no code implementations • 14 Dec 2023 • Baike She, Lei Xin, Philip E. Paré, Matthew Hale
Gaussian Process Regression excels in using small datasets and providing uncertainty bounds, and both of these properties are critical in modeling and predicting epidemic spreading processes with limited data.
no code implementations • 30 Nov 2023 • Lei Xin, George Chiu, Shreyas Sundaram
We develop a data-dependent threshold that can be used in our test that allows one to achieve a pre-specified upper bound on the probability of making a false alarm.
1 code implementation • Nature Machine Intelligence 2023 • Zeping Mao, Ruixue Zhang, Lei Xin, Ming Li
Here we reveal that in the process of peptide prediction, missing fragmentation results in the generation of incorrect amino acids within those regions and causes error accumulation thereafter.
no code implementations • 15 Sep 2023 • Lei Xin, George Chiu, Shreyas Sundaram
Identifying a linear system model from data has wide applications in control theory.
no code implementations • 8 Feb 2023 • Lei Xin, Lintao Ye, George Chiu, Shreyas Sundaram
We consider the problem of learning the dynamics of a linear system when one has access to data generated by an auxiliary system that shares similar (but not identical) dynamics, in addition to data from the true system.
no code implementations • 12 Sep 2022 • Lei Xin, George Chiu, Shreyas Sundaram
We provide non-asymptotic bounds on the estimation error, leveraging the statistical properties of the underlying model.
1 code implementation • 11 Apr 2022 • Lei Xin, Lintao Ye, George Chiu, Shreyas Sundaram
We study the problem of identifying the dynamics of a linear system when one has access to samples generated by a similar (but not identical) system, in addition to data from the true system.
no code implementations • 24 Mar 2022 • Lei Xin, George Chiu, Shreyas Sundaram
Existing results on learning rate and consistency of autonomous linear system identification rely on observations of steady state behaviors from a single long trajectory, and are not applicable to unstable systems.
2 code implementations • Nature Machine Intelligence 2021 • Rui Qiao, Ngoc Hieu Tran, Lei Xin, Xin Chen, Ming Li, Baozhen Shan, Ali Ghodsi
De novo peptide sequencing is the key technology for finding novel peptides from mass spectra.
1 code implementation • 15 Sep 2020 • Fatema Tuz Zohora, M Ziaur Rahman, Ngoc Hieu Tran, Lei Xin, Baozhen Shan, Ming Li
A promising technique of discovering disease biomarkers is to measure the relative protein abundance in multiple biofluid samples through liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics.
1 code implementation • 17 Apr 2019 • Rui Qiao, Ngoc Hieu Tran, Lei Xin, Baozhen Shan, Ming Li, Ali Ghodsi
Personalized cancer vaccines are envisioned as the next generation rational cancer immunotherapy.
1 code implementation • Nature Methods 2018 • Ngoc Hieu Tran, Rui Qiao, Lei Xin, Xin Chen, Chuyi Liu, Xianglilan Zhang, Baozhen Shan, Ali Ghodsi, Ming Li
We present DeepNovo-DIA, a de novo peptide-sequencing method for data-independent acquisition (DIA) mass spectrometry data.
no code implementations • 9 Dec 2017 • Fatema Tuz Zohora, Ngoc Hieu Tran, Xianglilan Zhang, Lei Xin, Baozhen Shan, Ming Li
In this paper we propose a novel deep learning based model, DeepIso, that uses Convolutional Neural Networks (CNNs) to scan an LC-MS map to detect peptide features and estimate their abundance.
1 code implementation • 8 Oct 2017 • Ngoc Hieu Tran, Zachariah Levine, Lei Xin, Baozhen Shan, Ming Li
We combine two modules de novo sequencing and database search into a single deep learning framework for peptide identification, and integrate de Bruijn graph assembly technique to offer a complete solution to reconstruct protein sequences from tandem mass spectrometry data.
2 code implementations • PNAS 2017 • Ngoc Hieu Tran, Xianglilan Zhang, Lei Xin, Baozhen Shan, Ming Li
In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing.