Computational and Mathematical Methods in Medicine

Soft Computing for Analysis of Biomedical Data


Status
Published

Lead Editor

1Pablo de Olavide University, Seville, Spain

2Memorial University of Newfoundland, St. John’s, Canada

3National University of Asuncion, San Lorenzo, Paraguay


Soft Computing for Analysis of Biomedical Data

Description

Soft computing (SC) techniques can be used to tackle problems characterized by imprecision, uncertainty, and partial truth to achieve tractability and robustness at a low computational cost.

These features represent the main differences between SC and hard computing techniques and provide SC strategies with the ability to deal with ambiguous situations like imprecision and uncertainty. For this reason, SC techniques can obtain approximate solutions to problems which have no known methods to compute an exact solution. The main SC paradigms include fuzzy systems, evolutionary computation, artificial neural computing, metaheuristics, and swarm intelligence.

Those features render SC particularly suitable for analyzing medical data, which is typically characterized by imprecision and the presence of noise. Moreover, SC techniques allow easily integrating human knowledge, which can help achieve better solutions. Biomedical data may be of different nature: texts, images, signals, and so forth, which typically contain a high presence of noise.

The overall aim of this special issue is to compile the latest research and development, up-to-date issues, and challenges in the field of SC and its applications to biomedical data.

Potential topics include but are not limited to the following:

  • Medical imaging, signal processing, and text analysis
  • Data mining medical data and records
  • Clinical expert systems
  • Modelling and simulation of medical processes
  • Drug description analysis
  • Patient-centric care
  • Rational drug design and personalized medicine
  • Biomedical text/data mining and visualization
  • Network biology/medicine
  • Interpreting genomic or metagenomic data
  • Gene expression analysis
  • Discovering regulatory or expression pathways
  • Modeling ecosystems or population dynamics
  • Discovering genome-disease or genome-phenotype associations
  • Omics data analysis and functional genomics for complex diseases
  • Gene-gene interactions and gene-environment interactions for disease association
  • analysis
  • Protein structure prediction
  • Phylogenetics
  • Assembling next generation sequence data

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 3902484
  • - Editorial

Soft Computing for Analysis of Biomedical Data

Federico Divina | Miguel García-Torres | ... | Christian E. Schaerer
  • Special Issue
  • - Volume 2018
  • - Article ID 3146873
  • - Research Article

Cosine Similarity Measure between Hybrid Intuitionistic Fuzzy Sets and Its Application in Medical Diagnosis

Donghai Liu | Xiaohong Chen | Dan Peng
  • Special Issue
  • - Volume 2018
  • - Article ID 6708520
  • - Research Article

Potential Genes and Pathways of Neonatal Sepsis Based on Functional Gene Set Enrichment Analyses

YuXiu Meng | Xue Hong Cai | LiPei Wang
  • Special Issue
  • - Volume 2018
  • - Article ID 2497471
  • - Research Article

Biomedical Text Categorization Based on Ensemble Pruning and Optimized Topic Modelling

Aytuğ Onan
  • Special Issue
  • - Volume 2018
  • - Article ID 7207151
  • - Research Article

Analysis and Study of Diabetes Follow-Up Data Using a Data-Mining-Based Approach in New Urban Area of Urumqi, Xinjiang, China, 2016-2017

Yukai Li | Huling Li | Hua Yao
  • Special Issue
  • - Volume 2018
  • - Article ID 3018356
  • - Research Article

Exploration of Neural Activity under Cognitive Reappraisal Using Simultaneous EEG-fMRI Data and Kernel Canonical Correlation Analysis

Biao Yang | Jinmeng Cao | ... | Jianbo Xiang
  • Special Issue
  • - Volume 2018
  • - Article ID 9674108
  • - Research Article

Structure Optimization for Large Gene Networks Based on Greedy Strategy

Francisco Gómez-Vela | Domingo S. Rodriguez-Baena | José Luis Vázquez-Noguera
Computational and Mathematical Methods in Medicine
 Journal metrics
Acceptance rate32%
Submission to final decision46 days
Acceptance to publication39 days
CiteScore3.400
Impact Factor1.770
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