994 research outputs found
GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization
Bioinformatics tools have been developed to interpret gene expression data at
the gene set level, and these gene set based analyses improve the biologists'
capability to discover functional relevance of their experiment design. While
elucidating gene set individually, inter gene sets association is rarely taken
into consideration. Deep learning, an emerging machine learning technique in
computational biology, can be used to generate an unbiased combination of gene
set, and to determine the biological relevance and analysis consistency of
these combining gene sets by leveraging large genomic data sets. In this study,
we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model
with the incorporation of a priori defined gene sets that retain the crucial
biological features in the latent layer. We introduced the concept of the gene
superset, an unbiased combination of gene sets with weights trained by the
autoencoder, where each node in the latent layer is a superset. Trained with
genomic data from TCGA and evaluated with their accompanying clinical
parameters, we showed gene supersets' ability of discriminating tumor subtypes
and their prognostic capability. We further demonstrated the biological
relevance of the top component gene sets in the significant supersets. Using
autoencoder model and gene superset at its latent layer, we demonstrated that
gene supersets retain sufficient biological information with respect to tumor
subtypes and clinical prognostic significance. Superset also provides high
reproducibility on survival analysis and accurate prediction for cancer
subtypes.Comment: Presented in the International Conference on Intelligent Biology and
Medicine (ICIBM 2018) at Los Angeles, CA, USA and published in BMC Systems
Biology 2018, 12(Suppl 8):14
Phantom dark ghost in Einstein–Cartan gravity
A class of dynamical dark energy models is constructed through an extended version of fermion fields corresponding to phantom dark ghost spinors, which are spin 1/2 with mass dimension 1. We find that if these spinors interact with torsion fields in a homogeneous and isotropic universe, then it does not imply any future dark energy singularity or any abrupt event, though the fermion has a negative kinetic energy. In fact, the equation of state of this dark energy model will asymptotically approach the value w = -1 from above without crossing the phantom divide and inducing therefore a de Sitter state. Consequently, we expect the model to be stable because no real phantom fields will be created. At late time, the torsion fields will vanish as the corresponding phantom dark ghost spinors dilute. As would be expected, intuitively, this result is unaffected by the presence of cold dark matter although the proof is not as straightforward as in general relativity
Predicting drug response of tumors from integrated genomic profiles by deep neural networks
The study of high-throughput genomic profiles from a pharmacogenomics
viewpoint has provided unprecedented insights into the oncogenic features
modulating drug response. A recent screening of ~1,000 cancer cell lines to a
collection of anti-cancer drugs illuminated the link between genotypes and
vulnerability. However, due to essential differences between cell lines and
tumors, the translation into predicting drug response in tumors remains
challenging. Here we proposed a DNN model to predict drug response based on
mutation and expression profiles of a cancer cell or a tumor. The model
contains a mutation and an expression encoders pre-trained using a large
pan-cancer dataset to abstract core representations of high-dimension data,
followed by a drug response predictor network. Given a pair of mutation and
expression profiles, the model predicts IC50 values of 265 drugs. We trained
and tested the model on a dataset of 622 cancer cell lines and achieved an
overall prediction performance of mean squared error at 1.96 (log-scale IC50
values). The performance was superior in prediction error or stability than two
classical methods and four analog DNNs of our model. We then applied the model
to predict drug response of 9,059 tumors of 33 cancer types. The model
predicted both known, including EGFR inhibitors in non-small cell lung cancer
and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive
analysis further revealed the molecular mechanisms underlying the resistance to
a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer
potential of a novel agent, CX-5461, in treating gliomas and hematopoietic
malignancies. Overall, our model and findings improve the prediction of drug
response and the identification of novel therapeutic options.Comment: Accepted for presentation in the International Conference on
Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA.
Currently under consideration for publication in a Supplement Issue of BMC
Genomic
Block Based Imperial Competitive Algorithm with Greedy Search for Traveling Salesman Problem
Imperial competitive algorithm (ICA) simulates a multi-agent algorithm. Each agent is like a kingdom has its country, and the strongest country in each agent is called imperialist, others are colony. Countries are competitive with imperialist which in the same kingdom by evolving. So this country will move in the search space to find better solutions with higher fitness to be a new imperialist. The main idea in this paper is using the peculiarity of ICA to explore the search space to solve the kinds of combinational problems. Otherwise, we also study to use the greed search to increase the local search ability. To verify the proposed algorithm in this paper, the experimental results of traveling salesman problem (TSP) is according to the traveling salesman problem library (TSPLIB). The results show that the proposed algorithm has higher performance than the other known methods
Block Based Imperial Competitive Algorithm with Greedy Search for Traveling Salesman Problem
Imperial competitive algorithm (ICA) simulates a multi-agent algorithm. Each agent is like a kingdom has its country, and the strongest country in each agent is called imperialist, others are colony. Countries are competitive with imperialist which in the same kingdom by evolving. So this country will move in the search space to find better solutions with higher fitness to be a new imperialist. The main idea in this paper is using the peculiarity of ICA to explore the search space to solve the kinds of combinational problems. Otherwise, we also study to use the greed search to increase the local search ability. To verify the proposed algorithm in this paper, the experimental results of traveling salesman problem (TSP) is according to the traveling salesman problem library (TSPLIB). The results show that the proposed algorithm has higher performance than the other known methods
Block Based Imperial Competitive Algorithm with Greedy Search for Traveling Salesman Problem
Imperial competitive algorithm (ICA) simulates a multi-agent algorithm. Each agent is like a kingdom has its country, and the strongest country in each agent is called imperialist, others are colony. Countries are competitive with imperialist which in the same kingdom by evolving. So this country will move in the search space to find better solutions with higher fitness to be a new imperialist. The main idea in this paper is using the peculiarity of ICA to explore the search space to solve the kinds of combinational problems. Otherwise, we also study to use the greed search to increase the local search ability. To verify the proposed algorithm in this paper, the experimental results of traveling salesman problem (TSP) is according to the traveling salesman problem library (TSPLIB). The results show that the proposed algorithm has higher performance than the other known methods
- …