International Journal of Molecular Sciences

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Dear Colleagues,

Medical informatics are the information technology to develop medical knowledge and to facilitate the delivery of patient medical care and clinical decision making. Its goal is discovering novel potential biomarkers, developing genetic risk prediction models, revealing the underlying mechanism of disease development, and exploring treatment strategies for diseases. Nowadays, precision medicine focuses on a patient’s disease at different levels (from genetic level to clinic level), integrates multi-omics data, and seeks to find targeted treatments for each individual’s disease. Medical informatics techniques, such as statistical methods, deep learning, and machine learning, are paving a new effective way for personalized precision medicine. Together with multi-omics data, advanced medical informatics technology would help researchers to in-depth reveal the mechanisms of diseases from multi-dimension data, such as gene expression, bioimages, and clinical data, etc. Thus, it significantly requires collaboration among researchers with different disciplines, including computer scientists, biomedical scientists, clinicians, molecular evolutionist, and bioinformaticians. In this Special Issue, we would focus on advanced medical informatics and encourage the research on methodologies, mechanism discovery, and tool development that enables precision medicine. The topics of this Special Issue include, but are not limited to:

Prediction of novel biomarkers;
Comparative genomics and molecular evolution;
High-performance computing system application;
Data analysis for cancer Genomics;
Identification of novel drug targets;
Genomics markers knowledge discovery;
Statistical models for cancer data analysis;
Multi-omics data integration and analysis;
Deep learning with its applications in modeling diseases;
Computational intelligence techniques in medical image analysis.

Prof. Dr. Leyi Wei
Guest Editor

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Keywords

medical informatics
precision medicine
multi-omics data
deep learning

Published Papers (2 papers)

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Research

13 pages, 19019 KiB

Open AccessArticle

Estimating Metastatic Risk of Pancreatic Ductal Adenocarcinoma at Single-Cell Resolution

by Sina Chen, Shunheng Zhou, Yu-e Huang, Mengqin Yuan, Wanyue Lei, Jiahao Chen, Kongxuan Lin and Wei Jiang

Int. J. Mol. Sci. 2022, 23(23), 15020; https://doi.org/10.3390/ijms232315020 - 30 Nov 2022

Viewed by 1814

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is characterized by intra-tumoral heterogeneity, and patients are always diagnosed after metastasis. Thus, finding out how to effectively estimate metastatic risk underlying PDAC is necessary. In this study, we proposed scMetR to evaluate the metastatic risk of tumor cells based on single-cell RNA sequencing (scRNA-seq) data. First, we identified diverse cell types, including tumor cells and other cell types. Next, we grouped tumor cells into three sub-populations according to scMetR score, including metastasis-featuring tumor cells (MFTC), transitional metastatic tumor cells (TransMTC), and conventional tumor cells (ConvTC). We identified metastatic signature genes (MSGs) through comparing MFTC and ConvTC. Functional enrichment analysis showed that up-regulated MSGs were enriched in multiple metastasis-associated pathways. We also found that patients with high expression of up-regulated MSGs had worse prognosis. Spatial mapping of MFTC showed that they are preferentially located in the cancer and duct epithelium region, which was enriched with the ductal cells’ associated inflammation. Further, we inferred cell–cell interactions, and observed that interactions of the ADGRE5 signaling pathway, which is associated with metastasis, were increased in MFTC compared to other tumor sub-populations. Finally, we predicted 12 candidate drugs that had the potential to reverse expression of MSGs. Taken together, we have proposed scMetR to estimate metastatic risk in PDAC patients at single-cell resolution which might facilitate the dissection of tumor heterogeneity. Full article

(This article belongs to the Special Issue Towards Precision Medicine with Advanced Medical Informatics)

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19 pages, 3783 KiB

Open AccessArticle

Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations

by Asghar Ali Shah, Fahad Alturise, Tamim Alkhalifah and Yaser Daanial Khan

Int. J. Mol. Sci. 2022, 23(19), 11539; https://doi.org/10.3390/ijms231911539 - 29 Sep 2022

Cited by 8 | Viewed by 1866

Abstract

Genes are composed of DNA and each gene has a specific sequence. Recombination or replication within the gene base ends in a permanent change in the nucleotide collection in a DNA called mutation and some mutations can lead to cancer. Breast adenocarcinoma starts in secretary cells. Breast adenocarcinoma is the most common of all cancers that occur in women. According to a survey within the United States of America, there are more than 282,000 breast adenocarcinoma patients registered each 12 months, and most of them are women. Recognition of cancer in its early stages saves many lives. A proposed framework is developed for the early detection of breast adenocarcinoma using an ensemble learning technique with multiple deep learning algorithms, specifically: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bi-directional LSTM. There are 99 types of driver genes involved in breast adenocarcinoma. This study uses a dataset of 4127 samples including men and women taken from more than 12 cohorts of cancer detection institutes. The dataset encompasses a total of 6170 mutations that occur in 99 genes. On these gene sequences, different algorithms are applied for feature extraction. Three types of testing techniques including independent set testing, self-consistency testing, and a 10-fold cross-validation test is applied to validate and test the learning approaches. Subsequently, multiple deep learning approaches such as LSTM, GRU, and bi-directional LSTM algorithms are applied. Several evaluation metrics are enumerated for the validation of results including accuracy, sensitivity, specificity, Mathew’s correlation coefficient, area under the curve, training loss, precision, recall, F1 score, and Cohen’s kappa while the values obtained are 99.57, 99.50, 99.63, 0.99, 1.0, 0.2027, 99.57, 99.57, 99.57, and 99.14 respectively. Full article

(This article belongs to the Special Issue Towards Precision Medicine with Advanced Medical Informatics)

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