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Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, China
College of Artificial Intelligence, Nankai University, Tianjin 300350, China
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
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Bioinformatics and Intelligent Information Processing

Abstract submission deadline
closed (31 July 2023)
Manuscript submission deadline
closed (30 November 2023)
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3293

Topic Information

Dear Colleagues,

The 2023 Bioinformatics and Intelligent Information Processing Conference (BIIP2023), the annual conference of the Bioinformatics and Artificial Life Committee of the Chinese Association for Artificial Intelligence (CAAI), will be held in Jinan City, Shandong Province, China, from June 18th to June 20th, 2023. The conference is organized by the CAAI and hosted by the Bioinformatics and Artificial Life Committee of CAAI and the School of Control Science and Engineering of Shandong University. Under the current breakthroughs in large language models of AI, it is of great significance and value to discuss how to use new AI technologies to promote biomedical research. BIIP2023 aims to build such a platform for scientists in related fields. The conference will invite many distinguished experts and scholars in the fields of AI, life science, and medical science to give talks and run tutorials. In addition, sessions will also be set up for talks about the latest research progress and trends of interesting topics. The topic collection plans to present novel and advanced interdisciplinary research achievements in bioinformatics and intelligent information processing. We warmly welcome scholars in the related fields to submit their works to the journals involved in this topic collection. The topics include but are not limited to the following areas:

S1: Self-organization phenomena and mechanisms in natural and human-made systems

S2: Bioanalysis and intelligent processing algorithms

S3: Biological multi-omics data analysis

S4: Biological networks and systems biology

S5: Intelligent drug design

S6: Precision medicine and big data

S7: Biological and health big data analytics

S8: Biomedical image analysis

S9: Digital diagnosis and smart health

S10: Bioinformatics foundations of the brain and brain-like intelligence

S11: Artificial life systems and synthetic biology

S12: Artificial life and artificial intelligence

S13: Digital-based life and intelligent health

S14: Intelligent computing for digital-based life

S15: Other related fields

Prof. Dr. Zhiping Liu
Prof. Dr. Han Zhang
Prof. Dr. Junwei Han
Topic Editors

Keywords

  • bioinformatics
  • artificial intelligence
  • intelligent information processing
  • artificial life
  • models and algorithms
  • systems and simulators
  • systems biology
  • biomedical big data
  • large language models

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
AI
ai 		- - 2020 21.8 Days CHF 1200
Entropy
entropy 		2.7 4.7 1999 20.4 Days CHF 2600
Genes
genes 		3.5 5.1 2010 17.9 Days CHF 2600
International Journal of Molecular Sciences
ijms 		5.6 7.8 2000 16.8 Days CHF 2900
Machine Learning and Knowledge Extraction
make 		3.9 8.5 2019 19.2 Days CHF 1400

Preprints is a platform dedicated to making early versions of research outputs permanently available and citable. MDPI journals allow posting on preprint servers such as Preprints.org prior to publication. For more details about reprints, please visit https://www.preprints.org.

Published Papers (3 papers)

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18 pages, 2565 KiB  
Article
Enhancing Electrocardiogram (ECG) Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification
by
Amnon Bleich
,
Antje Linnemann
,
Benjamin Jaidi
,
Björn H. Diem
and
Tim O. F. Conrad
Mach. Learn. Knowl. Extr. 2023, 5(4), 1539-1556; https://doi.org/10.3390/make5040077 - 21 Oct 2023
Viewed by 714
Abstract
Implantable Cardiac Monitor (ICM) devices are demonstrating, as of today, the fastest-growing market for implantable cardiac devices. As such, they are becoming increasingly common in patients for measuring heart electrical activity. ICMs constantly monitor and record a patient’s heart rhythm, and when triggered, [...] Read more.
Implantable Cardiac Monitor (ICM) devices are demonstrating, as of today, the fastest-growing market for implantable cardiac devices. As such, they are becoming increasingly common in patients for measuring heart electrical activity. ICMs constantly monitor and record a patient’s heart rhythm, and when triggered, send it to a secure server where health care professionals (HCPs) can review it. These devices employ a relatively simplistic rule-based algorithm (due to energy consumption constraints) to make alerts for abnormal heart rhythms. This algorithm is usually parameterized to an over-sensitive mode in order to not miss a case (resulting in a relatively high false-positive rate), and this, combined with the device’s nature of constantly monitoring the heart rhythm and its growing popularity, results in HCPs having to analyze and diagnose an increasingly growing number of data. In order to reduce the load on the latter, automated methods for ECG analysis are nowadays becoming a great tool to assist HCPs in their analysis. While state-of-the-art algorithms are data-driven rather than rule-based, training data for ICMs often consist of specific characteristics that make their analysis unique and particularly challenging. This study presents the challenges and solutions in automatically analyzing ICM data and introduces a method for its classification that outperforms existing methods on such data. It carries this out by combining high-frequency noise detection (which often occurs in ICM data) with a semi-supervised learning pipeline that allows for the re-labeling of training episodes and by using segmentation and dimension-reduction techniques that are robust to morphology variations of the sECG signal (which are typical to ICM data). As a result, it performs better than state-of-the-art techniques on such data with, e.g., an F1 score of 0.51 vs. 0.38 of our baseline state-of-the-art technique in correctly calling atrial fibrillation in ICM data. As such, it could be used in numerous ways, such as aiding HCPs in the analysis of ECGs originating from ICMs by, e.g., suggesting a rhythm type. Full article
(This article belongs to the Topic Bioinformatics and Intelligent Information Processing)
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14 pages, 2920 KiB  
Article
A Comprehensive Self-Resistance Gene Database for Natural-Product Discovery with an Application to Marine Bacterial Genome Mining
by
Hua Dong
and
Dengming Ming
Int. J. Mol. Sci. 2023, 24(15), 12446; https://doi.org/10.3390/ijms241512446 - 04 Aug 2023
Viewed by 497
Abstract
In the world of microorganisms, the biosynthesis of natural products in secondary metabolism and the self-resistance of the host always occur together and complement each other. Identifying resistance genes from biosynthetic gene clusters (BGCs) helps us understand the self-defense mechanism and predict the [...] Read more.
In the world of microorganisms, the biosynthesis of natural products in secondary metabolism and the self-resistance of the host always occur together and complement each other. Identifying resistance genes from biosynthetic gene clusters (BGCs) helps us understand the self-defense mechanism and predict the biological activity of natural products synthesized by microorganisms. However, a comprehensive database of resistance genes is still lacking, which hinders natural product annotation studies in large-scale genome mining. In this study, we compiled a resistance gene database (RGDB) by scanning the four available databases: CARD, MIBiG, NCBIAMR, and UniProt. Every resistance gene in the database was annotated with resistance mechanisms and possibly involved chemical compounds, using manual annotation and transformation from the resource databases. The RGDB was applied to analyze resistance genes in 7432 BGCs in 1390 genomes from a marine microbiome project. Our calculation showed that the RGDB successfully identified resistance genes for more than half of the BGCs, suggesting that the database helps prioritize BGCs that produce biologically active natural products. Full article
(This article belongs to the Topic Bioinformatics and Intelligent Information Processing)
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16 pages, 1733 KiB  
Article
Search for Dispersed Repeats in Bacterial Genomes Using an Iterative Procedure
by
Eugene Korotkov
,
Yulia Suvorova
,
Dimitry Kostenko
and
Maria Korotkova
Int. J. Mol. Sci. 2023, 24(13), 10964; https://doi.org/10.3390/ijms241310964 - 30 Jun 2023
Cited by 1 | Viewed by 1169
Abstract
We have developed a de novo method for the identification of dispersed repeats based on the use of random position-weight matrices (PWMs) and an iterative procedure (IP). The created algorithm (IP method) allows detection of dispersed repeats for which the average number of [...] Read more.
We have developed a de novo method for the identification of dispersed repeats based on the use of random position-weight matrices (PWMs) and an iterative procedure (IP). The created algorithm (IP method) allows detection of dispersed repeats for which the average number of substitutions between any two repeats per nucleotide (x) is less than or equal to 1.5. We have shown that all previously developed methods and algorithms (RED, RECON, and some others) can only find dispersed repeats for x ≤ 1.0. We applied the IP method to find dispersed repeats in the genomes of E. coli and nine other bacterial species. We identify three families of approximately 1.09 × 106, 0.64 × 106, and 0.58 × 106 DNA bases, respectively, constituting almost 50% of the complete E. coli genome. The length of the repeats is in the range of 400 to 600 bp. Other analyzed bacterial genomes contain one to three families of dispersed repeats with a total number of 103 to 6 × 103 copies. The existence of such highly divergent repeats could be associated with the presence of a single-type triplet periodicity in various genes or with the packing of bacterial DNA into a nucleoid. Full article
(This article belongs to the Topic Bioinformatics and Intelligent Information Processing)
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