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Algorithms
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Advanced Research on Machine Learning Algorithms in Bioinformatics
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Advanced Research on Machine Learning Algorithms in Bioinformatics

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 4453

Special Issue Editors

Prof. Dr. Carla Piazza

E-Mail Website
Guest Editor
Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy
Interests: systems biology; hybrid automata; model checking; information flow security
Special Issues, Collections and Topics in MDPI journals
Dr. Roberto Pagliarini

E-Mail Website
Guest Editor Assistant
Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy
Interests: systems biology; computational biology; mathematical modelling

Special Issue Information

Dear colleagues,

Epigenetic variation and, more generally, somatic mutations represent molecular components of biodiversity that directly link the genome to the environment. Recently, epigenetics emerged as a promising aspect for the diagnosis of several disorders. It could become an opportunity to uncover new mechanisms as well as therapeutic targets for cancer and analyze their links with metabolic dysregulation. The application of machine learning and automated reasoning techniques to mutational studies composed of huge amounts of multi-omics data could significantly boost discovery and therapy development. For these reasons, we invite you to submit your latest research related to the development and application of artificial intelligence methods to this kind of problem to this Special Issue. It will focus on algorithms in the following areas:

  • Epigenomic and multi-omics data clustering;
  • Computational approaches to modeling and optimizing cancer treatment;
  • Patient-specific integrated network modeling;
  • Single-cell analysis in cancer genomics and epigenomics;
  • Modeling the evolutionary dynamics of cancer: from epigenetic regulation to cell population dynamics.

Prof. Dr. Carla Piazza
Guest Editor

Dr. Roberto Pagliarini
Guest Editor Assistant

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. Algorithms 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 1600 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

  • artificial intelligence
  • computational biology
  • machine learning
  • genomics
  • data clustering

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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22 pages, 18158 KiB  
Article
A Novel Model for Noninvasive Haemoglobin Detection Based on Visibility Network and Clustering Network for Multi-Wavelength PPG Signals
by Lei Liu, Ziyi Wang, Xiaohan Zhang, Yan Zhuang and Yongbo Liang
Algorithms 2025, 18(2), 75; https://doi.org/10.3390/a18020075 (registering DOI) - 1 Feb 2025
Viewed by 234
Abstract
Non-invasive haemoglobin (Hb) testing devices enable large-scale haemoglobin screening, but their accuracy is not comparable to traditional blood tests. To this end, this paper aims to design a non-invasive haemoglobin testing device and propose a classification-regression prediction framework for non-invasive testing of haemoglobin [...] Read more.
Non-invasive haemoglobin (Hb) testing devices enable large-scale haemoglobin screening, but their accuracy is not comparable to traditional blood tests. To this end, this paper aims to design a non-invasive haemoglobin testing device and propose a classification-regression prediction framework for non-invasive testing of haemoglobin using visibility graphs (VG) with network clustering of multi-sample pulse-wave-weighted undirected graphs as the features to optimize the detection accuracy of non-invasive haemoglobin measurements. Different prediction methods were compared by analyzing 608 segments of multiwavelength fingertip PPG signal data from 152 volunteers and analyzing and comparing the data and methods. The results showed that the classification using NVG with complex network clustering as features in the interval classification model was the best, with its classification accuracy (acc) of 93.35% and model accuracy of 88.28%. Among the regression models, the classification regression stack: SVM-Light Gradient Boosting Machine (LGBM) was the most effective, with a Mean Absolute Error (MAE) of 6.67 g/L, a Root Mean Square Error (RMSE) of 8.21 g/L, and an R-Square (R2) of 0.64. The results of this study indicate that the use of complex network technology in non-invasive haemoglobin detection can effectively improve its accuracy, and the detector designed in this study is promising to carry out a more accurate large-scale haemoglobin screening. Full article
(This article belongs to the Special Issue Advanced Research on Machine Learning Algorithms in Bioinformatics)
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15 pages, 1605 KiB  
Article
CSpredR: A Multi-Site mRNA Subcellular Localization Prediction Method Based on Fusion Encoding and Hybrid Neural Networks
by Xiao Wang, Wenshuai Suo and Rong Wang
Algorithms 2025, 18(2), 67; https://doi.org/10.3390/a18020067 - 26 Jan 2025
Viewed by 331
Abstract
Current research widely acknowledges that the subcellular localization of mRNA is crucial for understanding its biological functions. However, current methods for mRNA subcellular localization based on k-mer frequency features may overlook the sequential information of the sequence, and a single encoding method may [...] Read more.
Current research widely acknowledges that the subcellular localization of mRNA is crucial for understanding its biological functions. However, current methods for mRNA subcellular localization based on k-mer frequency features may overlook the sequential information of the sequence, and a single encoding method may not adequately extract the sequence’s features. This paper proposes a novel deep learning prediction method, CSpredR, specifically designed for predicting the subcellular localization of multi-site mRNAs. Unlike previous methods, CSpredR first employs k-mer to tokenize the mRNA sequences, then converts the tokenized sequences into de Bruijn graphs, thereby enabling a more precise capture of the structural information within the sequences. To mitigate the impact of lost sequential information and better capture sequence features, we combine word2vec and fasttext models to extract the features of each node in the graph and retain the sequence order. They can encode the k-mer units in the sequence into word vectors, thus serving as the node feature vectors of the graph. In this way, each node in the graph is assigned a feature vector containing rich semantic information. Subsequently, we utilize multi-scale convolutional neural networks and bidirectional long short-term memory networks to capture sequence features, respectively, and fuse the results as input for a multi-head attention mechanism model. The information from these heads is integrated into the node representations, and finally, the attention-processed data are fed into an MLP (Multi-Layer Perceptron) for prediction tasks. Extensive experiments reveal that CSpredR achieves a 2% improvement over the best existing predictors, offering a more effective tool for mRNA subcellular localization prediction. Full article
(This article belongs to the Special Issue Advanced Research on Machine Learning Algorithms in Bioinformatics)
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17 pages, 2444 KiB  
Article
Three-Way Alignment Improves Multiple Sequence Alignment of Highly Diverged Sequences
by Mahbubeh Askari Rad, Alibek Kruglikov and Xuhua Xia
Algorithms 2024, 17(5), 205; https://doi.org/10.3390/a17050205 - 10 May 2024
Cited by 1 | Viewed by 2951
Abstract
The standard approach for constructing a phylogenetic tree from a set of sequences consists of two key stages. First, a multiple sequence alignment (MSA) of the sequences is computed. The aligned data are then used to reconstruct the phylogenetic tree. The accuracy of [...] Read more.
The standard approach for constructing a phylogenetic tree from a set of sequences consists of two key stages. First, a multiple sequence alignment (MSA) of the sequences is computed. The aligned data are then used to reconstruct the phylogenetic tree. The accuracy of the resulting tree heavily relies on the quality of the MSA. The quality of the popularly used progressive sequence alignment depends on a guide tree, which determines the order of aligning sequences. Most MSA methods use pairwise comparisons to generate a distance matrix and reconstruct the guide tree. However, when dealing with highly diverged sequences, constructing a good guide tree is challenging. In this work, we propose an alternative approach using three-way dynamic programming alignment to generate the distance matrix and the guide tree. This three-way alignment incorporates information from additional sequences to compute evolutionary distances more accurately. Using simulated datasets on two symmetric and asymmetric trees, we compared MAFFT with its default guide tree with MAFFT with a guide tree produced using the three-way alignment. We found that (1) the three-way alignment can reconstruct better guide trees than those from the most accurate options of MAFFT, and (2) the better guide tree, on average, leads to more accurate phylogenetic reconstruction. However, the improvement over the L-INS-i option of MAFFT is small, attesting to the excellence of the alignment quality of MAFFT. Surprisingly, the two criteria for choosing the best MSA (phylogenetic accuracy and sum-of-pair score) conflict with each other. Full article
(This article belongs to the Special Issue Advanced Research on Machine Learning Algorithms in Bioinformatics)
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