IU Indianapolis ScholarWorks :: Browsing by Author "Wang, Xiao"

Browsing by Author "Wang, Xiao"

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A data denoising approach to optimize functional clustering of single cell RNA-sequencing data
(IEEE, 2020-12) Wan, Changlin; Jia, Dongya; Zhao, Yue; Chang, Wennan; Cao, Sha; Wang, Xiao; Zhang, Chi; Medical and Molecular Genetics, School of Medicine
Single cell RNA-sequencing (scRNA-seq) technology enables comprehensive transcriptomic profiling of thousands of cells with distinct phenotypic and physiological states in a complex tissue. Substantial efforts have been made to characterize single cells of distinct identities from scRNA-seq data, including various cell clustering techniques. While existing approaches can handle single cells in terms of different cell (sub)types at a high resolution, identification of the functional variability within the same cell type remains unsolved. In addition, there is a lack of robust method to handle the inter-subject variation that often brings severe confounding effects for the functional clustering of single cells. In this study, we developed a novel data denoising and cell clustering approach, namely CIBS, to provide biologically explainable functional classification for scRNA-seq data. CIBS is based on a systems biology model of transcriptional regulation that assumes a multi-modality distribution of the cells' activation status, and it utilizes a Boolean matrix factorization approach on the discretized expression status to robustly derive functional modules. CIBS is empowered by a novel fast Boolean Matrix Factorization method, namely PFAST, to increase the computational feasibility on large scale scRNA-seq data. Application of CIBS on two scRNA-seq datasets collected from cancer tumor micro-environment successfully identified subgroups of cancer cells with distinct expression patterns of epithelial-mesenchymal transition and extracellular matrix marker genes, which was not revealed by the existing cell clustering analysis tools. The identified cell groups were significantly associated with the clinically confirmed lymph-node invasion and metastasis events across different patients.
Detecting Traffic Information From Social Media Texts With Deep Learning Approaches
(IEEE, 2018-11) Chen, Yuanyuan; Lv, Yisheng; Wang, Xiao; Li, Lingxi; Wang, Fei-Yue; Electrical and Computer Engineering, School of Engineering and Technology
Mining traffic-relevant information from social media data has become an emerging topic due to the real-time and ubiquitous features of social media. In this paper, we focus on a specific problem in social media mining which is to extract traffic relevant microblogs from Sina Weibo, a Chinese microblogging platform. It is transformed into a machine learning problem of short text classification. First, we apply the continuous bag-of-word model to learn word embedding representations based on a data set of three billion microblogs. Compared to the traditional one-hot vector representation of words, word embedding can capture semantic similarity between words and has been proved effective in natural language processing tasks. Next, we propose using convolutional neural networks (CNNs), long short-term memory (LSTM) models and their combination LSTM-CNN to extract traffic relevant microblogs with the learned word embeddings as inputs. We compare the proposed methods with competitive approaches, including the support vector machine (SVM) model based on a bag of n-gram features, the SVM model based on word vector features, and the multi-layer perceptron model based on word vector features. Experiments show the effectiveness of the proposed deep learning approaches.
Excess TGF-β mediates muscle weakness associated with bone metastases in mice
(SpringerNature, 2015-11) Waning, David L.; Mohammad, Khalid S.; Reiken, Steven; Xie, Wenjun; Andersson, Daniel C.; John, Sutha; Chiechi, Antonella; Wright, Laura E.; Umanskaya, Alisa; Niewolna, Maria; Trivedi, Trupti; Charkhzarrin, Sahba; Khatiwada, Pooja; Wronska, Anetta; Haynes, Ashley; Benassi, Maria Serena; Witzmann, Frank A.; Zhen, Gehua; Wang, Xiao; Cao, Xu; Roodman, G. David; Marks, Andrew R.; Guise, Theresa A.; Department of Medicine, IU School of Medicine
Cancer-associated muscle weakness is a poorly understood phenomenon, and there is no effective treatment. Here we find that seven different mouse models of human osteolytic bone metastases-representing breast, lung and prostate cancers, as well as multiple myeloma-exhibited impaired muscle function, implicating a role for the tumor-bone microenvironment in cancer-associated muscle weakness. We found that transforming growth factor (TGF)-β, released from the bone surface as a result of metastasis-induced bone destruction, upregulated NADPH oxidase 4 (Nox4), resulting in elevated oxidization of skeletal muscle proteins, including the ryanodine receptor and calcium (Ca(2+)) release channel (RyR1). The oxidized RyR1 channels leaked Ca(2+), resulting in lower intracellular signaling, which is required for proper muscle contraction. We found that inhibiting RyR1 leakage, TGF-β signaling, TGF-β release from bone or Nox4 activity improved muscle function in mice with MDA-MB-231 bone metastases. Humans with breast- or lung cancer-associated bone metastases also had oxidized skeletal muscle RyR1 that is not seen in normal muscle. Similarly, skeletal muscle weakness, increased Nox4 binding to RyR1 and oxidation of RyR1 were present in a mouse model of Camurati-Engelmann disease, a nonmalignant metabolic bone disorder associated with increased TGF-β activity. Thus, pathological TGF-β release from bone contributes to muscle weakness by decreasing Ca(2+)-induced muscle force production.
FLUXestimator: a webserver for predicting metabolic flux and variations using transcriptomics data
(Oxford University Press, 2023) Zhang, Zixuan; Zhu, Haiqi; Dang, Pengtao; Wang, Jia; Chang, Wennan; Wang, Xiao; Alghamdi, Norah; Lu, Alex; Zang, Yong; Wu, Wenzhuo; Wang, Yijie; Zhang, Yu; Cao, Sha; Zhang, Chi; Medical and Molecular Genetics, School of Medicine
Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies.
OC_Finder: Osteoclast Segmentation, Counting, and Classification Using Watershed and Deep Learning
(Frontiers Media, 2022) Wang, Xiao; Kittaka, Mizuho; He, Yilin; Zhang, Yiwei; Ueki, Yasuyoshi; Kihara, Daisuke; Biomedical Sciences and Comprehensive Care, School of Dentistry
Osteoclasts are multinucleated cells that exclusively resorb bone matrix proteins and minerals on the bone surface. They differentiate from monocyte/macrophage lineage cells in the presence of osteoclastogenic cytokines such as the receptor activator of nuclear factor-κB ligand (RANKL) and are stained positive for tartrate-resistant acid phosphatase (TRAP). In vitro osteoclast formation assays are commonly used to assess the capacity of osteoclast precursor cells for differentiating into osteoclasts wherein the number of TRAP-positive multinucleated cells is counted as osteoclasts. Osteoclasts are manually identified on cell culture dishes by human eyes, which is a labor-intensive process. Moreover, the manual procedure is not objective and results in lack of reproducibility. To accelerate the process and reduce the workload for counting the number of osteoclasts, we developed OC_Finder, a fully automated system for identifying osteoclasts in microscopic images. OC_Finder consists of cell image segmentation with a watershed algorithm and cell classification using deep learning. OC_Finder detected osteoclasts differentiated from wild-type and Sh3bp2 KI/+ precursor cells at a 99.4% accuracy for segmentation and at a 98.1% accuracy for classification. The number of osteoclasts classified by OC_Finder was at the same accuracy level with manual counting by a human expert. OC_Finder also showed consistent performance on additional datasets collected with different microscopes with different settings by different operators. Together, successful development of OC_Finder suggests that deep learning is a useful tool to perform prompt and accurate unbiased classification and detection of specific cell types in microscopic images.
Social relation and physical lane aggregator: Integrating social and physical features for multimodal motion prediction
(Emerald Publishing, 2022-10-11) Chen, Qiyuan; Wei, Zebing; Wang, Xiao; Li, Lingxi; Lv, Yisheng; Electrical and Computer Engineering, School of Engineering and Technology
Purpose The purpose of this paper aims to model interaction relationship of traffic agents for motion prediction, which is critical for autonomous driving. It is obvious that traffic agents’ trajectories are influenced by physical lane rules and agents’ social interactions. Design/methodology/approach In this paper, the authors propose the social relation and physical lane aggregator for multimodal motion prediction, where the social relations of agents are mainly captured with graph convolutional networks and self-attention mechanism and then fused with the physical lane via the self-attention mechanism. Findings The proposed methods are evaluated on the Waymo Open Motion Dataset, and the results show the effectiveness of the proposed two feature aggregation modules for trajectory prediction. Originality/value This paper proposes a new design method to extract traffic interactions, and the attention mechanism is used in each part of the model to extract and fuse different relational features, which is different from other methods and improves the accuracy of the LSTM-based trajectory prediction method.
Steps toward Parallel Intelligence
(IEEE, 2016-10) Wang, Fei-Yue; Wang, Xiao; Li, Lingxi; Li, Li; Department of Electrical and Computer Engineering, School of Engineering and Technology
The origin of artificial intelligence is investigated, based on which the concepts of hybrid intelligence and parallel intelligence are presented. The paradigm shift in Intelligence indicates the "new normal" of cyber-social-physical systems (CPSS), in which the system behaviors are guided by Merton's Laws. Thus, the ACP-based parallel intelligence consisting of Artificial societies, Computational experiments and Parallel execution are introduced to bridge the big modeling gap in CPSS.
Tumor-Infiltrating Immune-Related Long Non-Coding RNAs Indicate Prognoses and Response to PD-1 Blockade in Head and Neck Squamous Cell Carcinoma
(Frontiers Media, 2021-10-19) Ma, Ben; Jiang, Hongyi; Luo, Yi; Liao, Tian; Xu, Weibo; Wang, Xiao; Dong, Chuanpeng; Ji, Qinghai; Wang, Yu; BioHealth Informatics, School of Informatics and Computing
Long non-coding RNAs (lncRNAs) in immune cells play critical roles in tumor cell-immune cell interactions. This study aimed to characterize the landscape of tumor-infiltrating immune-related lncRNAs (Ti-lncRNAs) and reveal their correlations with prognoses and immunotherapy response in head and neck squamous cell carcinoma (HNSCC). We developed a computational model to identify Ti-lncRNAs in HNSCC and analyzed their associations with clinicopathological features, molecular alterations, and immunotherapy response. A signature of nine Ti-lncRNAs demonstrated an independent prognostic factor for both overall survival and disease-free survival among the cohorts from Fudan University Shanghai Cancer Center, The Cancer Genome Atlas, GSE41613, and GSE42743. The Ti-lncRNA signature scores in immune cells showed significant associations with TP53 mutation, CDKN2A mutation, and hypoxia. Inferior signature scores were enriched in patients with high levels of PDCD1 and CTLA4 and high expanded immune gene signature (IGS) scores, who displayed good response to PD-1 blockade in HNSCC. Consistently, superior clinical response emerged in melanoma patients with low signature scores undergoing anti-PD-1 therapy. Moreover, the Ti-lncRNA signature was a prognostic factor independent of PDCD1, CTLA4, and the expanded IGS score. In conclusion, tumor-infiltrating immune profiling identified a prognostic Ti-lncRNA signature indicative of clinical response to PD-1 blockade in HNSCC.
Tunable spin-state bistability in a spin crossover molecular complex
(IOP, 2019) Jiang, Xuanyuan; Hao, Guanhua; Wang, Xiao; Mosey, Aaron; Zhang, Xin; Yu, Le; Yost, Andrew J.; Zhang, Xin; DiChiara, Anthony D.; N'Diaye, Alpha T.; Cheng, Xuemei; Zhang, Jian; Cheng, Ruihua; Xu, Xiaoshan; Dowben, Peter A.; Physics, School of Sciences
The spin crossover (SCO) transitions at both the surface and over the entire volume of the [Fe{H2B(pz)2}2(bipy)] polycrystalline films on Al2O3 substrates have been studied, where pz = pyrazol-1-yl and bipy = 2,2'-bipyridine. For [Fe{H2B(pz)2}2(bipy)] films of hundreds of nm thick, magnetometry and x-ray absorption spectroscopy measurements show thermal hysteresis in the SCO transition with temperature, although the transition in bulk [Fe{H2B(pz)2}2(bipy)] occurs in a non-hysteretic fashion at 157 K. While the size of the crystallites in those films are similar, the hysteresis becomes more prominent in thinner films, indicating a significant effect of the [Fe{H2B(pz)2}2(bipy)]/Al2O3 interface. Bistability of spin states, which can be inferred from the thermal hysteresis, was directly observed using temperature-dependent x-ray diffraction; the crystallites behave as spin-state domains that coexist during the transition. The difference between the spin state of molecules at the surface of the [Fe{H2B(pz)2}2(bipy)] films and that of the molecules within the films, during the thermal cycle, indicates that both cooperative (intermolecular) effects and coordination are implicated in perturbations to the SCO transition.

Browsing by Author "Wang, Xiao"

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