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1.
In the process of drug discovery and disease treatment, drug repositioning is broadly studied to identify biological targets for existing drugs. Many methods have been proposed for drug–target interaction prediction by taking into account different kinds of data sources. However, most of the existing methods only use one side information for drugs or targets to predict new targets for drugs. Some recent works have improved the prediction accuracy by jointly considering multiple representations of drugs and targets. In this work, the authors propose a drug–target prediction approach by matrix completion with multi‐view side information (MCM) of drugs and proteins from both structural view and chemical view. Different from existing studies for drug–target prediction, they predict drug–target interaction by directly completing the interaction matrix between them. The experimental results show that the MCM method could obtain significantly higher accuracies than the comparison methods. They finally report new drug–target interactions for 26 FDA‐approved drugs, and biologically discuss these targets using existing references.Inspec keywords: proteins, diseases, medical computing, drugs, genetics, molecular biophysicsOther keywords: drug–target interaction prediction, prediction accuracy, matrix completion, multiview side information, structural view, chemical view, drug repositioning, drug discovery, biological targets, FDA‐approved drugs
Nomenclature
- known drug–target interaction matrix
- complete low‐rank matrix in the structural view
- complete low‐rank matrix in the chemical view
- drug–drug similarity matrix in the structural view
- target–target similarity matrix in the structural view
- drug–drug similarity matrix in the chemical view
- target–target similarity matrix in the chemical view
- drugs feature matrix in the structural view
- protein targets feature matrix in the structural view
- drugs feature matrix in the chemical view
- protein targets feature matrix in the chemical view
- the common complete drug–target interaction matrix
- any given matrix
- inner product for matrices
- gradient operator
- ,
- trade‐off parameters
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Songlin Ran 《Materials Letters》2009,63(1):94-96
Ti1−zNbzN ceramics were fabricated by sintering nanocrystalline titanium-niobium oxynitride (Ti1−zNbzOxNy) powders using spark plasma sintering (SPS) technique at 1060 °C for 3 min in an N2 atmosphere. The phase composition and microstructure were characterized by XRD, SEM, TEM and EDS. The results showed that Ti1−zNbzN ceramics remained the cubic structures of Ti1−zNbzOxNy powders. There were XRD peak shifts in the cubic phases between Ti1−zNbzN ceramics and corresponding Ti1−zNbzOxNy powders. During the sintering process, oxygen separated from Ti1−zNbzOxNy to form titanium-niobium oxides. Ti1−zNbzN (0 < z < 1) had a more compact structure than TiOxNy and NbOxNy. Ti0.5Nb0.5N ceramic had the biggest grain size in the series of Ti1−zNbzN. 相似文献
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针对传统的基于数据驱动的机械故障模式识别方法中需要人工构造算法提取特征以及人工构造特征提取算法繁琐的问题,结合卷积神经网络(CNN)在图像特征自动提取与图像分类识别中的广泛应用,提出了一种基于CNN图像分类的轴承故障模式识别方法。首先,利用集合经验模态分解(EEMD)方法对轴承振动信号进行自适应分解并用相关系数对得到的本征模函数分量进行筛选。其次,对筛选得到的本征模函数分量进行伪魏格纳-威利时频分析(PWVD)计算得到信号的时频分布图,并对时频图进行预处理。最后,将轴承15种不同工况预处理后的时频图利用CNN进行特征提取与分类识别。将该方法与同类方法进行了对比,分类正确率提高了4.26%。 相似文献
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Jun LiangWen-Zhong Lu Jia-Min WuJian-Guo Guan 《Materials Science and Engineering: B》2011,176(2):99-102
Li2TiO3 ceramics were prepared at the sintering temperatures from 1050 to 1250 °C. The optimal microwave dielectric properties were ?r = 23.29, Q × f = 15,525 GHz (5.9 GHz), and τf = 35.05 ppm/ °C for the sample sintered at 1200 °C. The microwave dielectric properties were improved obviously when the Li2TiO3 ceramics were sintered at low temperatures with small additions of H3BO3 (B2O3 in the form of H3BO3). Only monoclinic Li2TiO3 was found in the pure or H3BO3-doped Li2TiO3 ceramics. About 1.0 wt.% H3BO3 addition aided the sintering of Li2TiO3 ceramics effectively while excessive H3BO3 (≥2.5 wt.%) was not favorable. Typically the best microwave dielectric properties were ?r = 23.28, Q × f = 37,110 GHz (6.3 GHz), and τf = 30.43 ppm/ °C for the 1.0 wt.% H3BO3-doped Li2TiO3 ceramic sintered at 920 for 3 h, which is promising for LTCC applications. 相似文献
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The effect of preloading on crack nucleation time was examined with compact tension specimens having various notch radius in 0.1N-H2SO4 aqueous solution for 200°C tempered AISI 4340 steel. Crack nucleation time tn increases by preloading for a given apparent stress intensity factor Kp2. The curve K?2 vs. tn deviates upward from the curve for the non preloading case. A linear relationship between the crack nucleation time and parameter is seen in semi-log diagram, where is taken as the value at tn=α due to preloading. The apparent threshold stress intensity factor increases with K?2 which is the apparent stress intensity factor of preloading. A detached crack is nucleated at some distance from the notch root and extends in a form of circle. This distance increases with increasing K?2. The effect of load reduction during crack growth was examined. When the K-value was reduced from K1 to K2, an incubation time was observed before the crack started growing under the K2-value. The incubation time tm tends to increase with increasing ΔK = K1-K2. The threshold stress intensity factor was also found to increase for high load reduction.In order to explain these experimental results, a new dislocation model is proposed on the basis of stress induced diffusion of hydrogen in high stress region ahead of the notch root or a crack. This model suggests that the change in the crack nucleation time and the increase of the incubation time due to preloading or load reduction are caused by reducing the hydrostatic pressure and by spreading the hydrogen saturated region which requires more time for the hydrogen accumulation due to preloading or load reduction. The theory predicts the experimentally observed relations between and tn and between log tin and ΔK. 相似文献
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H1−xLaNb2−xMoxO7 was prepared by solid-state reaction followed by an ion-exchange reaction. Pt was incorporated in the interlayer of H1−xLaNb2−xMoxO7 by the stepwise intercalation reaction. The H1−xLaNb2−xMoxO7 showed hydrogen production activity and the activities were greatly enhanced by Pt co-incorporating. The x value in H1−xLaNb2−xMoxO7 had an important effect on the photocatalytic activity of the catalyst. When the x = 0.05, the H1−xLaNb2−xMoxO7/Pt showed a photocatalytic activity of 80 cm3 h−1 g−1 hydrogen evolution rate in 10 vol.% methanol solution under irradiation from a 100 W mercury lamp at 333 K. 相似文献
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《Materials Letters》2006,60(17-18):2211-2213
The dielectric properties of (Ba1−xCax)1−1.5yBiyTiO3 (x = 0.10, 0.20 and 0.30, y = 0.05) ceramics were investigated. XRD analysis shows that 5 at.% of Bi doping can be fully incorporated into the perovskite lattice of (Ba1−xCax)TiO3. The maximal dielectric constant Km of (Ba1−xCax)1−1.5yBiyTiO3 ceramics decreases significantly with increasing x for all the compositions. Compared with undoped Ba1−xCaxTiO3 ceramics [Mater. Chem. Phys. 77 (2002)], Bi doping remarkably shifts the temperature of the peak dielectric constants Tm to lower temperature and a broad dielectric peak exhibits strong frequency dispersion. With increasing frequency, Km decreases and Tm shifts to higher temperatures in (Ba1−xCax)1−1.5yBiyTiO3 ceramics. A typical behavior to well-known relaxor ferroelectric is observed. The relaxation behavior observation is suggested due to a random electric field induced domain state. 相似文献
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The literature on the spread and vaporisation of cryogenic liquids on water is reviewed and a new model proposed. The model incorporates the features o
Symbols | |
Cp | Heat capacity. |
g | Acceleration due to gravity. |
Gm | Molar vaporisation rate. |
h | Latent heat with two suffices denoting phase change. |
h | Pool thickness (no suffices). |
i | Enthalpy. |
k,K | Constants. |
KA,KB | Functions of pool temperature and composition. |
kw | Thermal diffusivity of water. |
l | Depth in water where temperature is constant. |
L | Length scale of an oil spill. |
Lm | Molar liquid spill rate. |
Lwi | Latent heat of fusion of ice. |
M | Pool mass, M0 initially. |
Ms,Mv | Mass spilled, mass vaporised. |
q | Heat flux to cryogenic liquid. |
r | Radial co-ordinate |
R,R* | Radius of pool. |
t | Time. |
T | Temperature. |
U | Velocity. |
Wm | Number of moles of material in the pool. |
xA | Molar methane portion of liquid pool (xB, ethane). |
xAS | Molar methane portion of spilling liquid. |
yA | Molar methane portion of vapour (yB, ethane). |
z | Vertical coordinate. |
δ | Vapour layer thickness. |
ΔT | Temperature difference. |
? | Ice layer thickness. |
θ | Temperature relative to freezing point of water. |
λ | Effective thermal conductivity. |
μ | Viscosity. |
? | Density. |
σ | Surface tension. |
ø | Heat flux to liquid pool = q. |
Suffices | |
A | Average (or methane fraction with x,y). |
B | Boiling point of cryogen (or ethane fraction with x,y). |
fc | Film collapse. |
fg | Liquid to vapour phase change. |
F | Freezing of water. |
i | Ice. |
l, L | Liquid. |
lv | Liquid to vapour phase change. |
LE | Leading edge. |
m | Molar. |
O | Initial |
o | Oil. |
p | Pool. |
s | Spilling liquid. |
v | Vapour. |
w | Water. |