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Advances in Computational Neuroscience
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Advances in Computational Neuroscience

A special issue of Current Issues in Molecular Biology (ISSN 1467-3045). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 3119

Special Issue Editor

Dr. Siwei Zhang

E-Mail Website
Guest Editor
Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
Interests: neurobiology; developmental biolog; cell biology; functional genomics; mental disorders
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development in deep-learning algorithms, artificial neural networks, and a deeper understanding of mental disorders at the neuronal level, we have witnessed the giant advancement of computational neuroscience in recent years, including computational methodologies, biological traits, and even the artificial-intelligence-based theories of mind. Such advancement has been especially prominent within the field of memory and mental-related research, in which the combination of the personalized genome, optogenetics, and in silico simulation of neural networks within brain regions made it possible to interpret and model higher neural functionalities.

Here, we invite researchers from all spectra and disciplines of computational neuroscience to contribute their investigation outputs to this Special Issue, including, but not limited to, synaptic modeling and regulation, neural network patterning and topological analysis, EEG/fMRI-based imaging observations and functional brain mass modeling, information theory, and other multidisciplinary background studies.

We sincerely look forward to receiving your valuable contributions.

Dr. Siwei Zhang
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. Current Issues in Molecular 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 2200 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

  • computational neuroscience
  • neural networks
  • in silico neuronal circuits
  • signal modulation
  • synapse plasticity
  • deep learning
  • mental simulation
  • mind theory
  • complex system
  • convolutional neural network (CNN)

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Published Papers (1 paper)

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Research

17 pages, 3898 KiB  
Article
MIFNN: Molecular Information Feature Extraction and Fusion Deep Neural Network for Screening Potential Drugs
by Jingjing Wang, Hongzhen Li, Wenhan Zhao, Tinglin Pang, Zengzhao Sun, Bo Zhang and Huaqiang Xu
Curr. Issues Mol. Biol. 2022, 44(11), 5638-5654; https://doi.org/10.3390/cimb44110382 - 13 Nov 2022
Viewed by 2720
Abstract
Molecular property prediction is essential for drug screening and reducing the cost of drug discovery. Current approaches combined with deep learning for drug prediction have proven their viability. Based on the previous deep learning networks, we propose the Molecular Information Fusion Neural Network [...] Read more.
Molecular property prediction is essential for drug screening and reducing the cost of drug discovery. Current approaches combined with deep learning for drug prediction have proven their viability. Based on the previous deep learning networks, we propose the Molecular Information Fusion Neural Network (MIFNN). The features of MIFNN are as follows: (1) we extracted directed molecular information using 1D-CNN and the Morgan fingerprint using 2D-CNN to obtain more comprehensive feature information; (2) we fused two molecular features from one-dimensional and two-dimensional space, and we used the directed message-passing method to reduce the repeated collection of information and improve efficiency; (3) we used a bidirectional long short-term memory and attention module to adjust the molecular feature information and improve classification accuracy; (4) we used the particle swarm optimization algorithm to improve the traditional support vector machine. We tested the performance of the model on eight publicly available datasets. In addition to comparing the overall classification capability with the baseline model, we conducted a series of ablation experiments to verify the optimization of different modules in the model. Compared with the baseline model, our model achieved a maximum improvement of 14% on the ToxCast dataset. The performance was very stable on most datasets. On the basis of the current experimental results, MIFNN performed better than previous models on the datasets applied in this paper. Full article
(This article belongs to the Special Issue Advances in Computational Neuroscience)
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