57 research outputs found
Empowering Graph Representation Learning with Test-Time Graph Transformation
As powerful tools for representation learning on graphs, graph neural
networks (GNNs) have facilitated various applications from drug discovery to
recommender systems. Nevertheless, the effectiveness of GNNs is immensely
challenged by issues related to data quality, such as distribution shift,
abnormal features and adversarial attacks. Recent efforts have been made on
tackling these issues from a modeling perspective which requires additional
cost of changing model architectures or re-training model parameters. In this
work, we provide a data-centric view to tackle these issues and propose a graph
transformation framework named GTrans which adapts and refines graph data at
test time to achieve better performance. We provide theoretical analysis on the
design of the framework and discuss why adapting graph data works better than
adapting the model. Extensive experiments have demonstrated the effectiveness
of GTrans on three distinct scenarios for eight benchmark datasets where
suboptimal data is presented. Remarkably, GTrans performs the best in most
cases with improvements up to 2.8%, 8.2% and 3.8% over the best baselines on
three experimental settings
The instant effect of embodiment via mirror visual feedback on electroencephalogram-based brain connectivity changes: A pilot study
The therapeutic efficacy of mirror visual feedback (MVF) is attributed to the perception of embodiment. This study intends to investigate the instantaneous effect of embodiment on brain connectivity. Twelve healthy subjects were required to clench and open their non-dominant hands and keep the dominant hands still during two experimental sessions. In the first session, the dominant hand was covered and no MVF was applied, named the sham-MVF condition. Random vibrotactile stimulations were applied to the non-dominant hand with MVF in the subsequent session. Subjects were asked to pedal while having embodiment perception during motor tasks. As suggested by previous findings, trials of no vibration and continuous vibration were selected for this study, named the condition of MVF and vt-MVF. EEG signals were recorded and the alterations in brain connectivity were analyzed. The average node degrees of sham-MVF, MVF, and vt-MVF conditions were largely different in the alpha band (9.94, 11.19, and 17.37, respectively). Further analyses showed the MVF and vt-MVF had more nodes with a significantly large degree, which mainly occurred in the central and the visual stream involved regions. Results of network metrics showed a significant increment of local and global efficiency, and a reduction of characteristic path length for the vt-MVF condition in the alpha and beta bands compared to sham-MVF, and in the alpha band compared to MVF. Similar trends were found for MVF condition in the beta band compared to sham-MVF. Moreover, significant leftward asymmetry of global efficiency and rightward asymmetry of characteristic path length was reported in the vt-MVF condition in the beta band. These results indicated a positive impact of embodiment on network connectivity and neural communication efficiency, which reflected the potential mechanisms of MVF for new insight into neural modulation
Single-Cell Multimodal Prediction via Transformers
The recent development of multimodal single-cell technology has made the
possibility of acquiring multiple omics data from individual cells, thereby
enabling a deeper understanding of cellular states and dynamics. Nevertheless,
the proliferation of multimodal single-cell data also introduces tremendous
challenges in modeling the complex interactions among different modalities. The
recently advanced methods focus on constructing static interaction graphs and
applying graph neural networks (GNNs) to learn from multimodal data. However,
such static graphs can be suboptimal as they do not take advantage of the
downstream task information; meanwhile GNNs also have some inherent limitations
when deeply stacking GNN layers. To tackle these issues, in this work, we
investigate how to leverage transformers for multimodal single-cell data in an
end-to-end manner while exploiting downstream task information. In particular,
we propose a scMoFormer framework which can readily incorporate external domain
knowledge and model the interactions within each modality and cross modalities.
Extensive experiments demonstrate that scMoFormer achieves superior performance
on various benchmark datasets. Remarkably, scMoFormer won a Kaggle silver medal
with the rank of 24/1221 (Top 2%) without ensemble in a NeurIPS 2022
competition. Our implementation is publicly available at Github.Comment: CIKM 202
Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation
Recent years have witnessed the dramatic growth of paper volumes with plenty
of new research papers published every day, especially in the area of computer
science. How to glean papers worth reading from the massive literature to do a
quick survey or keep up with the latest advancement about a specific research
topic has become a challenging task. Existing academic search engines such as
Google Scholar return relevant papers by individually calculating the relevance
between each paper and query. However, such systems usually omit the
prerequisite chains of a research topic and cannot form a meaningful reading
path. In this paper, we introduce a new task named Reading Path Generation
(RPG) which aims at automatically producing a path of papers to read for a
given query. To serve as a research benchmark, we further propose SurveyBank, a
dataset consisting of large quantities of survey papers in the field of
computer science as well as their citation relationships. Each survey paper
contains key phrases extracted from its title and multi-level reading lists
inferred from its references. Furthermore, we propose a
graph-optimization-based approach for reading path generation which takes the
relationship between papers into account. Extensive evaluations demonstrate
that our approach outperforms other baselines. A Real-time Reading Path
Generation System (RePaGer) has been also implemented with our designed model.
To the best of our knowledge, we are the first to target this important
research problem. Our source code of RePaGer system and SurveyBank dataset can
be found on here.Comment: 16 pages, 12 figure
Deep Learning in Single-Cell Analysis
Single-cell technologies are revolutionizing the entire field of biology. The
large volumes of data generated by single-cell technologies are
high-dimensional, sparse, heterogeneous, and have complicated dependency
structures, making analyses using conventional machine learning approaches
challenging and impractical. In tackling these challenges, deep learning often
demonstrates superior performance compared to traditional machine learning
methods. In this work, we give a comprehensive survey on deep learning in
single-cell analysis. We first introduce background on single-cell technologies
and their development, as well as fundamental concepts of deep learning
including the most popular deep architectures. We present an overview of the
single-cell analytic pipeline pursued in research applications while noting
divergences due to data sources or specific applications. We then review seven
popular tasks spanning through different stages of the single-cell analysis
pipeline, including multimodal integration, imputation, clustering, spatial
domain identification, cell-type deconvolution, cell segmentation, and
cell-type annotation. Under each task, we describe the most recent developments
in classical and deep learning methods and discuss their advantages and
disadvantages. Deep learning tools and benchmark datasets are also summarized
for each task. Finally, we discuss the future directions and the most recent
challenges. This survey will serve as a reference for biologists and computer
scientists, encouraging collaborations.Comment: 77 pages, 11 figures, 15 tables, deep learning, single-cell analysi
Risk factors and prediction model of sleep disturbance in patients with maintenance hemodialysis: A single center study
ObjectivesThis study aimed to explore the risk factors and develop a prediction model of sleep disturbance in maintenance hemodialysis (MHD) patients.MethodsIn this study, 193 MHD patients were enrolled and sleep quality was assessed by Pittsburgh Sleep Quality Index. Binary logistic regression analysis was used to explore the risk factors for sleep disturbance in MHD patients, including demographic, clinical and laboratory parameters, and that a prediction model was developed on the basis of risk factors by two-way stepwise regression. The final prediction model is displayed by nomogram and verified internally by bootstrap resampling procedure.ResultsThe prevalence of sleep disturbance and severe sleep disturbance in MHD patients was 63.73 and 26.42%, respectively. Independent risk factors for sleep disturbance in MHD patients included higher 0.1*age (OR = 1.476, 95% CI: 1.103–1.975, P = 0.009), lower albumin (OR = 0.863, 95% CI: 0.771–0.965, P = 0.010), and lower 10*calcium levels (OR = 0.747, 95% CI: 0.615–0.907, P = 0.003). In addition, higher 0.1*age, lower albumin levels, and anxiety were independently associated with severe sleep disturbance in MHD patients. A risk prediction model of sleep disturbance in MHD patients showed that the concordance index after calibration is 0.736, and the calibration curve is approximately distributed along the reference line.ConclusionsOlder age, lower albumin and calcium levels are higher risk factors of sleep disturbance in MHD, and the prediction model for the assessment of sleep disturbance in MHD patients has excellent discrimination and calibration
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