The University of Tokyo · Engineering
Professor Yaozhong Zhang's research lab specializes in computational biology and bioinformatics, focusing on developing advanced machine learning and deep learning methods for genomic data analysis. Key research directions include nanopore sequencing basecalling and methylation detection using transformer-based models, phage-host interaction prediction through contrastive learning and sequence embedding, and the application of neural networks to model complex biological processes such as epithelial-mesenchymal transition in cancer. The lab emphasizes innovative algorithm design for high-accuracy, efficient biological sequence analysis.
Figures are computed from collected data and may differ slightly.
EC: Endometrial cancer; 3'-UTR: 3'-untranslated regions; TPX2: target protein for Xenopus kinesin-like protein 2; TCGA: the Cancer Genome Atlas; UCEC: uterine corpus endometrial carcinoma; CCK-8: cell counting kit-8; OD: optical density; FCM: flow cytometry; EMT: epithelial-mesenchymal transition.
Our results show that formulating the basecalling problem as a one-dimensional segmentation task is a promising approach, which does basecalling and segmentation jointly.
Supplementary data are available at Bioinformatics online.
RNNs provides an alternative and flexible way to calculate sequence-specific bias without explicitly pre-determining sequence structures.
DNA methylation is a common nucleotide modification, which is associated with various biological processes, such as gene expression and aging. Nanopore sequencing provides a direct detecting approach through searching specific current signal shifts. Recently, model-based approaches, especially those using deep learning models, have achieved significant performance improvements on nanopore methylation detection. In this work, we explore using the non-recurrent neural network structure of Bidirect
Accurately identifying phage-host relationships from their genome sequences is still challenging, especially for those phages and hosts with less homologous sequences. In this work, focusing on identifying the phage-host relationships at the species and genus level, we propose a contrastive learning based approach to learn whole-genome sequence embeddings that can take account of phage-host interactions (PHIs). Contrastive learning is used to make phages infecting the same hosts close to each ot
This work details a polyolefin-elastomer-based binder system to prepare fused filament fabrication (FFF) filaments and print cores for coils for electrical engines. The processability, homogeneity, and thermal properties of the polyolefin-elastomer-based filaments are explored. A two-step debinding and sintering process was established for manufacturing dense iron parts. Results indicate the developed filaments possess superior printing and sintering (at 900°C) performance, yielding only 20% wei
The wide utilization of lithium-ion batteries (LIBs) prompts extensive research on the anode materials with large capacity and excellent stability. Despite the attractive electrochemical properties of pure Si anodes outperforming other Si-based materials, its unsafety caused by huge volumetric expansion is commonly admitted. Silicon monoxide (SiO) anode is advantageous in mild volume fluctuation, and would be a proper alternative if the low initial columbic efficiency and conductivity can be ame
Supertagging is a widely used speed-up technique for deep parsing. In another aspect, supertagging has been exploited in other NLP tasks than parsing for utilizing the rich syntactic information given by the supertags. However, the performance of supertagger is still a bottleneck for such applications. In this paper, we investigated the relationship between supertagging and parsing, not just to speed up the deep parser; We started from a sequence labeling view of HPSG supertagging, examining how
The source code and associated data can be accessed at https://github.com/yaozhong/bert_investigation.
Dry film photoresists (DF PRs) are widely used to perform photolithography on non-traditional substrates such as printing circuit boards, plastic sheets, or non-planar surfaces. Commercially available DF PRs are usually in a negative tone and rather thick, limiting lithographic resolution and versatility. The relatively large pressure required for lamination also prevents the technology from being used for delicate substrates. Here we present a modified soft-lithographic process, namely photores
In a supertagging task, sequence labeling models are commonly used. But their limited ability to model long-distance information presents a bottleneck to make further improvements. In this paper, we modeled this long-distance information in dependency formalism and integrated it into the process of HPSG supertagging. The experiments showed that the dependency information is very informative for supertag disambiguation. We also evaluated the improved supertagger in the HPSG parser. 1
Congenital heart disease (CHD) has become the leading mortal cause for an infant from congenital abnormalities. Pulmonary artery hypertension (PAH) is one of the complications of CHD.[1] It has also been reported that upregulation of microRNA-98 (miR-98) could mediate the suppression of cardiac hypertrophy, which implies that miR-98 might play an important role in CHD.[2] In this study, the clinical data from hospitalized patients were retrospectively analyzed to identify if there is any evidenc
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