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PURSUE YOUR GRADUATE EDUCATION AT ASIA'S TOP UNIVERSITY
Nurturing the next generation of innovative scholars, thought leaders and game
changers, equipped with both deep disciplinary expertise and broad
interdisciplinary perspectives.
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Why NUS: An Overview
OUR ACHIEVEMENTS
01
1ST
In Asia,
QS World University Rankings 2024
3RD
In Asia,
THE Asia University Rankings 2023
8TH
Globally,
QS World University Rankings 2024
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WHY NUS?
02
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04
SERGEY MECHTAEV
Lecturer, University College London
QUANG LOC LE
Research Fellow, University College London
ASEEM PAHUJA
Lecturer, University of Manchester
DING YI
Assistant Professor, University of Warwick
BIN CUI
Professor, Peking University
IEEE Fellow
BENJAMIN GYORI
Associate Professor, Harvard University
LINH THI XUAN PHAN
Associate Professor, University of Penssylvannia
SHWETA SHINDE
Assistant Professor, ETH Zurich
ANURAG ANSHU
Associate Professor, Harvard
XU HENG
Associate Professor, University of Pennsylvannia
MARCEL BÖHME
Faculty, Max Planck Institute for Security and Privacy
SERGEY MECHTAEV
Lecturer, University College London
QUANG LOC LE
Research Fellow, University College London
ASEEM PAHUJA
Lecturer, University of Manchester
DING YI
Assistant Professor, University of Warwick
BIN CUI
Professor, Peking University
IEEE Fellow
BENJAMIN GYORI
Associate Professor, Harvard University
LINH THI XUAN PHAN
Associate Professor, University of Penssylvannia
SHWETA SHINDE
Assistant Professor, ETH Zurich
* Events
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Clear All
7 Feb 2024 Journal Article
COMPUTING PHD STUDENT MOHAMMAD NEAMUL KABIR DEVICES A METHOD TO ACCURATELY
PREDICT PROTEINS AT LOW SEQUENCE HOMOLOGY
Computing
Current protein family modeling methods like profile Hidden Markov Model (pHMM),
k-mer based methods, and deep learning-based methods do not provide very
accurate protein function prediction for proteins in the twilight zone, due to
low sequence similarity to reference proteins with known functions. Results: We
present a novel method EnsembleFam, aiming at better function prediction for
proteins in the twilight zone. EnsembleFam extracts the core characteristics of
a protein family using similarity and dissimilarity features calculated from
sequence homology relations. EnsembleFam trains three separate Support Vector
Machine (SVM) classifiers for each family using these features, and an ensemble
prediction is made to classify novel proteins into these families. Extensive
experiments are conducted using the Clusters of Orthologous Groups (COG) dataset
and G Protein-Coupled Receptor (GPCR) dataset. EnsembleFam not only outperforms
state-of-the-art methods on the overall dataset but also provides a much more
accurate prediction for twilight zone proteins. Conclusions: EnsembleFam, a
machine learning method to model protein families, can be used to better
identify members with very low sequence homology. Using EnsembleFam protein
functions can be predicted using just sequence information with better accuracy
than state-of-the-art methods.
read more
Kabir M.N., Wong L. EnsembleFam: towards more accurate protein family prediction
in the twilight zone. BMC Bioinformatics 23(1), (2022).
10.1186/s12859-022-04626-w
Mohammad Neamul Kabir
Computer Science PhD Student
Computing
Lim Soon Wong
Read Article
27 Dec 2023 Journal Article
FOOD SCIENCE AND TECHNOLOGY PHD STUDENT SU LING SHAN COLLABORATIVELY DEVELOPED
PLANT-BASED CELL CULTURE SCAFFOLD FOR CHEAPER, MORE SUSTAINABLE CULTURED MEAT
Science
Cultivating meat from muscle stem cells in vitro requires 3D edible scaffolds as
the supporting matrix. Electrohydrodynamic (EHD) printing is an emerging
3D-printing technology for fabricating ultrafine fibrous scaffolds with high
precision microstructures for biomedical applications. However, edible
EHD-printed scaffolds remain scarce in cultured meat (CM) production partly due
to special requirements with regard to the printability of ink. Here, hordein or
secalin is mixed, which are cereal prolamins extracted from barley or rye, with
zein to produce pure prolamin-based inks, which exhibit favorable printability
similar to common polycaprolactone ink. Zein/hordein and zein/secalin scaffolds
with highly ordered tessellated structures are successfully fabricated after
optimizing printing conditions. The prolamin scaffolds demonstrated good water
stability and in vitro degradability due to the porous fiber surface, which is
spontaneously generated by culturing muscle cells for 1 week. Moreover, mouse
skeletal myoblasts (C2C12) and porcine skeletal muscle satellite cells (PSCs)
can adhere and proliferate on the fibrous matrix, and a CM slice is produced by
culturing PSCs on prolamin scaffolds with high tissue similarity. The
upregulation of myogenic proteins shows that the differentiation process is
triggered in the 3D culture, demonstrating the great potential of prolamin
scaffolds in CM production.
read more
Su L., Jing L., Zeng X., Chen T., Liu H., Kong Y., Wang X., Yang X., Fu C., Sun
J., Huang D. 3D-Printed Prolamin Scaffolds for Cell-Based Meat Culture. Advanced
Materials 35(2), (2023).
10.1002/adma.202207397
Lingshan Su
Food Science & Technology PhD Student
FoS
Dejian Huang
Read Article
27 Dec 2023 Journal Article
COLLEGE OF DESIGN AND ENGINEERING PHD STUDENT LIM CHENG KAI WORKED WITH
ASSOCIATE PROFESSOR POH CHUEH LOO TO DEVELOP A NOVEL SYSTEM WHICH CAPTURES AND
STORES IMAGES DIRECTLY INTO DNA.
NUS Graduate School
The increasing integration between biological and digital interfaces has led to
heightened interest in utilizing biological materials to store digital data,
with the most promising one involving the storage of data within defined
sequences of DNA that are created by de novo DNA synthesis. However, there is a
lack of methods that can obviate the need for de novo DNA synthesis, which tends
to be costly and inefficient. Here, in this work, we detail a method of
capturing 2-dimensional light patterns into DNA, by utilizing optogenetic
circuits to record light exposure into DNA, encoding spatial locations with
barcoding, and retrieving stored images via high-throughput next-generation
sequencing. We demonstrate the encoding of multiple images into DNA, totaling
1152 bits, selective image retrieval, as well as robustness to drying, heat and
UV. We also demonstrate successful multiplexing using multiple wavelengths of
light, capturing 2 different images simultaneously using red and blue light.
This work thus establishes a ‘living digital camera’, paving the way towards
integrating biological systems with digital devices.
read more
Lim C.K., Yeoh J.W., Kunartama A.A., Yew W.S., Poh C.L. A biological camera that
captures and stores images directly into DNA. Nature Communications 14(1),
(2023).
10.1038/s41467-023-38876-w
Cheng Kai Lim
NUSGS Deanâs Office PhD Student
NUSGS
Chueh Loo Poh, AP Wen Shan Yew
Read Article
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* 21 Lower Kent Ridge Road
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