115 research outputs found
Drug-Target Interaction Networks Prediction Using Short-linear Motifs
Drug-target interaction (DTI) prediction is a fundamental step in drug discovery and genomic research and contributes to medical treatment. Various computational methods have been developed to find potential DTIs. Machine learning (ML) has been currently used for new DTIs identification from existing DTI networks. There are mainly two ML-based approaches for DTI network prediction: similarity-based methods and feature-based methods. In this thesis, we propose a feature-based approach, and firstly use short-linear motifs (SLiMs) as descriptors of protein. Additionally, chemical substructure fingerprints are used as features of drug. Moreover, another challenge in this field is the lack of negative data for the training set because most data which can be found in public databases is interaction samples. Many researchers regard unknown drug-target pairs as non-interaction, which is incorrect, and may cause serious consequences. To solve this problem, we introduce a strategy to select reliable negative samples according to the features of positive data. We use the same benchmark datasets as previous research in order to compare with them. After trying three classifiers k nearest neighbours (k-NN), Random Forest (RF) and Support Vector Machine (SVM), we find that the results of k-NN are satisfied but not as excellent as RF and SVM. Compared with existing approaches using the same datasets to solve the same problem, our method performs the best under most circumstance
The HI Bias during the Epoch of Reionization
The neutral hydrogen (HI) and its 21 cm line are promising probes to the
reionization process of the intergalactic medium (IGM). To use this probe
effectively, it is imperative to have a good understanding on how the neutral
hydrogen traces the underlying matter distribution. Here we study this problem
using semi-numerical modeling by combining the HI in the IGM and the HI from
halos during the epoch of reionization (EoR), and investigate the evolution and
the scale-dependence of the neutral fraction bias as well as the 21 cm line
bias. We find that the neutral fraction bias on large scales is negative during
reionization, and its absolute value on large scales increases during the early
stage of reionization and then decreases during the late stage. During the late
stage of reionization, there is a transition scale at which the HI bias
transits from negative on large scales to positive on small scales, and this
scale increases as the reionization proceeds to the end.Comment: 11 pages, 11 figures, MNRAS accepte
Development and performance analysis of Si-CaP/fine particulate bone powder combined grafts for bone regeneration
Towards Better Accuracy-efficiency Trade-offs: Divide and Co-training
The width of a neural network matters since increasing the width will
necessarily increase the model capacity. However, the performance of a network
does not improve linearly with the width and soon gets saturated. In this case,
we argue that increasing the number of networks (ensemble) can achieve better
accuracy-efficiency trade-offs than purely increasing the width. To prove it,
one large network is divided into several small ones regarding its parameters
and regularization components. Each of these small networks has a fraction of
the original one's parameters. We then train these small networks together and
make them see various views of the same data to increase their diversity.
During this co-training process, networks can also learn from each other. As a
result, small networks can achieve better ensemble performance than the large
one with few or no extra parameters or FLOPs. Small networks can also achieve
faster inference speed than the large one by concurrent running on different
devices. We validate our argument with 8 different neural architectures on
common benchmarks through extensive experiments. The code is available at
\url{https://github.com/mzhaoshuai/Divide-and-Co-training}
Roles of plant growth substance in callus induction of Achyranthes bidentata
   In this research, callus from leaves, petioles and stems of Achyranthes bidentata was evidently initiated by plant growth substance, in which 2,4-dichlorophenoxyacetic acid (2,4-D) was very important to callus induction, but effects of other plant growth substances were various, and the optimum combination of plant growth substances for callus induction from leaves, petioles and stems was respectively obtained. Compared with callus induction from leaves and petioles, callus induction from stems was easier, and the higher induction rate and bigger mass of callus from stems were obtained. This study showed that the dedifferentiation capacity of various explants from Achyranthes bidentata was obviously different, and effects of plant growth substance on callus induction from various explants of Achyranthes bidentata were significantly diverse
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