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Book ; Online: Stochastic Models of Stem Cells and Their Descendants under Different Criticality Assumptions

Nguyen, Nam H / Kimmel, Marek

2022

Abstract: We study time continuous branching processes with exponentially distributed lifetimes, with two types of cells that proliferate according to binary fission. A range of possible system dynamics are considered, each of which is characterized by the ... ...

Abstract	We study time continuous branching processes with exponentially distributed lifetimes, with two types of cells that proliferate according to binary fission. A range of possible system dynamics are considered, each of which is characterized by the mutation rate of the original cells and the survival probability of the altered cells' progeny. For each system, we derive a closed-form expression for the joint probability generating function of cell counts, and perform asymptotic analysis on the behaviors of the cell population with particular focus on probability of extinction. Part of our results confirms known properties of branching processes using a different approach while other are original. While the model is best suited for modeling the fate of differentiating stem cells, we discuss other scenarios in which these system dynamics may be applicable in real life. We also discuss the history of the subject. Comment: 23 pages including abstract, references, and supplement
Keywords	Mathematics - Probability ; Mathematics - Statistics Theory ; Statistics - Computation
Subject code	612
Publishing date	2022-04-25
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: LFSPROShiny: An Interactive R/Shiny App for Prediction and Visualization of Cancer Risks in Families With Deleterious Germline

Nguyen, Nam H / Dodd-Eaton, Elissa B / Peng, Gang / Corredor, Jessica L / Jiao, Wenwei / Woodman-Ross, Jacynda / Arun, Banu K / Wang, Wenyi

JCO clinical cancer informatics

2024 Volume 8, Page(s) e2300167

Abstract: Purpose: LFSPRO is an R library that implements risk prediction models for Li-Fraumeni syndrome (LFS), a genetic disorder characterized by deleterious germline mutations in the : Methods: LFSPROShiny implements two models that have been validated on ... ...

Abstract	Purpose: LFSPRO is an R library that implements risk prediction models for Li-Fraumeni syndrome (LFS), a genetic disorder characterized by deleterious germline mutations in the Methods: LFSPROShiny implements two models that have been validated on multiple LFS patient cohorts: a competing risk model that predicts cancer-specific risks for the first primary and a recurrent-event model that predicts the risk of a second primary tumor. Starting with a visualization template, we keep regular contact with GCs, who ran LFSPROShiny in their counseling sessions, to collect feedback and discuss potential improvement. On receiving the family history as input, LFSPROShiny renders the family into a pedigree and displays the risk estimates of the family members in a tabular format. The software offers interactive overlaid side-by-side bar charts for visualization of the patients' cancer risks relative to the general population. Results: We walk through a detailed example to illustrate how GCs can run LFSPROShiny in clinics from data preparation to downstream analyses and interpretation of results with an emphasis on the utilities that LFSPROShiny provides to aid decision making. Conclusion: Since December 2021, we have applied LFSPROShiny to over 100 families from counseling sessions at the MD Anderson Cancer Center. Our study suggests that software tools with easy-to-use interfaces are crucial for the dissemination of risk prediction models in clinical settings, hence serving as a guideline for future development of similar models.
MeSH term(s)	Humans ; Genetic Predisposition to Disease ; Germ Cells ; Germ-Line Mutation ; Li-Fraumeni Syndrome/diagnosis ; Li-Fraumeni Syndrome/genetics ; Li-Fraumeni Syndrome/epidemiology ; Mobile Applications ; Tumor Suppressor Protein p53/genetics
Chemical Substances	TP53 protein, human ; Tumor Suppressor Protein p53
Language	English
Publishing date	2024-01-05
Publishing country	United States
Document type	Journal Article
ISSN	2473-4276
ISSN (online)	2473-4276
DOI	10.1200/CCI.23.00167
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Personalized Risk Prediction for Cancer Survivors: A Bayesian Semi-parametric Recurrent Event Model with Competing Outcomes.

Nguyen, Nam H / Shin, Seung Jun / Dodd-Eaton, Elissa B / Ning, Jing / Wang, Wenyi

bioRxiv : the preprint server for biology

2023

Abstract: Multiple primary cancers are increasingly more frequent due to improved survival of cancer patients. Characteristics of the first primary cancer largely impact the risk of developing subsequent primary cancers. Hence, model-based risk characterization of ...

Abstract	Multiple primary cancers are increasingly more frequent due to improved survival of cancer patients. Characteristics of the first primary cancer largely impact the risk of developing subsequent primary cancers. Hence, model-based risk characterization of cancer survivors that captures patient-specific variables is needed for healthcare policy making. We propose a Bayesian semi-parametric framework, where the occurrence processes of the competing cancer types follow independent non-homogeneous Poisson processes and adjust for covariates including the type and age at diagnosis of the first primary. Applying this framework to a historically collected cohort with families presenting a highly enriched history of multiple primary tumors and diverse cancer types, we have derived a suite of age-to-onset penetrance curves for cancer survivors. This includes penetrance estimates for second primary lung cancer, potentially impactful to ongoing cancer screening decisions. Using Receiver Operating Characteristic (ROC) curves, we have validated the good predictive performance of our models in predicting second primary lung cancer, sarcoma, breast cancer, and all other cancers combined, with areas under the curves (AUCs) at 0.89, 0.91, 0.76 and 0.68, respectively. In conclusion, our framework provides covariate-adjusted quantitative risk assessment for cancer survivors, hence moving a step closer to personalized health management for this unique population.
Language	English
Publishing date	2023-03-06
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.02.28.530537
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Article: LFSPROShiny: an interactive R/Shiny app for prediction and visualization of cancer risks in families with deleterious germline

Nguyen, Nam H / Dodd-Eaton, Elissa B / Peng, Gang / Corredor, Jessica L / Jiao, Wenwei / Woodman-Ross, Jacynda / Arun, Banu K / Wang, Wenyi

medRxiv : the preprint server for health sciences

2023

Abstract	Purpose: LFSPRO is an R library that implements risk prediction models for Li-Fraumeni syndrome (LFS), a genetic disorder characterized by deleterious germline mutations in the Methods: LFSPROShiny implements two models that have been validated on multiple LFS patient cohorts: a competing-risk model that predicts cancer-specific risks for the first primary, and a recurrent-event model that predicts the risk of a second primary tumor. Starting with a visualization template, we keep regular contact with GCs, who ran LFSPROShiny in their counseling sessions, to collect feedback and discuss potential improvement. Upon receiving the family history as input, LFSPROShiny renders the family into a pedigree, and displays the risk estimates of the family members in a tabular format. The software offers interactive overlaid side-by-side bar charts for visualization of the patients' cancer risks relative to the general population. Results: We walk through a detailed example to illustrate how GCs can run LFSPROShiny in clinics, from data preparation to downstream analyses and interpretation of results with an emphasis on the utilities that LFSPROShiny provides to aid decision making. Conclusion: Since Dec 2021, we have applied LFSPROShiny to over 100 families from counseling sessions at MD Anderson Cancer Center. Our study suggests that software tools with easy-to-use interfaces are crucial for the dissemination of risk prediction models in clinical settings, hence serving as a guideline for future development of similar models.
Language	English
Publishing date	2023-08-15
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.08.11.23293956
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Book ; Online: A Time Series is Worth 64 Words

Nie, Yuqi / Nguyen, Nam H. / Sinthong, Phanwadee / Kalagnanam, Jayant

Long-term Forecasting with Transformers

2022

Abstract: We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components: (i) segmentation of time series into subseries-level patches which are ... ...

Abstract	We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components: (i) segmentation of time series into subseries-level patches which are served as input tokens to Transformer; (ii) channel-independence where each channel contains a single univariate time series that shares the same embedding and Transformer weights across all the series. Patching design naturally has three-fold benefit: local semantic information is retained in the embedding; computation and memory usage of the attention maps are quadratically reduced given the same look-back window; and the model can attend longer history. Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models. We also apply our model to self-supervised pre-training tasks and attain excellent fine-tuning performance, which outperforms supervised training on large datasets. Transferring of masked pre-trained representation on one dataset to others also produces SOTA forecasting accuracy. Code is available at: https://github.com/yuqinie98/PatchTST. Comment: Accepted by ICLR 2023
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2022-11-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Tiny Time Mixers (TTMs)

Ekambaram, Vijay / Jati, Arindam / Nguyen, Nam H. / Dayama, Pankaj / Reddy, Chandra / Gifford, Wesley M. / Kalagnanam, Jayant

Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series

2024

Abstract: Large pre-trained models for zero/few-shot learning excel in language and vision domains but encounter challenges in multivariate time series (TS) due to the diverse nature and scarcity of publicly available pre-training data. Consequently, there has ... ...

Abstract	Large pre-trained models for zero/few-shot learning excel in language and vision domains but encounter challenges in multivariate time series (TS) due to the diverse nature and scarcity of publicly available pre-training data. Consequently, there has been a recent surge in utilizing pre-trained large language models (LLMs) with token adaptations for TS forecasting. These approaches employ cross-domain transfer learning and surprisingly yield impressive results. However, these models are typically very slow and large (~billion parameters) and do not consider cross-channel correlations. To address this, we present Tiny Time Mixers (TTM), a significantly small model based on the lightweight TSMixer architecture. TTM marks the first success in developing fast and tiny general pre-trained models (<1M parameters), exclusively trained on public TS datasets, with effective transfer learning capabilities for forecasting. To tackle the complexity of pre-training on multiple datasets with varied temporal resolutions, we introduce several novel enhancements such as adaptive patching, dataset augmentation via downsampling, and resolution prefix tuning. Moreover, we employ a multi-level modeling strategy to effectively model channel correlations and infuse exogenous signals during fine-tuning, a crucial capability lacking in existing benchmarks. TTM shows significant accuracy gains (12-38\%) over popular benchmarks in few/zero-shot forecasting. It also drastically reduces the compute needs as compared to LLM-TS methods, with a 14X cut in learnable parameters, 106X less total parameters, and substantial reductions in fine-tuning (65X) and inference time (54X). In fact, TTM's zero-shot often surpasses the few-shot results in many popular benchmarks, highlighting the efficacy of our approach. Code and pre-trained models will be open-sourced.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	330 ; 006
Publishing date	2024-01-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Guidance for Transitioning Among Anticoagulants.

Trang, Joseph / Nguyen, Nam H / Huynh, Sandrine

AACN advanced critical care

2019 Volume 30, Issue 3, Page(s) 209–216

MeSH term(s)	Administration, Oral ; Adult ; Aged ; Aged, 80 and over ; Anticoagulants/standards ; Anticoagulants/therapeutic use ; Blood Coagulation/drug effects ; Critical Care/standards ; Curriculum ; Education, Nursing, Continuing ; Female ; Humans ; Male ; Medication Therapy Management/standards ; Middle Aged ; Practice Guidelines as Topic ; Thrombosis/drug therapy
Chemical Substances	Anticoagulants
Language	English
Publishing date	2019-08-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	2239949-5
ISSN	1559-7776 ; 1559-7768
ISSN (online)	1559-7776
ISSN	1559-7768
DOI	10.4037/aacnacc2019172
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Article ; Online: Validating Risk Prediction Models for Multiple Primaries and Competing Cancer Outcomes in Families With Li-Fraumeni Syndrome Using Clinically Ascertained Data.

Nguyen, Nam H / Dodd-Eaton, Elissa B / Corredor, Jessica L / Woodman-Ross, Jacynda / Green, Sierra / Gutierrez, Angelica M / Arun, Banu K / Wang, Wenyi

Journal of clinical oncology : official journal of the American Society of Clinical Oncology

2024 , Page(s) JCO2301926

Abstract: Purpose: There exists a barrier between developing and disseminating risk prediction models in clinical settings. We hypothesize that this barrier may be lifted by demonstrating the utility of these models using incomplete data that are collected in ... ...

Abstract	Purpose: There exists a barrier between developing and disseminating risk prediction models in clinical settings. We hypothesize that this barrier may be lifted by demonstrating the utility of these models using incomplete data that are collected in real clinical sessions, as compared with the commonly used research cohorts that are meticulously collected. Materials and methods: Genetic counselors (GCs) collect family history when patients (ie, probands) come to MD Anderson Cancer Center for risk assessment of Li-Fraumeni syndrome, a genetic disorder characterized by deleterious germline mutations in the Results: For prediction of deleterious Conclusion: We describe a study that fills in the critical gap in knowledge for the utility of risk prediction models. Using a CCB cohort, our previously validated models have demonstrated good performance and outperformed the standard clinical criteria. Our study suggests that better risk counseling may be achieved by GCs using these already-developed mathematical models.
Language	English
Publishing date	2024-04-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	604914-x
ISSN	1527-7755 ; 0732-183X
ISSN (online)	1527-7755
ISSN	0732-183X
DOI	10.1200/JCO.23.01926
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Article ; Online: Evolution of physico-chemical properties of Dicranopteris linearis-derived activated carbon under various physical activation atmospheres.

Mai, Nga T / Nguyen, Minh N / Tsubota, Toshiki / Nguyen, Phuong L T / Nguyen, Nam H

Scientific reports

2021 Volume 11, Issue 1, Page(s) 14430

Abstract: This work emphasizes the effect of the physical activation using ... ...

Abstract	This work emphasizes the effect of the physical activation using CO
Language	English
Publishing date	2021-07-13
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-93934-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Benchmarking Neural Networks For Quantum Computations.

Nguyen, Nam H / Behrman, E C / Moustafa, Mohamed A / Steck, J E

IEEE transactions on neural networks and learning systems

2019 Volume 31, Issue 7, Page(s) 2522–2531

Abstract: The power of quantum computers is still somewhat speculative. Although they are certainly faster than classical ones at some tasks, the class of problems they can efficiently solve has not been mapped definitively onto known classical complexity theory. ... ...

Abstract	The power of quantum computers is still somewhat speculative. Although they are certainly faster than classical ones at some tasks, the class of problems they can efficiently solve has not been mapped definitively onto known classical complexity theory. This means that we do not know for which calculations there will be a "quantum advantage," once an algorithm is found. One way to answer the question is to find those algorithms, but finding truly quantum algorithms turns out to be very difficult. In previous work, over the past three decades, we have pursued the idea of using techniques of machine learning to develop algorithms for quantum computing. Here, we compare the performance of standard real- and complex-valued classical neural networks with that of one of our models for a quantum neural network, on both classical problems and on an archetypal quantum problem: the computation of an entanglement witness. The quantum network is shown to need far fewer epochs and a much smaller network to achieve comparable or better results.
Language	English
Publishing date	2019-09-02
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2019.2933394
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