185 research outputs found
Generalized gene co-expression analysis via subspace clustering using low-rank representation
BACKGROUND:
Gene Co-expression Network Analysis (GCNA) helps identify gene modules with potential biological functions and has become a popular method in bioinformatics and biomedical research. However, most current GCNA algorithms use correlation to build gene co-expression networks and identify modules with highly correlated genes. There is a need to look beyond correlation and identify gene modules using other similarity measures for finding novel biologically meaningful modules.
RESULTS:
We propose a new generalized gene co-expression analysis algorithm via subspace clustering that can identify biologically meaningful gene co-expression modules with genes that are not all highly correlated. We use low-rank representation to construct gene co-expression networks and local maximal quasi-clique merger to identify gene co-expression modules. We applied our method on three large microarray datasets and a single-cell RNA sequencing dataset. We demonstrate that our method can identify gene modules with different biological functions than current GCNA methods and find gene modules with prognostic values.
CONCLUSIONS:
The presented method takes advantage of subspace clustering to generate gene co-expression networks rather than using correlation as the similarity measure between genes. Our generalized GCNA method can provide new insights from gene expression datasets and serve as a complement to current GCNA algorithms
Prediction of nonlinear interface dynamics in the unidirectional freezing of particle suspensions with rigid compacted layer
Water freezing in particle suspensions widely exists in nature. As a typical
physical system of free boundary problem, the spatiotemporal evolution of the
solid/liquid interface not only origins from phase transformation but also from
permeation flow in front of ice. Physical models have been proposed in previous
efforts to describe the interface dynamic behaviors in unidirectional freezing
of particle suspensions. However, there are several physical parameters
difficult to be determined in previous investigations dedicated to describing
the spatiotemporal evolution in unidirectional freezing of particle
suspensions. Here, based on the fundamental momentum theorem, we propose a
consistent theoretical framework to address the unidirectional freezing process
in the particle suspensions coupled with the effect of water permeation. An
interface undercooling-dependent pushing force exerted on the compacted layer
with a specific formula is derived based on the surface tension. Then a dynamic
compacted layer is considered and analyzed. Numerical solutions of the
nonlinear models reveal the dependence of system dynamics on some typical
physical parameters, particle radius, initial particle concentration in the
suspensions, freezing velocity and so on. The system dynamics are characterized
by interface velocity, interface undercooling and interface recoil as functions
of time. The models allow us to reconsider the formation mechanism of ice
spears in freezing of particle suspensions in a simpler but novel way, with
potential implications for both understanding and controlling not only ice
formation in porous media but also crystallization processes in other complex
systems
Federated Learning with Reduced Information Leakage and Computation
Federated learning (FL) is a distributed learning paradigm that allows
multiple decentralized clients to collaboratively learn a common model without
sharing local data. Although local data is not exposed directly, privacy
concerns nonetheless exist as clients' sensitive information can be inferred
from intermediate computations. Moreover, such information leakage accumulates
substantially over time as the same data is repeatedly used during the
iterative learning process. As a result, it can be particularly difficult to
balance the privacy-accuracy trade-off when designing privacy-preserving FL
algorithms. In this paper, we introduce Upcycled-FL, a novel federated learning
framework with first-order approximation applied at every even iteration. Under
this framework, half of the FL updates incur no information leakage and require
much less computation. We first conduct the theoretical analysis on the
convergence (rate) of Upcycled-FL, and then apply perturbation mechanisms to
preserve privacy. Experiments on real-world data show that Upcycled-FL
consistently outperforms existing methods over heterogeneous data, and
significantly improves privacy-accuracy trade-off while reducing 48% of the
training time on average
The effect of reading engagement on scientific literacy – an analysis based on the XGBoost method
Scientific literacy is a key factor of personal competitiveness, and reading is the most common activity in daily learning life, and playing the influence of reading on individuals day by day is the most convenient way to improve the level of scientific literacy of all people. Reading engagement is one of the important student characteristics related to reading literacy, which is highly malleable and is jointly reflected by behavioral, cognitive, and affective engagement, and it is of theoretical and practical significance to explore the relationship between reading engagement and scientific literacy using reading engagement as an entry point. In this study, we used PISA2018 data from China to explore the relationship between reading engagement and scientific literacy with a sample of 15-year-old students in mainland China. 36 variables related to reading engagement and background variables (gender, grade, and socioeconomic and cultural status of the family) were selected from the questionnaire as the independent variables, and the score of the Scientific Literacy Assessment (SLA) was taken as the outcome variable, and supervised machine learning method, the XGBoost algorithm, to construct the model. The dataset is randomly divided into training set and test set to optimize the model, which can verify that the obtained model has good fitting degree and generalization ability. Meanwhile, global and local personalized interpretation is done by introducing the SHAP value, a cutting-edge machine model interpretation method. It is found that among the three major components of reading engagement, cognitive engagement is the more influential factor, and students with high reading cognitive engagement level are more likely to get high scores in scientific literacy assessment, which is relatively dominant in the model of this study. On the other hand, this study verifies the feasibility of the current popular machine learning model, i.e., XGBoost, in a large-scale international education assessment program, with a better model adaptability and conditions for global and local interpretation
TENSILE: A Tensor granularity dynamic GPU memory scheduling method towards multiple dynamic workloads system
Recently, deep learning has been an area of intense research. However, as a
kind of computing-intensive task, deep learning highly relies on the scale of
GPU memory, which is usually prohibitive and scarce. Although there are some
extensive works have been proposed for dynamic GPU memory management, they are
hard to be applied to systems with multiple dynamic workloads, such as
in-database machine learning systems.
In this paper, we demonstrated TENSILE, a method of managing GPU memory in
tensor granularity to reduce the GPU memory peak, considering the multiple
dynamic workloads. TENSILE tackled the cold-starting and across-iteration
scheduling problem existing in previous works. We implement TENSILE on a deep
learning framework built by ourselves and evaluated its performance. The
experiment results show that TENSILE can save more GPU memory with less extra
time overhead than prior works in both single and multiple dynamic workloads
scenarios
FairNN- Conjoint Learning of Fair Representations for Fair Decisions
In this paper, we propose FairNN a neural network that performs joint feature
representation and classification for fairness-aware learning. Our approach
optimizes a multi-objective loss function in which (a) learns a fair
representation by suppressing protected attributes (b) maintains the
information content by minimizing a reconstruction loss and (c) allows for
solving a classification task in a fair manner by minimizing the classification
error and respecting the equalized odds-based fairness regularized. Our
experiments on a variety of datasets demonstrate that such a joint approach is
superior to separate treatment of unfairness in representation learning or
supervised learning. Additionally, our regularizers can be adaptively weighted
to balance the different components of the loss function, thus allowing for a
very general framework for conjoint fair representation learning and decision
making.Comment: Code will be availabl
Complex relationship between gut microbiota and thyroid dysfunction: a bidirectional two-sample Mendelian randomization study
BackgroundMany studies have reported the link between gut microbiota and thyroid dysfunction. However, the causal effect of gut microbiota on thyroid dysfunction and the changes in gut microbiota after the onset of thyroid dysfunction are not clear.MethodsA two-sample Mendelian randomization (MR) study was used to explore the complex relationship between gut microbiota and thyroid dysfunction. Data on 211 bacterial taxa were obtained from the MiBioGen consortium, and data on thyroid dysfunction, including hypothyroidism, thyroid-stimulating hormone alteration, thyroxine deficiency, and thyroid peroxidase antibodies positivity, were derived from several databases. Inverse variance weighting (IVW), weighted median, MR-Egger, weighted mode, and simple mode were applied to assess the causal effects of gut microbiota on thyroid dysfunction. Comprehensive sensitivity analyses were followed to validate the robustness of the results. Finally, a reverse MR study was conducted to explore the alteration of gut microbiota after hypothyroidism onset.ResultsOur bidirectional two-sample MR study revealed that the genera Intestinimonas, Eubacterium brachy group, Ruminiclostridium5, and Ruminococcaceae UCG004 were the risk factors for decreased thyroid function, whereas the genera Bifidobacterium and Lachnospiraceae UCG008 and phyla Actinobacteria and Verrucomicrobia were protective. The abundance of eight bacterial taxa varied after the onset of hypothyroidism. Sensitivity analysis showed that no heterogeneity or pleiotropy existed in the results of this study.ConclusionThis novel MR study systematically demonstrated the complex relationship between gut microbiota and thyroid dysfunction, which supports the selection of more targeted probiotics to maintain thyroid–gut axis homeostasis and thus to prevent, control, and reverse the development of thyroid dysfunction
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