Search Results for author: Lei Xin

Found 15 papers, 8 papers with code

Modeling and Predicting Epidemic Spread: A Gaussian Process Regression Approach

no code implementations14 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.

regression

Online Change Points Detection for Linear Dynamical Systems with Finite Sample Guarantees

no code implementations30 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.

Change Point Detection Time Series

Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model

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.

de novo peptide sequencing

Learning Linearized Models from Nonlinear Systems with Finite Data

no code implementations15 Sep 2023 Lei Xin, George Chiu, Shreyas Sundaram

Identifying a linear system model from data has wide applications in control theory.

Learning Dynamical Systems by Leveraging Data from Similar Systems

no code implementations8 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.

Finite Sample Guarantees for Distributed Online Parameter Estimation with Communication Costs

no code implementations12 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.

Identifying the Dynamics of a System by Leveraging Data from Similar Systems

1 code implementation11 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.

Learning the Dynamics of Autonomous Linear Systems From Multiple Trajectories

no code implementations24 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.

PointIso: Point Cloud Based Deep Learning Model for Detecting Arbitrary-Precision Peptide Features in LC-MS Map through Attention Based Segmentation

1 code implementation15 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.

DeepNovoV2: Better de novo peptide sequencing with deep learning

1 code implementation17 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.

de novo peptide sequencing

DeepIso: A Deep Learning Model for Peptide Feature Detection

no code implementations9 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.

Drug Discovery Specificity

Protein identification with deep learning: from abc to xyz

1 code implementation8 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.

de novo peptide sequencing

Cannot find the paper you are looking for? You can Submit a new open access paper.