Biology subjects

Kwoh, C. K.

Publications and source records attributed to Kwoh, C. K..

5 recordsLinked to original sources

bioRxiv2021

Graph Contextualized Attention Network for Predicting Synthetic Lethality in Human Cancers

MotivationSynthetic Lethality (SL) plays an increasingly critical role in the targeted anticancer therapeutics. In addition, identifying SL interactions can create opportunities to selectively kill cancer cells without harming normal cells. Given the high cost of wet-lab experiments, in silico prediction of SL interactions as an alternative can be a rapid and cost-effective way to guide the experimental screening of candidate SL pairs. Several matrix factorization-based methods have recently been proposed for human SL prediction. However, they are limited in capturing the dependencies of neighbors. In addition, it is also highly challenging to make accurate predictions for new genes without any known SL partners. ResultsIn this work, we propose a novel graph contextualized attention network named GCATSL to learn gene representations for SL prediction. First, we leverage different data sources to construct multiple feature graphs for genes, which serve as the feature inputs for our GCATSL method. Second, for each feature graph, we design node-level attention mechanism to effectively capture the importance of local and global neighbors and learn local and global representations for the nodes, respectively. We further exploit multi-layer perceptron (MLP) to aggregate the original features with the local and global representations and then derive the feature-specific representations. Third, to derive the final representations, we design feature-level attention to integrate feature-specific representations by taking the importance of different feature graphs into account. Extensive experimental results on three datasets under different settings demonstrate that our GCATSL model outperforms 14 state-of-the-art methods consistently. In addition, case studies further validate the effectiveness of our proposed model in identifying novel SL pairs. AvailabilityPython codes and dataset are available at: Contactluojiawei@hnu.edu.cn and xlli@i2r.a-star.edu.sg

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bioRxiv2020

Chromatin Interaction Neural Network (ChINN): A machine learning-based method for predicting chromatin interactions from DNA sequences

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. Various computational methods have been developed to predict chromatin interactions. Most of these methods rely on large collections of ChIP-Seq/RNA-Seq/DNase-Seq datasets and predict only enhancer-promoter interactions. Some of the state-of-the-art methods have poor experimental designs, leading to over-exaggerated performances and misleading conclusions. Here we developed a computational method, Chromatin Interaction Neural Network (ChINN), to predict chromatin interactions between open chromatin regions by using only DNA sequences of the interacting open chromatin regions. ChINN is able to predict CTCF-, RNA polymerase II- and HiC-associated chromatin interactions between open chromatin regions. ChINN also shows good across-sample performances and captures various sequence features that are predictive of chromatin interactions. To apply our results to clinical patient data, we applied CHINN to predict chromatin interactions in 6 chronic lymphocytic leukemia (CLL) patient samples and a cohort of open chromatin data from 84 CLL samples that was previously published. Our results demonstrated extensive heterogeneity in chromatin interactions in patient samples, and one of the sources of this heterogeneity were the different subtypes of CLL.

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bioRxiv2020

VirPreNet: A weighted ensemble convolutional neural network for the virulence prediction of influenza A virus using all 8 segments

MotivationInfluenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics. The evolution of influenza viruses remains to be the main obstacle in the effectiveness of antiviral treatments due to rapid mutations. Previous work has been investigated to reveal the determinants of virulence of the influenza A virus. To further facilitate flu surveillance, explicit detection of influenza virulence is crucial to protect public health from potential future pandemics. ResultsIn this paper, we propose a weighted ensemble convolutional neural network for the virulence prediction of influenza A viruses named VirPreNet that uses all 8 segments. Firstly, mouse lethal dose 50 is exerted to label the virulence of infections into two classes, namely avirulent and virulent. A numerical representation of amino acids named ProtVec is applied to the 8-segments in a distributed manner to encode the biological sequences. After splittings and embeddings of influenza strains, the ensemble convolutional neural network is constructed as the base model on the influenza dataset of each segment, which serves as the VirPreNets main part. Followed by a linear layer, the initial predictive outcomes are integrated and assigned with different weights for the final prediction. The experimental results on the collected influenza dataset indicate that VirPreNet achieves state-of-the-art performance combining ProtVec with our proposed architecture. It outperforms baseline methods on the independent testing data. Moreover, our proposed model reveals the importance of PB2 and HA segments on the virulence prediction. We believe that our model may provide new insights into the investigation of influenza virulence. Contactyinr0002@e.ntu.edu.sg Availability and ImplementationCodes and data to generate the VirPreNet are publicly available at https://github.com/Rayin-saber/VirPreNet

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bioRxiv2020

Alignment-free machine learning approaches for the lethality prediction of potential novel human-adapted coronavirus using genomic nucleotide

A newly emerging novel coronavirus appeared and rapidly spread worldwide and World Health Organization declared a pandemic on March 11, 2020. The roles and characteristics of coronavirus have captured much attention due to its power of causing a wide variety of infectious diseases, from mild to severe on humans. The detection of the lethality of human coronavirus is key to estimate the viral toxicity and provide perspective for treatment. We developed alignment-free machine learning approaches for an ultra-fast and highly accurate prediction of the lethality of potential human-adapted coronavirus using genomic nucleotide. We performed extensive experiments through six different feature transformation and machine learning algorithms in combination with digital signal processing to infer the lethality of possible future novel coronaviruses using previous existing strains. The results tested on SARS-CoV, MERS-Cov and SARS-CoV-2 datasets show an average 96.7% prediction accuracy. We also provide preliminary analysis validating the effectiveness of our models through other human coronaviruses. Our study achieves high levels of prediction performance based on raw RNA sequences alone without genome annotations and specialized biological knowledge. The results demonstrate that, for any novel human coronavirus strains, this alignment-free machine learning-based approach can offer a reliable real-time estimation for its viral lethality.

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bioRxiv2020

IAV-CNN: a 2D convolutional neural network model to predict antigenic variants of influenza A virus

The rapid evolution of influenza viruses constantly leads to the emergence of novel influenza strains that are capable of escaping from population immunity. The timely determination of antigenic variants is critical to vaccine design. Empirical experimental methods like hemagglutination inhibition (HI) assays are time-consuming and labor-intensive, requiring live viruses. Recently, many computational models have been developed to predict the antigenic variants without considerations of explicitly modeling the interdependencies between the channels of feature maps. Moreover, the influenza sequences consisting of similar distribution of residues will have high degrees of similarity and will affect the prediction outcome. Consequently, it is challenging but vital to determine the importance of different residue sites and enhance the predictive performance of influenza antigenicity. We have proposed a 2D convolutional neural network (CNN) model to infer influenza antigenic variants (IAV-CNN). Specifically, we introduce a new distributed representation of amino acids, named ProtVec that can be applied to a variety of downstream proteomic machine learning tasks. After splittings and embeddings of influenza strains, a 2D squeeze-and-excitation CNN architecture is constructed that enables networks to focus on informative residue features by fusing both spatial and channel-wise information with local receptive fields at each layer. Experimental results on three influenza datasets show IAV-CNN achieves state-of-the-art performance combing the new distributed representation with our proposed architecture. It outperforms both traditional machine algorithms with the same feature representations and the majority of existing models in the independent test data. Therefore we believe that our model can be served as a reliable and robust tool for the prediction of antigenic variants.

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