1,737 research outputs found
MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature
<p>Abstract</p> <p>Background</p> <p>DNA methylation is regarded as a potential biomarker in the diagnosis and treatment of cancer. The relations between aberrant gene methylation and cancer development have been identified by a number of recent scientific studies. In a previous work, we used co-occurrences to mine those associations and compiled the MeInfoText 1.0 database. To reduce the amount of manual curation and improve the accuracy of relation extraction, we have now developed MeInfoText 2.0, which uses a machine learning-based approach to extract gene methylation-cancer relations.</p> <p>Description</p> <p>Two maximum entropy models are trained to predict if aberrant gene methylation is related to any type of cancer mentioned in the literature. After evaluation based on 10-fold cross-validation, the average precision/recall rates of the two models are 94.7/90.1 and 91.8/90% respectively. MeInfoText 2.0 provides the gene methylation profiles of different types of human cancer. The extracted relations with maximum probability, evidence sentences, and specific gene information are also retrievable. The database is available at <url>http://bws.iis.sinica.edu.tw:8081/MeInfoText2/</url>.</p> <p>Conclusion</p> <p>The previous version, MeInfoText, was developed by using association rules, whereas MeInfoText 2.0 is based on a new framework that combines machine learning, dictionary lookup and pattern matching for epigenetics information extraction. The results of experiments show that MeInfoText 2.0 outperforms existing tools in many respects. To the best of our knowledge, this is the first study that uses a hybrid approach to extract gene methylation-cancer relations. It is also the first attempt to develop a gene methylation and cancer relation corpus.</p
Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumes
Atrial Fibrillation (AF) is characterized by rapid, irregular heartbeats, and
can lead to fatal complications such as heart failure. The disease is divided
into two sub-types based on severity, which can be automatically classified
through CT volumes for disease screening of severe cases. However, existing
classification approaches rely on generic radiomic features that may not be
optimal for the task, whilst deep learning methods tend to over-fit to the
high-dimensional volume inputs. In this work, we propose a novel
radiomics-informed deep-learning method, RIDL, that combines the advantages of
deep learning and radiomic approaches to improve AF sub-type classification.
Unlike existing hybrid techniques that mostly rely on na\"ive feature
concatenation, we observe that radiomic feature selection methods can serve as
an information prior, and propose supplementing low-level deep neural network
(DNN) features with locally computed radiomic features. This reduces DNN
over-fitting and allows local variations between radiomic features to be better
captured. Furthermore, we ensure complementary information is learned by deep
and radiomic features by designing a novel feature de-correlation loss.
Combined, our method addresses the limitations of deep learning and radiomic
approaches and outperforms state-of-the-art radiomic, deep learning, and hybrid
approaches, achieving 86.9% AUC for the AF sub-type classification task. Code
is available at https://github.com/xmed-lab/RIDL.Comment: Accepted by MICCAI2
Identification of key bioactive anti-migraine constituents of Asari radix et rhizoma using network pharmacology and nitroglycerin-induced migraine rat model
Purpose: To elucidate the bioactive constituents of Asari radix et rhizoma (ARR) in treating migraine based on network pharmacology and nitroglycerin-induced migraine rat model.
Methods: The potential bioactive constituents of ARR were identified with the aid of literature retrieval and virtual screening, and the migraine-related hub genes were identified using protein-protein interaction and topology analyses. Then, the interaction between the potential bioactive constituents and hub genes was determined with molecular docking and topology, leading to the prediction of the anti-migraine constituents of ARR. Moreover, a rat model of nitroglycerin-induced migraine was used to confirm the prediction by measuring the frequency of head-scratching and head-shaking behavior (FHHB) in the rats. In addition, levels of nitric oxide (NO) and calcitonin gene-related peptide (CGRP) in blood, norepinephrine (NE) and 5-hydroxytryptamine (5-HT) in brain were measured using appropriate commercial kits.
Results: Network pharmacology revealed that naringenin-7-O-β-D-glucopyranoside and higenamine might be the key anti-migraine bioactive constituents of ARR. On addition of naringenin-7-O-β-D- glucopyranoside or higenamine to ARR, there was marked enhancement of the mitigating effect of ARR on nitroglycerin-induced abnormalities in levels of NO, CGRP, 5-HT and NE, as well as FHHB in rats (p < 0.05 or 0.01).
Conclusion: These findings indicate that naringenin-7-O-β-D-glucopyranoside and higenamine might be the key bioactive and anti-migraine constituents of ARR. However, in addition to naringenin-7-O-β-D- glucopyranoside and higenamine, there were many other anti-migraine constituents in ARR. Therefore, there is need for further investigations on the actual contributions of these two constituents of ARR in treating migraine
(E)-3-Methyl-5-(4-methylphenoxy)-1-phenyl-1H-pyrazole-4-carbaldehyde O-[(2-chloro-1,3-thiazol-5-yl)methyl]oxime
In the title compound, C22H19ClN4O2S, the planes of the benzene ring, the substituted phenyl ring and the thiazole ring make dihedral angles of 18.4 (3), 88.9 (2) and 63.0 (3)°, respectively, with the pyrazole ring
Citrullus Ianatus Extract Reverses Oxidative and Haematological Dysfuntion in Carbon Tetrachloride Induced Liver Damaged Rats
Anticancer drug-loaded multifunctional nanoparticles to enhance the chemotherapeutic efficacy in lung cancer metastasis
Combined Scaling for Open-Vocabulary Image Classification
We present a combined scaling method - named BASIC - that achieves 85.7%
top-1 accuracy on the ImageNet ILSVRC-2012 validation set without learning from
any labeled ImageNet example. This accuracy surpasses best published similar
models - CLIP and ALIGN - by 9.3%. Our BASIC model also shows significant
improvements in robustness benchmarks. For instance, on 5 test sets with
natural distribution shifts such as ImageNet-{A,R,V2,Sketch} and ObjectNet, our
model achieves 84.3% top-1 average accuracy, only a small drop from its
original ImageNet accuracy.
To achieve these results, we scale up the contrastive learning framework of
CLIP and ALIGN in three dimensions: data size, model size, and batch size. Our
dataset has 6.6B noisy image-text pairs, which is 4x larger than ALIGN, and 16x
larger than CLIP. Our largest model has 3B weights, which is 3.75x larger in
parameters and 8x larger in FLOPs than ALIGN and CLIP. Finally, our batch size
is 65536 which is 2x more than CLIP and 4x more than ALIGN.
We encountered two main challenges with the scaling rules of BASIC. First,
the main challenge with implementing the combined scaling rules of BASIC is the
limited memory of accelerators, such as GPUs and TPUs. To overcome the memory
limit, we propose two simple methods which make use of gradient checkpointing
and model parallelism. Second, while increasing the dataset size and the model
size has been the defacto method to improve the performance of deep learning
models like BASIC, the effect of a large contrastive batch size on such
contrastive-trained image-text models is not well-understood. To shed light on
the benefits of large contrastive batch sizes, we develop a theoretical
framework which shows that larger contrastive batch sizes lead to smaller
generalization gaps for image-text models such as BASIC
InGaN-based light-emitting diodes with an embedded conical air-voids structure
The conical air-void structure of an InGaN light-emitting diode (LEDs) was formed at the GaN/sapphire interface to increase the light extraction efficiency. The fabrication process of the conical air-void structure consisted of a dry process and a crystallographic wet etching process on an undoped GaN layer, followed by a re-growth process for the InGaN LED structure. A higher light output power (1.54 times) and a small divergent angle (120o) were observed, at a 20mA operation current, on the treated LED structure when compared to a standard LED without the conical air-void structure. In this electroluminescence spectrum, the emission intensity and the peak wavelength varied periodically by corresponding to the conical air-void patterns that were measured through a 100nm-optical-aperture fiber probe. The conical air-void structure reduced the compressed strain at the GaN/sapphire interface by inducing the wavelength blueshift phenomenon and the higher internal quantum efficiency of the photoluminescence spectra for the treated LED structure
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