Artificial Intelligence-Based Omics Data Analysis

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ORIGINAL RESEARCH

Published on 21 Aug 2023

GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning

Nur Sebnem Ersoz
Burcu Bakir-Gungor
Malik Yousef

GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning

doi 10.3389/fgene.2023.1139082

3,397 views
11 citations

ORIGINAL RESEARCH

Published on 09 May 2023

Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest

Zijie Wang
Yuzhi Zhu
Zhule Liu
Hongfu Li
Xinqiang Tang
Yi Jiang

Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest

doi 10.3389/fgene.2023.1190887

2,069 views

ORIGINAL RESEARCH

Published on 06 Apr 2023

OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features

Maha A. Thafar
Somayah Albaradei
Mahmut Uludag
Mona Alshahrani
Takashi Gojobori
Magbubah Essack
Xin Gao

OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features

doi 10.3389/fgene.2023.1139626

1,924 views
4 citations

ORIGINAL RESEARCH

Published on 22 Feb 2023

Imaging genetic association analysis of triple-negative breast cancer based on the integration of prior sample information

Shipeng Ning
Juan Xie
Jianlan Mo
You Pan
Rong Huang
Qinghua Huang
Jifeng Feng

Imaging genetic association analysis of triple-negative breast cancer based on the integration of prior sample information

doi 10.3389/fgene.2023.1090847

1,267 views
2 citations

ORIGINAL RESEARCH

Published on 29 Nov 2022

Development and performance evaluation of an artificial intelligence algorithm using cell-free DNA fragment distance for non-invasive prenatal testing (aiD-NIPT)

Junnam Lee
Sae-Mi Lee
Jin Mo Ahn
Tae-Rim Lee
Wan Kim
Eun-Hae Cho
Chang-Seok Ki

Development and performance evaluation of an artificial intelligence algorithm using cell-free DNA fragment distance for non-invasive prenatal testing (aiD-NIPT)

doi 10.3389/fgene.2022.999587

4,172 views
5 citations

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Frontiers in Genetics

Frontiers in Genetics

Computational Genomics

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