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Towards Precision Medicine with Advanced Medical Informatics
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Towards Precision Medicine with Advanced Medical Informatics

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 5360

Special Issue Editor

Prof. Dr. Leyi Wei

grade E-Mail Website
Guest Editor
School of Software, Shandong University, Jinan 250300, China
Interests: bioinformatics; machine learning; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • medical informatics
  • precision medicine
  • multi-omics data
  • deep learning

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Published Papers (2 papers)

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Research

13 pages, 19019 KiB  
Article
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
Cited by 2 | Viewed by 2421
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 [...] Read more.
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  
Article
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 14 | Viewed by 2290
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 [...] Read more.
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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