Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models
L Wang, Y Tan, X Yang, L Kuang… - Briefings in …, 2022 - academic.oup.com
In recent years, with the rapid development of techniques in bioinformatics and life science,
a considerable quantity of biomedical data has been accumulated, based on which …
a considerable quantity of biomedical data has been accumulated, based on which …
Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning
Background The coronavirus disease 2019 (COVID-19) pandemic has caused health
concerns worldwide since December 2019. From the beginning of infection, patients will …
concerns worldwide since December 2019. From the beginning of infection, patients will …
Learning with joint cross-document information via multi-task learning for named entity recognition
In information extraction, named entity recognition (NER) aims to locate named entities in
unstructured text and classify them into predefined categories. Most existing methods for …
unstructured text and classify them into predefined categories. Most existing methods for …
Network-based drug sensitivity prediction
Background Drug sensitivity prediction and drug responsive biomarker selection on high-
throughput genomic data is a critical step in drug discovery. Many computational methods …
throughput genomic data is a critical step in drug discovery. Many computational methods …
[HTML][HTML] Using hidden Markov model to predict recurrence of breast cancer based on sequential patterns in gene expression profiles
A new approach is presented to predict breast cancer recurrence through gene expression
profiles using hidden Markov models (HMM). In this regard, 322 genes were selected from …
profiles using hidden Markov models (HMM). In this regard, 322 genes were selected from …
Translation of Epigenetics in Cell-Free DNA Liquid Biopsy Technology and Precision Oncology
WY Tan, S Nagabhyrava, O Ang-Olson, P Das… - Current Issues in …, 2024 - mdpi.com
Technological advancements in cell-free DNA (cfDNA) liquid biopsy have triggered
exponential growth in numerous clinical applications. While cfDNA-based liquid biopsy has …
exponential growth in numerous clinical applications. While cfDNA-based liquid biopsy has …
Precision cancer classification using liquid biopsy and advanced machine learning techniques
A Eledkawy, T Hamza, S El-Metwally - Scientific Reports, 2024 - nature.com
Cancer presents a significant global health burden, resulting in millions of annual deaths.
Timely detection is critical for improving survival rates, offering a crucial window for timely …
Timely detection is critical for improving survival rates, offering a crucial window for timely …
omicsGAT: Graph attention network for cancer subtype analyses
The use of high-throughput omics technologies is becoming increasingly popular in all
facets of biomedical science. The mRNA sequencing (RNA-seq) method reports quantitative …
facets of biomedical science. The mRNA sequencing (RNA-seq) method reports quantitative …
Pan-cancer metastasis prediction based on graph deep learning method
Y Xu, X Cui, Y Wang - Frontiers in Cell and Developmental Biology, 2021 - frontiersin.org
Tumor metastasis is the major cause of mortality from cancer. From this perspective,
detecting cancer gene expression and transcriptome changes is important for exploring …
detecting cancer gene expression and transcriptome changes is important for exploring …
A 65-nm RRAM Compute-in-Memory Macro for Genome Processing
This work presents the first resistive random access memory (RRAM)-based compute-in-
memory (CIM) macro design tailored for genome processing. We analyze and demonstrate …
memory (CIM) macro design tailored for genome processing. We analyze and demonstrate …