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3rd Edition of Intelligent Computing in Biology and Medicine
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3rd Edition of Intelligent Computing in Biology and Medicine

A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2358

Special Issue Editor

Prof. Dr. De-Shuang Huang

E-Mail Website
Guest Editor
Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo 315201, China
Interests: bioinformatics; biological image processing; pattern recognition and neural network
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As artificial intelligence (AI) is now a hot research area, intelligent computing technology is also a blooming field. Currently, intelligent computing technology is playing an increasingly important role in helping derive meaningful and logical conclusions in biology and medicine. Understanding the biological and medical data will help in answering important questions on life on Earth and in finding solutions for global health problems, and it can even help in solving problems like drug design and disease diagnosis. The data generated from biology and medicine possess unique properties, such as low-quality data, big data size, different complex formats, high dimensionality, many duplications, high noise, etc. All of these require a special skill set or unique tools for analysis and interpretation. Thus, research using intelligent computing technology on biological and medical data is becoming a very popular topic in the computer science research community.

In this Special Issue focused on intelligent computing in biology and medicine, we invite technical papers in the fields of proteomics, molecular recognition, protein folding, bioinformatics, etc., in relation to intelligent computing technology. The aim of this Special Issue is to assemble a collection of manuscripts that showcase the latest research in the bioinformatics field.

Prof. Dr. De-Shuang Huang
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. Biology is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). 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

  • bioinformatics
  • genome
  • protein
  • omics
  • gene expression
  • interaction
  • disease
  • intelligent computing
  • data integration

Published Papers (3 papers)

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Research

16 pages, 5841 KiB  
Article
PseUpred-ELPSO Is an Ensemble Learning Predictor with Particle Swarm Optimizer for Improving the Prediction of RNA Pseudouridine Sites
by Xiao Wang, Pengfei Li, Rong Wang and Xu Gao
Biology 2024, 13(4), 248; https://doi.org/10.3390/biology13040248 - 08 Apr 2024
Viewed by 539
Abstract
RNA pseudouridine modification exists in different RNA types of many species, and it has a significant role in regulating the expression of biological processes. To understand the functional mechanisms for RNA pseudouridine sites, the accurate identification of pseudouridine sites in RNA sequences is [...] Read more.
RNA pseudouridine modification exists in different RNA types of many species, and it has a significant role in regulating the expression of biological processes. To understand the functional mechanisms for RNA pseudouridine sites, the accurate identification of pseudouridine sites in RNA sequences is essential. Although several fast and inexpensive computational methods have been proposed, the challenge of improving recognition accuracy and generalization still exists. This study proposed a novel ensemble predictor called PseUpred-ELPSO for improved RNA pseudouridine site prediction. After analyzing the nucleotide composition preferences between RNA pseudouridine site sequences, two feature representations were determined and fed into the stacking ensemble framework. Then, using five tree-based machine learning classifiers as base classifiers, 30-dimensional RNA profiles are constructed to represent RNA sequences, and using the PSO algorithm, the weights of the RNA profiles were searched to further enhance the representation. A logistic regression classifier was used as a meta-classifier to complete the final predictions. Compared to the most advanced predictors, the performance of PseUpred-ELPSO is superior in both cross-validation and the independent test. Based on the PseUpred-ELPSO predictor, a free and easy-to-operate web server has been established, which will be a powerful tool for pseudouridine site identification. Full article
(This article belongs to the Special Issue 3rd Edition of Intelligent Computing in Biology and Medicine)
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20 pages, 2560 KiB  
Article
Unified Convolutional Sparse Transformer for Disease Diagnosis, Monitoring, Drug Development, and Therapeutic Effect Prediction from EEG Raw Data
by Zhengda He, Linjie Chen, Jiaying Xu, Hao Lv, Rui-ning Zhou, Jianhua Hu, Yadong Chen and Yang Gao
Biology 2024, 13(4), 203; https://doi.org/10.3390/biology13040203 - 22 Mar 2024
Viewed by 719
Abstract
Electroencephalogram (EEG) analysis plays an indispensable role across contemporary medical applications, which encompasses diagnosis, monitoring, drug discovery, and therapeutic assessment. This work puts forth an end-to-end deep learning framework that is uniquely tailored for versatile EEG analysis tasks by directly operating on raw [...] Read more.
Electroencephalogram (EEG) analysis plays an indispensable role across contemporary medical applications, which encompasses diagnosis, monitoring, drug discovery, and therapeutic assessment. This work puts forth an end-to-end deep learning framework that is uniquely tailored for versatile EEG analysis tasks by directly operating on raw waveform inputs. It aims to address the challenges of manual feature engineering and the neglect of spatial interrelationships in existing methodologies. Specifically, a spatial channel attention module is introduced to emphasize the critical inter-channel dependencies in EEG signals through channel statistics aggregation and multi-layer perceptron operations. Furthermore, a sparse transformer encoder is used to leverage selective sparse attention in order to efficiently process long EEG sequences while reducing computational complexity. Distilling convolutional layers further concatenates the temporal features and retains only the salient patterns. As it was rigorously evaluated on key EEG datasets, our model consistently accomplished a superior performance over the current approaches in detection and classification assignments. By accounting for both spatial and temporal relationships in an end-to-end paradigm, this work facilitates a versatile, automated EEG understanding across diseases, subjects, and objectives through a singular yet customizable architecture. Extensive empirical validation and further architectural refinement may promote broader clinical adoption prospects. Full article
(This article belongs to the Special Issue 3rd Edition of Intelligent Computing in Biology and Medicine)
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16 pages, 2998 KiB  
Article
Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields
by Pi-Jing Wei, An-Dong Zhu, Ruifen Cao and Chunhou Zheng
Biology 2024, 13(3), 184; https://doi.org/10.3390/biology13030184 - 14 Mar 2024
Viewed by 889
Abstract
Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with high heterogeneity. Thus, the discovery [...] Read more.
Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with high heterogeneity. Thus, the discovery of driver genes at the individual level is becoming more valuable but is a great challenge. Although there have been some computational methods proposed to tackle this challenge, few can cover all patient samples well, and there is still room for performance improvement. In this study, to identify individual-level driver genes more efficiently, we propose the PDGCN method. PDGCN integrates multiple types of data features, including mutation, expression, methylation, copy number data, and system-level gene features, along with network structural features extracted using Node2vec in order to construct a sample–gene interaction network. Prediction is performed using a graphical convolutional neural network model with a conditional random field layer, which is able to better combine the network structural features with biological attribute features. Experiments on the ACC (Adrenocortical Cancer) and KICH (Kidney Chromophobe) datasets from TCGA (The Cancer Genome Atlas) demonstrated that the method performs better compared to other similar methods. It can identify not only frequently mutated driver genes, but also rare candidate driver genes and novel biomarker genes. The results of the survival and enrichment analyses of these detected genes demonstrate that the method can identify important driver genes at the individual level. Full article
(This article belongs to the Special Issue 3rd Edition of Intelligent Computing in Biology and Medicine)
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