111 research outputs found
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
Graph representation learning (GRL) has emerged as a pivotal field that has
contributed significantly to breakthroughs in various fields, including
biomedicine. The objective of this survey is to review the latest advancements
in GRL methods and their applications in the biomedical field. We also
highlight key challenges currently faced by GRL and outline potential
directions for future research.Comment: Accepted by 2023 IMIA Yearbook of Medical Informatic
Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills
Learning-based vehicle planning is receiving increasing attention with the
emergence of diverse driving simulators and large-scale driving datasets. While
offline reinforcement learning (RL) is well suited for these safety-critical
tasks, it still struggles to plan over extended periods. In this work, we
present a skill-based framework that enhances offline RL to overcome the
long-horizon vehicle planning challenge. Specifically, we design a variational
autoencoder (VAE) to learn skills from offline demonstrations. To mitigate
posterior collapse of common VAEs, we introduce a two-branch sequence encoder
to capture both discrete options and continuous variations of the complex
driving skills. The final policy treats learned skills as actions and can be
trained by any off-the-shelf offline RL algorithms. This facilitates a shift in
focus from per-step actions to temporally extended skills, thereby enabling
long-term reasoning into the future. Extensive results on CARLA prove that our
model consistently outperforms strong baselines at both training and new
scenarios. Additional visualizations and experiments demonstrate the
interpretability and transferability of extracted skills
Boosting Operational DNN Testing Efficiency through Conditioning
With the increasing adoption of Deep Neural Network (DNN) models as integral
parts of software systems, efficient operational testing of DNNs is much in
demand to ensure these models' actual performance in field conditions. A
challenge is that the testing often needs to produce precise results with a
very limited budget for labeling data collected in field.
Viewing software testing as a practice of reliability estimation through
statistical sampling, we re-interpret the idea behind conventional structural
coverages as conditioning for variance reduction. With this insight we propose
an efficient DNN testing method based on the conditioning on the representation
learned by the DNN model under testing. The representation is defined by the
probability distribution of the output of neurons in the last hidden layer of
the model. To sample from this high dimensional distribution in which the
operational data are sparsely distributed, we design an algorithm leveraging
cross entropy minimization.
Experiments with various DNN models and datasets were conducted to evaluate
the general efficiency of the approach. The results show that, compared with
simple random sampling, this approach requires only about a half of labeled
inputs to achieve the same level of precision.Comment: Published in the Proceedings of the 27th ACM Joint European Software
Engineering Conference and Symposium on the Foundations of Software
Engineering (ESEC/FSE 2019
From moments of the distribution function to hydrodynamics the non-conformal case
We study the one-dimensional boost-invariant Boltzmann equation in the
relaxation-time approximation using special moments of the distribution
function for a system with a finite particle mass. The infinite hierarchy of
moments can be truncated by keeping only the three lowest moments that
correspond to the three independent components of the energy-momentum tensor.
We show that such a three-moment truncation reproduces accurately the exact
solution of the kinetic equation after a simple renormalization that takes into
account the effects of the neglected higher moments. We derive second-order
Israel-Stewart hydrodynamic equations from the three-moment equations, and show
that, for most physically relevant initial conditions, these equations yield
results comparable to those of the three-moment truncation, albeit less
accurate. We attribute this feature to the fact that the structure of
Israel-Stewart equations is similar to that of the three-moment truncation. In
particular, the presence of the relaxation term in the Israel-Stewart
equations, yields an early-time regime that mimics approximately the
collisionless regime. A detailed comparison of the three-moment truncation with
second-order non-conformal hydrodynamics reveals ambiguities in the definition
of second-order transport coefficients. These ambiguities affect the ability of
Israel-Stewart hydrodynamics to reproduce results of kinetic theory.Comment: 44 pages, 11 figure
Explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction
Alzheimer's disease and related dementias (ADRD) ranks as the sixth leading
cause of death in the US, underlining the importance of accurate ADRD risk
prediction. While recent advancement in ADRD risk prediction have primarily
relied on imaging analysis, yet not all patients undergo medical imaging before
an ADRD diagnosis. Merging machine learning with claims data can reveal
additional risk factors and uncover interconnections among diverse medical
codes. Our goal is to utilize Graph Neural Networks (GNNs) with claims data for
ADRD risk prediction. Addressing the lack of human-interpretable reasons behind
these predictions, we introduce an innovative method to evaluate relationship
importance and its influence on ADRD risk prediction, ensuring comprehensive
interpretation.
We employed Variationally Regularized Encoder-decoder Graph Neural Network
(VGNN) for estimating ADRD likelihood. We created three scenarios to assess the
model's efficiency, using Random Forest and Light Gradient Boost Machine as
baselines. We further used our relation importance method to clarify the key
relationships for ADRD risk prediction. VGNN surpassed other baseline models by
10% in the area under the receiver operating characteristic. The integration of
the GNN model and relation importance interpretation could potentially play an
essential role in providing valuable insight into factors that may contribute
to or delay ADRD progression.
Employing a GNN approach with claims data enhances ADRD risk prediction and
provides insights into the impact of interconnected medical code relationships.
This methodology not only enables ADRD risk modeling but also shows potential
for other image analysis predictions using claims data
Pretreatment plasma fibrinogen level as a prognostic biomarker for patients with lung cancer
Many researchers have shown that pretreatment plasma fibrinogen levels are closely correlated with the prognosis of patients with lung cancer (LC). In this study, we thus performed a meta-analysis to systematically assess the prognostic value of pretreatment plasma fibrinogen levels in LC patients. A computerized systematic search in PubMed, EMBASE, Web of Science and China National Knowledge Infrastructure (CNKI) was performed up to March 15, 2018. Studies with available data on the prognostic value of plasma fibrinogen in LC patients were eligible for inclusion. The pooled hazard ratios (HRs) and odd ratios (ORs) with 95% confidence intervals (CIs) were used to evaluate the correlation between pretreatment plasma fibrinogen levels and prognosis as well as clinicopathological characteristics. A total of 17 studies with 6,460 LC patients were included in this meta-analysis. A higher pretreatment plasma fibrinogen level was significantly associated with worse overall survival (OS) (HR: 1.57; 95% CI: 1.39-1.77; p=0.001), disease-free survival (DFS) (HR: 1.53; 95% CI: 1.33-1.76; p=0.003), and progression-free survival (PFS) (HR: 3.14; 95% CI: 2.15-4.59; po0.001). Furthermore, our subgroup and sensitivity analyses demonstrated that the pooled HR for OS was robust and reliable. In addition, we also found that a higher fibrinogen level predicted advanced TNM stage (III-IV) (OR=2.18, 95% CI: 1.79-2.66; po0.001) and a higher incidence of lymph node metastasis (OR=1.74, 95% CI: 1.44-2.10; p=0.02). Our study suggested that higher pretreatment plasma fibrinogen levels predict worse prognoses in LC patients
Innate Immune Cells: A Potential and Promising Cell Population for Treating Osteosarcoma
Advanced, recurrent, or metastasized osteosarcomas remain challenging to cure or even alleviate. Therefore, the development of novel therapeutic strategies is urgently needed. Cancer immunotherapy has greatly improved in recent years, with options including adoptive cellular therapy, vaccination, and checkpoint inhibitors. As such, immunotherapy is becoming a potential strategy for the treatment of osteosarcoma. Innate immunocytes, the first line of defense in the immune system and the bridge to adaptive immunity, are one of the vital effector cell subpopulations in cancer immunotherapy. Innate immune cell-based therapy has shown potent antitumor activity against hematologic malignancies and some solid tumors, including osteosarcoma. Importantly, some immune checkpoints are expressed on both innate and adaptive immune cells, modulating their functions in tumor immunity. Therefore, blocking or activating immune checkpoint-mediated downstream signaling pathways can improve the therapeutic effects of innate immune cell-based therapy. In this review, we summarize the current status and future prospects of innate immune cell-based therapy for the treatment of osteosarcoma, with a focus on the potential synergistic effects of combination therapy involving innate immunotherapy and immune checkpoint inhibitors/oncolytic viruses
CHORD: Category-level Hand-held Object Reconstruction via Shape Deformation
In daily life, humans utilize hands to manipulate objects. Modeling the shape
of objects that are manipulated by the hand is essential for AI to comprehend
daily tasks and to learn manipulation skills. However, previous approaches have
encountered difficulties in reconstructing the precise shapes of hand-held
objects, primarily owing to a deficiency in prior shape knowledge and
inadequate data for training. As illustrated, given a particular type of tool,
such as a mug, despite its infinite variations in shape and appearance, humans
have a limited number of 'effective' modes and poses for its manipulation. This
can be attributed to the fact that humans have mastered the shape prior of the
'mug' category, and can quickly establish the corresponding relations between
different mug instances and the prior, such as where the rim and handle are
located. In light of this, we propose a new method, CHORD, for Category-level
Hand-held Object Reconstruction via shape Deformation. CHORD deforms a
categorical shape prior for reconstructing the intra-class objects. To ensure
accurate reconstruction, we empower CHORD with three types of awareness:
appearance, shape, and interacting pose. In addition, we have constructed a new
dataset, COMIC, of category-level hand-object interaction. COMIC contains a
rich array of object instances, materials, hand interactions, and viewing
directions. Extensive evaluation shows that CHORD outperforms state-of-the-art
approaches in both quantitative and qualitative measures. Code, model, and
datasets are available at https://kailinli.github.io/CHORD.Comment: To be presented at ICCV 2023, Pari
Degradation of the Separase-cleaved Rec8, a Meiotic Cohesin Subunit, by the N-end Rule Pathway
The Ate1 arginyltransferase (R-transferase) is a component of the N-end rule pathway, which recognizes proteins containing N-terminal degradation signals called N-degrons, polyubiquitylates these proteins, and thereby causes their degradation by the proteasome. Ate1 arginylates N-terminal Asp, Glu, or (oxidized) Cys. The resulting N-terminal Arg is recognized by ubiquitin ligases of the N-end rule pathway. In the yeast Saccharomyces cerevisiae, the separase-mediated cleavage of the Scc1/Rad21/Mcd1 cohesin subunit generates a C-terminal fragment that bears N-terminal Arg and is destroyed by the N-end rule pathway without a requirement for arginylation. In contrast, the separase-mediated cleavage of Rec8, the mammalian meiotic cohesin subunit, yields a fragment bearing N-terminal Glu, a substrate of the Ate1 R-transferase. Here we constructed and used a germ cell-confined Ate1−/− mouse strain to analyze the separase-generated C-terminal fragment of Rec8. We show that this fragment is a short-lived N-end rule substrate, that its degradation requires N-terminal arginylation, and that male Ate1−/− mice are nearly infertile, due to massive apoptotic death of Ate1−/− spermatocytes during the metaphase of meiosis I. These effects of Ate1 ablation are inferred to be caused, at least in part, by the failure to destroy the C-terminal fragment of Rec8 in the absence of N-terminal arginylation
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