studies are necessary to further incorporate proteomic analysis into precision oncology.
INTERNATIONAL JOURNAL OF CANCER
(2021)
Article
Rheumatology
Ashley Elliott, Dennis McGonagle, Madeleine Rooney
Summary: Treatment options for PsA have expanded significantly in the past decade, but there are challenges in selecting and switching biologic therapies. Enthesitis plays a crucial role in the pathogenesis of PsA and may contribute to its various clinical presentations.
Article
Multidisciplinary Sciences
Yueyi Li, Peixin Du, Hao Zeng, Yuhao Wei, Haoxuan Fu, Xi Zhong, Xuelei Ma
Summary: The study aimed to predict the molecular features and overall survival (OS) of endometrial carcinoma (EC) patients using histopathological imaging. By analyzing histological image features and constructing random forest models verified by cross-validation, prognostic models for OS were developed. The performance of the models was evaluated using AUC over the test set.
Article
Cell Biology
Linyan Chen, Hao Zeng, Yu Xiang, Yeqian Huang, Yuling Luo, Xuelei Ma
Summary: In this study, machine learning models based on histopathological image features successfully predicted genetic aberrations, transcriptional subtypes, and survival outcomes of LUAD patients. The combination of histopathological image features with single omics data improved the prognostic power for LUAD patients.
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
(2021)
Article
Medicine, General & Internal
Xiaoxiao Cheng, Chong Dai, Yuqi Wen, Xiaoqi Wang, Xiaochen Bo, Song He, Shaoliang Peng
Summary: This study presents a multidimensional data integration model for predicting drug response in cells. The results show that the model effectively integrates structural features of drugs and cellular features from different omics data, outperforming previous approaches in terms of prediction accuracy. The model demonstrates strong reliability and stability.
Article
Biochemical Research Methods
Diogo B. Lima, Mathieu Dupre, Magalie Duchateau, Quentin Giai Gianetto, Martial Rey, Mariette Matondo, Julia Chamot-Rooke
Summary: The software can integrate data from multiple experiments and search engines, enabling rapid and easy visualization, validation, and comparison of proteoform sequences with high performance. The effectiveness of the method was demonstrated on a large-scale Escherichia coli dataset, where ProteoCombiner accurately shortlisted proteoforms identified by multiple search engines.
Article
Biochemistry & Molecular Biology
Catarina Marques-Pereira, Manuel N. Pires, Raquel P. Gouveia, Nadia N. Pereira, Ana B. Caniceiro, Nicia Rosario-Ferreira, Irina S. Moreira
Summary: This study examined the structural characteristics of the Membrane (M) protein of the Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) and predicted its dimeric structure and membrane orientation using computational methods. The study also identified a number of mutation sites in the M protein that appeared in variants of the virus, which could have implications for the development of new therapeutics.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Review
Plant Sciences
Federico Scossa, Saleh Alseekh, Alisdair R. Fernie
Summary: Our agricultural systems are facing the urgent challenge of feeding a growing global population, requiring a better understanding of plant genetic and phenotypic diversity. Recent advances in plant genomics, combined with molecular profiling approaches such as genomics, transcriptomics, and metabolomics, offer insights into the complex architecture of agricultural traits and guide crop breeding strategies.
JOURNAL OF PLANT PHYSIOLOGY
(2021)
Article
Energy & Fuels
Ziqi Shen, Wei Wei, Danman Wu, Tao Ding, Shengwei Mei
Summary: This paper discusses implementing a non-complementary strategy and proposes replacing strict complementarity with a weaker yet linear constraint. By modeling energy storage unit arbitrage via a linear program, it improves flexibility and profitability. The linearity also benefits theoretical analysis on more complex optimization problems involving duality and convex analysis.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2021)
Review
Biochemistry & Molecular Biology
Madeline Alizadeh, Natalia Sampaio Moura, Alyssa Schledwitz, Seema A. Patil, Jacques Ravel, Jean-Pierre Raufman
Summary: Studying individual data types in isolation is limited in providing comprehensive answers, but multi-omics approaches can generate and integrate multiple data types to offer a holistic understanding of biological and disease processes. Gastroenterology and hepatobiliary research benefit from these approaches due to the interconnectedness of the GI tract, brain, immune and endocrine systems, and GI microbiome. The use of big data in multi-omic, multi-site studies allows for better investigations into the connections between organ systems and more accurate evaluations of interventions.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Biochemical Research Methods
Heather Desaire, Milani Wijeweera Patabandige, David Hua
Summary: This study addresses the challenge of signal variability in mass spectrometry data and introduces a new statistical approach, the local-balanced model, to classify samples. By utilizing balanced subsets of training data to classify test samples, this model shows potential for generalizability across multiple mass spectrometry domains and can significantly improve classification accuracy compared to simple normalization methods.
ANALYTICAL AND BIOANALYTICAL CHEMISTRY
(2021)
Article
Oncology
Huifang Chen, Xiaosong Lan, Tao Yu, Lan Li, Sun Tang, Shuling Liu, Fujie Jiang, Lu Wang, Yao Huang, Ying Cao, Wei Wang, Xiaoxia Wang, Jiuquan Zhang
Summary: This study developed a radiogenomics model to predict axillary lymph node metastasis in breast cancer by integrating transcriptomic data and MRI data. The results showed that the radiogenomics model outperformed the genomics and radiomics models in predicting metastasis in breast cancer.
FRONTIERS IN ONCOLOGY
(2022)
Article
Multidisciplinary Sciences
Elif Everest, Ege Ulgen, Ugur Uygunoglu, Melih Tutuncu, Sabahattin Saip, Osman Ugur Sezerman, Aksel Siva, Eda Tahir Turanli
Summary: By combining genomic and proteomic data of MS patients, shared pathways were identified, suggesting that integrating multiple datasets can reduce false positive and negative results in genome-wide SNP associations.
Review
Environmental Sciences
Shafeeq Ur Rahman, Muhammad Farrakh Nawaz, Sadaf Gul, Ghulam Yasin, Babar Hussain, Yanliang Li, Hefa Cheng
Summary: Environmental pollution caused by heavy metals from anthropogenic activities has become a topic of increasing concern. Heavy metals, especially non-essential carcinogens, have been identified as major air, water, and soil pollutants that negatively impact the quantity, quality, and safety of plant-based food worldwide. Plants exposed to heavy metals experience reduced growth and yield, but they have developed complex defense mechanisms to avoid or tolerate the toxic effects. OMICS strategies have been widely used to understand the mechanisms of plant response and adaptation to heavy metal stress. Recent advancements in the understanding of the interaction between heavy metals and plants at molecular and cellular levels, as well as plants' defense strategies, are summarized in this review. The use of transcriptomics, genomics, metabolomics, proteomics, and ionomics for studying plant responses to heavy metal toxicity is reviewed, and challenges and future recommendations are discussed.
ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
(2022)
Review
Immunology
Robyn S. Kent, Emma M. Briggs, Beatrice L. Colon, Catalina Alvarez, Sara Silva Pereira, Mariana De Niz
Summary: This review discusses the methods used in Apicomplexan and Kinetoplastid research to generate big datasets and advance our understanding of related biology. The benefits and limitations of current technologies are debated, and potential future advancements are proposed.
FRONTIERS IN CELLULAR AND INFECTION MICROBIOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Dan Wu, Xinyi Guo, Jing Su, Ruoying Chen, Dmitriy Berenzon, Martin Guthold, Keith Bonin, Weiling Zhao, Xiaobo Zhou
BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR CELL RESEARCH
(2015)
Article
Biochemistry & Molecular Biology
Chenglin Liu, Jing Su, Fei Yang, Kun Wei, Jinwen Ma, Xiaobo Zhou
MOLECULAR BIOSYSTEMS
(2015)
Article
Biochemical Research Methods
Huiming Peng, Tao Peng, Jianguo Wen, David A. Engler, Rise K. Matsunami, Jing Su, Le Zhang, Chung-Che (Jeff) Chang, Xiaobo Zhou
Article
Oncology
Dong Soon Choi, Daniel J. Stark, Robert M. Raphael, Jianguo Wen, Jing Su, Xiaobo Zhou, Chung-Che Chang, Youli Zu
INTERNATIONAL JOURNAL OF CANCER
(2015)
Article
Multidisciplinary Sciences
Jing Su, Le Zhang, Wen Zhang, Dong Song Choi, Jianguo Wen, Beini Jiang, Chung-Che Chang, Xiaobo Zhou
Article
Genetics & Heredity
Qianqian Song, Jing Su, Lance D. Miller, Wei Zhang
Summary: In gene expression profiling studies, specifically in single-cell RNA sequencing analyses, accurately identifying and clustering co-expressed genes is essential for understanding cell identity and function. Existing methods for single-cell data often fail to accurately identify co-expressed genes, but the scLM algorithm tailored for single-cell data proves to be effective in detecting biologically significant gene clusters and can cluster multiple single-cell datasets simultaneously. Results from simulation and experimental data show that scLM outperforms existing methods and provides novel biological insights for mechanism discovery and understanding complex biosystems like cancer.
GENOMICS PROTEOMICS & BIOINFORMATICS
(2021)
Article
Multidisciplinary Sciences
Qianqian Song, Jing Su, Wei Zhang
Summary: Single-cell omics is a rapidly growing area in genomics, but leveraging disparate datasets for analysis is challenging. The scGCN, a graph convolutional network, allows for effective knowledge transfer across different omics datasets.
NATURE COMMUNICATIONS
(2021)
Letter
Oncology
Noha Sharafeldin, Benjamin Bates, Qianqian Song, Vithal Madhira, Yu Raymond Shao, Feifan Liu, Timothy Bergquist, Jing Su, Umit Topaloglu
JOURNAL OF CLINICAL ONCOLOGY
(2021)
Article
Biology
Minghan Chen, Chunrui Xu, Ziang Xu, Wei He, Haorui Zhang, Jing Su, Qianqian Song
Summary: In this study, the functional and signaling pathways of lung cancer therapeutics were comprehensively investigated using bioinformatics inference and multiscale modeling. Key genes involved in the effects of DEX treatment were identified and the TGF beta signaling pathway was found to be associated with survival prognosis in clinical lung cancer samples. A multiscale model of tumor regulation centered on both TGF beta-induced and ERBB-amplified signaling pathways was developed, and simulation results confirmed the dynamic effects of DEX therapy on lung cancer cells.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biochemical Research Methods
Ziyang Tang, Tonglin Zhang, Baijian Yang, Jing Su, Qianqian Song
Summary: Cell-cell communications are essential for biological signalling and play crucial roles in complex diseases. The development of single-cell spatial transcriptomics (SCST) technologies allows us to explore the spatial landscapes of cell communication. However, accurately inferring cellular communications from SCST data is challenging due to dropout events and noisy signals. In this study, we propose a novel adaptive graph model named spaCI, which integrates spatial locations and gene expression profiles to identify active ligand-receptor signalling axes between neighboring cells. By benchmarking with other methods, spaCI demonstrates superior performance on simulation data and real SCST datasets. Furthermore, spaCI can identify upstream transcriptional factors involved in the active ligand-receptor interactions. Applying spaCI to mouse cortex and non-small cell lung cancer datasets, we uncover hidden ligand-receptor interactions and reveal the importance of SMAD3 in regulating crosstalk between fibroblasts and tumors. Overall, spaCI addresses the challenges in interrogating SCST data to gain insights into cellular communications, enabling the discovery of disease mechanisms, effective biomarkers, and therapeutic targets.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Multidisciplinary Sciences
Ziyang Tang, Zuotian Li, Tieying Hou, Tonglin Zhang, Baijian Yang, Jing Su, Qianqian Song
Summary: SiGra is a method that leverages imaging information to reveal spatial domains and enhance sparse and noisy transcriptomics data. It outperforms state-of-the-art methods on both single-cell and spot-level spatial transcriptomics data from complex tissues, with a 37% improvement in model performance when including immunohistochemistry images.
NATURE COMMUNICATIONS
(2023)
Article
Oncology
Amit Kumar Mitra, Ujjal Kumar Mukherjee, Suman Mazumder, Vithal Madhira, Timothy Bergquist, Yu Raymond Shao, Feifan Liu, Qianqian Song, Jing Su, Shaji Kumar, Benjamin A. Bates, Noha Sharafeldin, Umit Topaloglu, Christopher G. Chute, Richard A. Moffitt, Melissa A. Haendel
Summary: This study analyzed the risk factors for severe COVID-19 symptoms and all-cause mortality in multiple myeloma (MM) patients using a large database. The results showed that a history of pulmonary and renal diseases, certain treatments, and a severe comorbidity index were significantly associated with higher risks of severe COVID-19 symptoms and death, while blood or marrow transplant and COVID-19 vaccination were associated with lower risk.
BLOOD CANCER JOURNAL
(2023)
Article
Genetics & Heredity
Qianqian Song, Xuewei Zhu, Lingtao Jin, Minghan Chen, Wei Zhang, Jing Su
Summary: In this study, a novel method called SMGR was developed to detect functional regulatory signals and target genes from single-cell multi-omics data. Results showed that SMGR outperformed existing methods in terms of accuracy. Application of SMGR to mixed-phenotype acute leukemia (MPAL) identified MPAL-specific regulatory programs, enhancing our understanding of the regulatory mechanisms and potential targets of this complex tumor.
NAR GENOMICS AND BIOINFORMATICS
(2022)
Article
Urology & Nephrology
Michael T. Eadon, Judith Maddatu, Sharon M. Moe, Arjun D. Sinha, Ricardo Melo Ferreira, Brent W. Miller, S. Jawad Sher, Jing Su, Victoria M. Pratt, Arlene B. Chapman, Todd C. Skaar, Ranjani N. Moorthi
Summary: The CKD-PGX study investigated the feasibility of pharmacogenomic testing in optimizing antihypertensive regimens for patients with CKD. The study found that most patients with uncontrolled hypertension had drug-gene interactions that predicted reduced efficacy of their medications.