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Machine
learning for biomedicine Computational
biology
We aim to unlock the mystery of life
hidden under our genome, cells and organisms.
Areas: Structural biology
| Genomics | Drug design | Data efficiency
Healthcare
Modern
hospitals and medical centres have
collected huge amount of clinical data for hundreds of millions of
patients over the past decades. However, how to make the best out of
the data for improving clinincal services remains the major
question. This research aims at characterising the data using
statistical models and applying the state-of-the-art machine learning
techniques for representation, clustering and prediction both at the
patient and the cohort levels.
Areas: ICU | Mental health | Preterm birth | Population health
| EMR | EEG | Medical imaging | Patient flow
Patents
Talks/Tutorials
- Machine learning and reasoning for drug discovery @ECML-PKDD, Sept 2021.
- AI in
Covid-19 pandemic, @VANJ Webminar,
May 2020.
- Modern
AI for drug discovery, VietAI
Summit, Nov 2019.
- Lecture on Deep
learning for biomedicine, Southeast
Asia Machine Learning (SEA ML) School, Depok, Greater Jakarta,
Indonesia, July 2019.
- Deep
learning for genomics: Present and future, Genomic Medicine 2019, Hanoi,
Vietnam, June 2019.
- Deep
learning for biomedicine: Genomics and Drug design, Institute of Big Data, Hanoi,
Vietnam, Jan 2019.
- Deep
learning for biomedical discovery and data mining, Tutorial at PAKDD'18, Melbourne, Australia.
- Deep
learning for biomedicine, Tutorial at ACML'17 in Seoul, Korea.
- Deep neural nets for
healthcare, @Amazon Seattle,
Feb 2017
Preprints
- An
evaluation of randomized machine learning methods for redundant data:
Predicting short and medium-term suicide risk from administrative
records and risk assessments, T Nguyen, T Tran, S Gopakumar, D Phung, S
Venkatesh, arXiv preprint arXiv:1605.01116
- Relational dynamic
memory networks, Trang Pham, Truyen
Tran, Svetha Venkatesh, arXiv
preprint arXiv:1808.04247
- Counterfactual explanation with multi-agent reinforcement learning for drug target prediction Tri Minh Nguyen, Thomas P Quinn, Thin Nguyen, Truyen Tran, arXiv preprint arXiv:2103.12983
Publications
- Application of machine learning techniques to identify data reliability
and factors affecting outcome after stroke using electronic
administrative records, Santu Rana, Wei Luo, Truyen Tran, Svetha Venkatesh, Paul Talman, Thanh G Phan, Dinh Phung, Benjamin B Clissold, Frontiers in Neurology, 2021, doi: 10.3389/fneur.2021.670379.
- GEFA: Early Fusion Approach in Drug-Target Affinity Prediction, Tri Minh Nguyen, Thin Nguyen, Thao Minh Le, Truyen Tran, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021.
- A spatio-temporal attention-based model for infant movement assessment from videos, Binh Nguyen-Thai, Vuong Le, C Morgan, N Badawi, Truyen Tran, Svetha Venkatesh, IEEE journal of biomedical and health informatics, 2021.
- Personalized
Annotation-based Networks (PAN) for the prediction of breast cancer
relapse, T Nguyen, SC Lee, TP Quinn, B Truong, X Li, T Tran, S Venkatesh, TD Le, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021.
- Precision
Psychiatry with immunological and cognitive biomarkers: A multi-domain
prediction for the diagnosis of Bipolar Disorder or Schizophrenia using
machine learning, Brisa Fernandes, Chandan Karmakar, Ryad
Tamouza, Truyen Tran, Nora
Hamdani, Hakim Laouamri, Jean-Romain Richard, Robert Yolken, Michael
Berk, Svetha Venkatesh, Marion Leboyer, Translational Psychiatry, 05/2020
- Deep
in the bowel: Highly interpretable neural encoder-decoder networks
predict gut metabolites from gut microbiome, V Le, TP Quinn, T Tran, S Venkatesh, BMC Genomics (21), 07/2020.
- DeepTRIAGE:
Interpretable and individualised biomarker scores using attention
mechanism for the classification of breast cancer sub-types. A
Beykikhoshk, TP Quinn, SC Lee, T Tran,
S Venkatesh, BMC Medical Genomics, 2020.
- Graph transformation
policy
network for chemical reaction prediction, Kien Do, Truyen Tran, Svetha Venkatesh, KDD'19.
- Attentional multilabel
learning over graphs: A message passing approach, K Do, T Tran, T Nguyen, S Venkatesh, Machine Learning, 2019.
- Dual Memory Neural
Computer for Asynchronous Two-view Sequential Learning, H Le, T Tran, S Venkatesh, KDD'18.
- Graph Memory Networks
for Molecular Activity Prediction, Trang Pham, Truyen Tran, Svetha Venkatesh, ICPR'18.
- Resset: A Recurrent
Model for Sequence of Sets with Applications to Electronic Medical
Records, P Nguyen, T Tran,
S Venkatesh, IJCNN'18.
- Finding Algebraic Structure of
Care in Time: A Deep Learning Approach, Phuoc Nguyen, Truyen Tran, Svetha Venkatesh, NIPS Workshop on Machine Learning for
Health (ML4H), 2017.
-
Dual control memory
augmented neural networks for treatment recommendations, H Le, T Tran, S Venkatesh, PAKDD'18.
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Deep Learning to Attend to Risk in
ICU, Phuoc Nguyen, Truyen
Tran, Svetha Venkatesh, IJCAI'17
Workshop on Knowledge Discovery in Healthcare II: Towards Learning
Healthcare Systems (KDH 2017).
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Graph Classification via Deep
Learning with Virtual Nodes Trang Pham, Truyen Tran, Hoa Dam, Svetha
Venkatesh, Third Representation
Learning for Graphs Workshop (ReLiG 2017).
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Learning Recurrent Matrix
Representation, Kien Do, Truyen
Tran, Svetha
Venkatesh. Third Representation Learning for Graphs
Workshop (ReLiG 2017), also:
arXiv
preprint arXiv: 1703.01454.
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Predicting
healthcare trajectories from medical records: A deep learning approach,Trang
Pham, Truyen Tran, Dinh
Phung, Svetha Venkatesh, Journal of
Biomedical Informatics, April 2017, DOI:
10.1016/j.jbi.2017.04.001. [Tech
report PDF].
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DeepCare:
A Deep
Dynamic Memory Model for Predictive Medicine, Trang Pham, Truyen Tran, Dinh
Phung, Svetha Venkatesh, PAKDD'16,
Auckland, NZ, April 2016.
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Deepr: A Convolutional
Net for Medical Records, Phuoc Nguyen, Truyen Tran, Nilmini
Wickramasinghe, Svetha Venkatesh, IEEE
Journal of Biomedical and Health Informatics, vol. 21, no.
1, pp. 22–30, Jan. 2017, Doi:
10.1109/JBHI.2016.2633963
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Stabilizing Linear
Prediction Models using Autoencoder, Shivapratap
Gopakumara, Truyen Tran,
Dinh Phung, Svetha Venkatesh, International
Conference on Advanced Data Mining and Applications (ADMA
2016).
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Guidelines for
Developing and Reporting of Machine Learning Predictive Models in
Biomedical Research,
Wei Luo Dinh Phung; Truyen Tran; Sunil Gupta; Santu Rana; Chandan
Karmakar; Alistair Shilton; John Yearwood; Nevenka Dimitrova; Tu Bao
Ho; Svetha Venkatesh; Michael Berk, JMIR,
18(12), 2016.
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Forecasting patient outflow from
wards having no real-time clinical data, Shivapratap
Gopakumara, Truyen Tran,
Wei Luo, Dinh Phung, Svetha Venkatesh, ICHI'16.
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Screening
for Post 32-Week Preterm Birth Risk: How Helpful is Routine Perinatal
Data collection? Wei Luo, Emily Y-S Huning, Truyen Tran, Dinh
Phung, Svetha Venkatesh, Heliyon,
Volume 2, Issue 6, June 2016, Article e00119.
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Preterm Birth
Prediction: Deriving Stable and Interpretable Rules from High
Dimensional Data, Truyen
Tran, Wei
Luo, Dinh Phung, Jonathan Morris, Kristen Rickard, Svetha Venkatesh, Conference on Machine Learning
in Healthcare, LA, USA Aug 2016.
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Predicting Risk of
Suicide Attempt Using History of Physical Illnesses From Electronic
Medical Records, Chandan
Karmakar, Wei Luo, Truyen Tran, Michael Berk, and
Svetha Venkatesh, JMIR Mental Health (JMH).
-
Consistency of the
Health of the Nation Outcome Scales (HoNOS) at inpatient-to-community
transition, Wei Luo, Richard Harvey, Truyen Tran, Dinh
Phung, Svetha Venkatesh, Jason Connor, BMJ Open 2016;6:e010732
doi:10.1136/bmjopen-2015-010732
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Forecasting Daily
Patient Outflow From a Ward Having no Real-Time Clinical Data,
Shivapratap Gopakumara, Truyen
Tran, Wei Luo, Dinh Phung, Svetha Venkatesh, JMIR,
Vol 4, No 3 (2016): Jul-Sept.
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Is
demography destiny? Application of machine learning techniques
to accurately predict population health outcomes from a minimal
demographic dataset, Wei Luo , Thin Nguyen, Melanie Nichols, Truyen Tran, Santu
Rana, Sunil Gupta, Dinh Phung, Svetha Venkatesh, Steve Allender. PLoS ONE, May 4,
2015DOI: 10.1371/journal.pone.0125602.
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Stabilizing Sparse Cox Model
using Statistic and Semantic Structures in Electronic Medical Records.
Shivapratap Gopakumar, Tu Dinh Nguyen, Truyen
Tran, Dinh
Phung, and Svetha Venkatesh, PAKDD'15,
HCM City, Vietnam, May 2015.
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Web
search activity data accurately predicts population chronic disease
risk in the USA,
Thin Nguyen, Truyen Tran,
Wei Luo, Sunil Gupta, Santu Rana, Dinh Phung,
Melanie Nichols, Lynne Millar, Svetha Venkatesh, Steven Allender, Journal of Epidemiology
& Community Health, 2015,
doi:10.1136/jech-2014-204523.
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Learning
vector
representation of medical objects via EMR-driven nonnegative restricted
Boltzmann machines (e-NRBM), Truyen
Tran,
Tu
D. Nguyen, D.
Phung, and S. Venkatesh, Journal
of Biomedical Informatics, 2015,
doi:10.1016/j.jbi.2015.01.012.
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Tensor-variate
Restricted Boltzmann Machines,
Tu D. Nguyen, Truyen Tran,
D.
Phung, and S. Venkatesh, AAAI
2015.
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A
framework for feature
extraction from hospital medical data with applications in risk
prediction, Truyen
Tran, Wei Luo, Dinh Phung, Sunil Gupta, Santu Rana,
Richard Lee Kennedy, Ann Larkins and Svetha Venkatesh, BMC Informatics,
2015, DOI:10.1186/s12859-014-0425-8.
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Stabilizing
high-dimensional
prediction models using feature graphs, Shivapratap
Gopakumar, Truyen Tran,
Tu Dinh Nguyen, Dinh Phung, and Svetha Venkatesh, IEEE Journal of Biomedical and
Health Informatics, 2014
DOI:10.1109/JBHI.2014.2353031S
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Stabilizing
sparse Cox model using clinical structures in electronic medical records,
S Gopakumar, Truyen Tran,
D Phung, S Venkatesh, 2nd
International Workshop on Pattern Recognition for Healthcare Analytics,
August 2014
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Speed
up health research through topic modeling of coded clinical data,
Wei Luo, Dinh Phung, Vu Nguyen, Truyen
Tran, Svetha Venkatesh, 2nd
International Workshop on Pattern Recognition for Healthcare Analytics,
August 2014
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iPoll:
Automatic polling using online search, T Nguyen, D Phung, W
Luo, Truyen Tran,
S Venkatesh, Proc. of 15th
International Conference on Web Information System Engineering
(WISE), 2014
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HealthMap:
A visual platform for patient suicide risk review, S Rana, W
Luo, Truyen Tran,
D Phung, S Venkatesh, R Harvey, HISA
Big Data, Melbourne April, 2014
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Predicting
unplanned readmission after myocardial infarction from routinely
collected administrative hospital data, Santu Rana, Truyen Tran, Wei
Luo, Dinh Phung, Richard L. Kennedy, and Svetha Venkatesh, Australian Health Review, 2014
doi:dx.doi.org/10.1071/AH14059
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Risk
stratification using data from electronic medical records better
predict suicide risks than clinician assessments, Truyen Tran, Wei
Luo, Dinh Phung, Richard Harvey, Michael Berk, Richard Lee Kennedy,
Svetha Venkatesh, BMC
Psychiatry,
14:76, 2014.
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Machine-learning
prediction of cancer survival: a retrospective study using electronic
administrative records and a cancer registry, Sunil
Gupta, Truyen Tran,
Wei Luo, Dinh Phung, Richard Lee Kennedy, Adam
Broad, David Campbell, David Kipp, Madhu Singh, Mustafa Khasraw, Leigh
Matheson, David M Ashley, Svetha Venkatesh, BMJ Open, 2014,
doi:10.1136/bmjopen-2013-004007
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Stabilized
sparse ordinal regression for medical risk stratification, Truyen Tran, Dinh
Phung, Wei Luo, and Svetha Venkatesh, Knowledge
and Information Systems,
2014, DOI: 10.1007/s10115-014-0740-4.
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An integrated framework for
suicide risk prediction, Truyen
Tran, Dinh
Phung, Wei Luo, Richard Harvey, Michael Berk, and Svetha Venkatesh, In
Proc. of 19th
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD),
Chicago, USA, August, 2013.
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Latent
patient profile modelling and
applications with Mixed-Variate Restricted Boltzmann Machine,
Tu
D. Nguyen, Truyen Tran,
D. Phung, and S. Venkatesh, In
Proc. of 17th
Pacific-Asia Conference on Knowledge Discovery and Data Mining
(PAKDD’13), Gold Coast, Australia, April 2013.
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Classification and Pattern
Discovery of Mood in Weblogs, Thin Nguyen, Dinh
Q. Phung, Brett
Adams, Truyen Tran
and Svetha Venkatesh.
In Proc. of Pacific-Asia
Conference on Knowledge Discovery
and Data Mining (PAKDD), 21-24 June, Hyderabad, India,
2010
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