no code implementations • 14 Jan 2025 • Reza Miry, Amit K. Chakraborty, Russell Greiner, Mark A. Lewis, Hao Wang, Tianyu Guan, Pouria Ramazi
We employed two simulated datasets to train the model: one representing generated dynamical systems with randomly selected polynomial terms to model new disease behaviors, and another simulating noise-induced disease dynamics to account for noisy measurements.
1 code implementation • 30 Oct 2024 • Roman Bushuiev, Anton Bushuiev, Niek F. de Jonge, Adamo Young, Fleming Kretschmer, Raman Samusevich, Janne Heirman, Fei Wang, Luke Zhang, Kai Dührkop, Marcus Ludwig, Nils A. Haupt, Apurva Kalia, Corinna Brungs, Robin Schmid, Russell Greiner, Bo wang, David S. Wishart, Li-Ping Liu, Juho Rousu, Wout Bittremieux, Hannes Rost, Tytus D. Mak, Soha Hassoun, Florian Huber, Justin J. J. van der Hooft, Michael A. Stravs, Sebastian Böcker, Josef Sivic, Tomáš Pluskal
To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data.
De novo molecule generation from MS/MS spectrum
De novo molecule generation from MS/MS spectrum (bonus chemical formulae)
+4
1 code implementation • 27 Oct 2024 • Shi-ang Qi, Yakun Yu, Russell Greiner
Survival prediction often involves estimating the time-to-event distribution from censored datasets.
1 code implementation • 10 Sep 2024 • Christian Marius Lillelund, Ali Hossein Gharari Foomani, Weijie Sun, Shi-ang Qi, Russell Greiner
These events are not mutually exclusive and there are often statistical dependencies between them.
1 code implementation • 12 May 2024 • Shi-ang Qi, Yakun Yu, Russell Greiner
Discrimination and calibration represent two important properties of survival analysis, with the former assessing the model's ability to accurately rank subjects and the latter evaluating the alignment of predicted outcomes with actual events.
1 code implementation • 26 Apr 2024 • Vikhyat Agrawal, Sunil Vasu Kalmady, Venkataseetharam Manoj Malipeddi, Manisimha Varma Manthena, Weijie Sun, Saiful Islam, Abram Hindle, Padma Kaul, Russell Greiner
Our results show that the performance achieved using our implementation of the FL approach is comparable to that of the pooled approach, where the model is trained over the aggregating data from all hospitals.
no code implementations • 13 Apr 2024 • Shan Gao, Amit K. Chakraborty, Russell Greiner, Mark A. Lewis, Hao Wang
In summary, we showed that there are statistical features that distinguish outbreak and non-outbreak sequences long before outbreaks occur.
1 code implementation • 24 Mar 2024 • Amit K. Chakraborty, Shan Gao, Reza Miry, Pouria Ramazi, Russell Greiner, Mark A. Lewis, Hao Wang
The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies.
no code implementations • 20 Jun 2023 • Ali Hossein Gharari Foomani, Michael Cooper, Russell Greiner, Rahul G. Krishnan
A survival dataset describes a set of instances (e. g. patients) and provides, for each, either the time until an event (e. g. death), or the censoring time (e. g. when lost to follow-up - which is a lower bound on the time until the event).
1 code implementation • 1 Jun 2023 • Shi-ang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner
One straightforward metric to evaluate a survival prediction model is based on the Mean Absolute Error (MAE) -- the average of the absolute difference between the time predicted by the model and the true event time, over all subjects.
1 code implementation • 29 May 2023 • Philippe Weitz, Masi Valkonen, Leslie Solorzano, Circe Carr, Kimmo Kartasalo, Constance Boissin, Sonja Koivukoski, Aino Kuusela, Dusan Rasic, Yanbo Feng, Sandra Sinius Pouplier, Abhinav Sharma, Kajsa Ledesma Eriksson, Stephanie Robertson, Christian Marzahl, Chandler D. Gatenbee, Alexander R. A. Anderson, Marek Wodzinski, Artur Jurgas, Niccolò Marini, Manfredo Atzori, Henning Müller, Daniel Budelmann, Nick Weiss, Stefan Heldmann, Johannes Lotz, Jelmer M. Wolterink, Bruno De Santi, Abhijeet Patil, Amit Sethi, Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa, Mahtab Farrokh, Neeraj Kumar, Russell Greiner, Leena Latonen, Anne-Vibeke Laenkholm, Johan Hartman, Pekka Ruusuvuori, Mattias Rantalainen
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications.
no code implementations • 9 Feb 2023 • Roberto Vega, Zehra Shah, Pouria Ramazi, Russell Greiner
Here, we propose two new methods to model the number of people infected with COVID-19 over time, each as a linear combination of latent sub-populations -- i. e., when we do not know which person is in which sub-population, and the only available observations are the aggregates across all sub-populations.
no code implementations • 14 Nov 2022 • Weijie Sun, Sunil Vasu Kalmady, Nariman Sepehrvand, Luan Manh Chu, Zihan Wang, Amir Salimi, Abram Hindle, Russell Greiner, Padma Kaul
Pandemic outbreaks such as COVID-19 occur unexpectedly, and need immediate action due to their potential devastating consequences on global health.
no code implementations • 6 Oct 2022 • Weijie Sun, Sunil Vasu Kalmady, Amir Salimi, Nariman Sepehrvand, Eric Ly, Abram Hindle, Russell Greiner, Padma Kaul
Electrocardiogram (ECG) abnormalities are linked to cardiovascular diseases, but may also occur in other non-cardiovascular conditions such as mental, neurological, metabolic and infectious conditions.
1 code implementation • 14 Jan 2022 • Roberto Vega, Russell Greiner
A predictor, $f_A : X \to Y$, learned with data from a source domain (A) might not be accurate on a target domain (B) when their distributions are different.
no code implementations • 11 Nov 2021 • Negar Hassanpour, Russell Greiner
In this paper, we take a generative approach that builds on the recent advances in Variational Auto-Encoders to simultaneously learn those underlying factors as well as the causal effects.
no code implementations • 3 Jun 2021 • Roberto Vega, Leonardo Flores, Russell Greiner
Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions.
no code implementations • 11 Feb 2021 • Roberto Vega, Pouneh Gorji, Zichen Zhang, Xuebin Qin, Abhilash Rakkunedeth Hareendranathan, Jeevesh Kapur, Jacob L. Jaremko, Russell Greiner
This complicates its use in tasks like image-based medical diagnosis, where the small training datasets are usually insufficient to learn appropriate data representations.
no code implementations • NeurIPS 2020 • Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang, Andrew J. Greenshaw, Russell Greiner
The optimization procedure extracts the common features for each site by using a single-iteration algorithm and maps these site-specific common features to the site-independent shared space.
no code implementations • ICLR 2020 • Negar Hassanpour, Russell Greiner
We consider the challenge of estimating treatment effects from observational data; and point out that, in general, only some factors based on the observed covariates X contribute to selection of the treatment T, and only some to determining the outcomes Y.
no code implementations • 19 Dec 2019 • Zichen Zhang, Qingfeng Lan, Lei Ding, Yue Wang, Negar Hassanpour, Russell Greiner
We learn two groups of latent random variables, where one group corresponds to variables that only cause selection bias, and the other group is relevant for outcome prediction.
1 code implementation • NeurIPS 2019 • Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar F. Caiafa, Russell Greiner, Martha White
We develop an efficient optimization strategy for this extremely high-dimensional sparse problem, by reducing the number of parameters using a greedy algorithm designed specifically for the problem.
no code implementations • ICML 2020 • Junfeng Wen, Russell Greiner, Dale Schuurmans
In many real-world applications, we want to exploit multiple source datasets of similar tasks to learn a model for a different but related target dataset -- e. g., recognizing characters of a new font using a set of different fonts.
1 code implementation • 25 Jun 2019 • Samuel Sokota, Ryan D'Orazio, Khurram Javed, Humza Haider, Russell Greiner
In this paper, we demonstrate that an existing method for estimating simultaneous prediction intervals from samples can easily be adapted for patient-specific survival curve analysis and yields accurate results.
no code implementations • 29 Mar 2019 • Neil C. Borle, Edmond A. Ryan, Russell Greiner
If we can accurately predict a patient's future BG values from his/her current features (e. g., predicting today's lunch BG value given today's diabetes diary entry for breakfast, including insulin injections, and perhaps earlier entries), then it is relatively easy to produce an effective regimen.
no code implementations • 25 Mar 2019 • Luke Kumar, Russell Greiner
As standard survival prediction models have a hard time coping with the high-dimensionality of such gene expression (GE) data, many projects use some dimensionality reduction techniques to overcome this hurdle.
2 code implementations • 28 Nov 2018 • Humza Haider, Bret Hoehn, Sarah Davis, Russell Greiner
This paper first motivates such "individual survival distribution" (ISD) models, and explains how they differ from standard models.
no code implementations • 1 Dec 2017 • Jumana Dakka, Pouya Bashivan, Mina Gheiratmand, Irina Rish, Shantenu Jha, Russell Greiner
Smart systems that can accurately diagnose patients with mental disorders and identify effective treatments based on brain functional imaging data are of great applicability and are gaining much attention.
no code implementations • 1 Jan 2016 • Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider, Dale Schuurmans, Russell Greiner
We propose a Laplace approximation that creates a stochastic unit from any smooth monotonic activation function, using only Gaussian noise.
no code implementations • 28 Sep 2015 • Siamak Ravanbakhsh, Barnabas Poczos, Russell Greiner
Boolean matrix factorization and Boolean matrix completion from noisy observations are desirable unsupervised data-analysis methods due to their interpretability, but hard to perform due to their NP-hardness.
no code implementations • 25 Sep 2014 • Siamak Ravanbakhsh, Russell Greiner
This paper studies the form and complexity of inference in graphical models using the abstraction offered by algebraic structures.
no code implementations • 4 Sep 2014 • Siamak Ravanbakhsh, Philip Liu, Trent Bjorndahl, Rupasri Mandal, Jason R. Grant, Michael Wilson, Roman Eisner, Igor Sinelnikov, Xiaoyu Hu, Claudio Luchinat, Russell Greiner, David S. Wishart
This information can be extracted from a biofluid's NMR spectrum.
no code implementations • NeurIPS 2014 • Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner
The cutting plane method is an augmentative constrained optimization procedure that is often used with continuous-domain optimization techniques such as linear and convex programs.
no code implementations • 6 May 2014 • Siamak Ravanbakhsh, Russell Greiner, Brendan Frey
During the learning, to produce a sample from the current model, we start from a training data and descend in the energy landscape of the "perturbed model", for a fixed number of steps, or until a local optima is reached.
no code implementations • 26 Jan 2014 • Siamak Ravanbakhsh, Russell Greiner
We introduce an efficient message passing scheme for solving Constraint Satisfaction Problems (CSPs), which uses stochastic perturbation of Belief Propagation (BP) and Survey Propagation (SP) messages to bypass decimation and directly produce a single satisfying assignment.
no code implementations • NeurIPS 2011 • Chun-Nam Yu, Russell Greiner, Hsiu-Chin Lin, Vickie Baracos
An accurate model of patient survival time can help in the treatment and care of cancer patients.