公开(公告)号：US20240013860A1

公开(公告)日：2024-01-11

申请号：US18347105

申请日：2023-07-05

Applicant: BIOINFORMATICS SOLUTIONS INC.

Inventor： Hieu TRAN ,  Ming LI

IPC: G16B20/30 ,  G16B20/20 ,  G01N33/68

CPC classification number: G16B20/30 ,  G16B20/20 ,  G01N33/6878

Abstract: Personalized machine learning systems and methods are provided to predict the collective response of a patient's CD8+ T cells by modeling positive and negative selection processes. For each individual patient, HLA-I self peptides were used as negative selection, and allele-matched immunogenic T cell epitopes as positive selection. The negative and positive peptides were used to train a binary classification model, which was then applied to predict the immunogenicity of candidate neoantigens of that patient.

公开(公告)号：US20170336419A1

公开(公告)日：2017-11-23

申请号：US15599431

申请日：2017-05-18

Applicant: BIOINFORMATICS SOLUTIONS INC.

Inventor： Ngoc Hieu TRAN ,  Mohammad Ziaur RAHMAN ,  Lin HE ,  Lei XIN ,  Baozhen SHAN ,  Ming LI

IPC: G01N33/68 ,  G06F19/22 ,  G06F19/26

CPC classification number: G01N33/6818 ,  G01N33/6848 ,  G01N33/6854 ,  G01N2560/00 ,  G06F19/22 ,  G06F19/24 ,  G06F19/26

Abstract: Methods and systems for determining amino acid sequence of a polypeptide or protein from mass spectrometry data is provided, using a weighted de Bruijn graph. Extracted and purified protein is cleaved into a mixture of peptide and then analyzed using mass spectrometry. A list of peptide sequences is derived from mass spectrometry fragment data by de novo sequencing, and amino acid confidence scores are determined from peak fragment ion intensity. A weighted de Bruijn graph is constructed for the list of peptide sequences having node weights defined by k−1 mer confidence scores. At least one contig is assembled from the de Bruijn graph by identifying node weights having the highest k-1 mer confidence scores.

公开(公告)号：US20200326348A1

公开(公告)日：2020-10-15

申请号：US16846817

申请日：2020-04-13

Applicant: BIOINFORMATICS SOLUTIONS INC.

Inventor： Rui QIAO ,  Ngoc Hieu Tran ,  Lei XIN ,  Xin CHEN ,  Baozhen Shan ,  Ali GHODSI ,  Ming LI

IPC: G01N33/68 ,  G16B30/00 ,  G16B20/00 ,  G16B40/00

Abstract: The present systems and methods are directed to de novo identification of peptide sequences from tandem mass spectrometry data. The systems and methods uses unconverted mass spectrometry data from which features are extracted. Using unconverted mass spectrometry data reduces the loss of information and provides more accurate sequencing of peptides. The systems and methods combine deep learning and neural networks to sequencing of peptides.