Recent Advances of Deep Learning in Biology

Iqbal, Muhammad; Ahmad, Iftikhar; Khan, Tamoor; Khan, Suleman; Ahmad, Muneer; Wang, Lulu

doi:10.1007/978-3-030-77939-9_21

Cited by 5 publications

(3 citation statements)

References 83 publications

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“…Most brain image analysis studies are focused on image data analysis at a single time point, which is prone to interference from different individuals. Longitudinal image data analysis at multiple time points in the time domain can obtain pathological changes in the pathogenesis process and achieve a more precise diagnosis of Alzheimer's disease [26]. Aiming at the above problems, this paper proposes an automatic analysis and diagnosis model of the multi-temporal brain function network based on the deep learning method.…”

Section: The Improved Methods Proposed In This Papermentioning

confidence: 99%

Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network

Sun

2022

IJERPH

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Most current research on Alzheimer’s disease (AD) is based on transverse measurements. Given the nature of neurodegeneration in AD progression, observing longitudinal changes in the structural features of brain networks over time may improve the accuracy of the predicted transformation and provide a good measure of the progression of AD. Currently, there is no cure for patients with existing AD dementia, but patients with mild cognitive impairment (MCI) in the prodromal stage of AD dementia may be diagnosed. The study of the early diagnosis of MCI and the prediction of MCI to AD transformation is of great significance for the monitoring of the MCI to AD transformation process. Despite the high rate of MCI conversion to AD, the neuropathological cause of MCI is heterogeneous. However, many people with MCI remain stable. Treatment options are different for patients with stable MCI and those with underlying dementia. Therefore, it is of great significance for clinical practice to predict whether patients with MCI will develop AD dementia. This paper proposes an improved algorithm that is based on a convolution neural network (CNN) with residuals combined with multi-layer long short-term memory (LSTM) to diagnose AD and predict MCI. Firstly, multi-time resting-state fMRI images were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database for preprocessing, and then an AAL brain partition template was used to construct a 90 × 90 functional connectivity (FC) network matrix of a whole-brain region of interest (ROI). Secondly, the diversity of training samples was increased by generating an adversarial network (GAN). Finally, a CNN with residuals and a multi-layer LSTM model were constructed to automatically classify and predict the functional adjacency matrix. This method can not only distinguish Alzheimer’s disease from normal health conditions at multiple time points, but can also predict progressive MCI (pMCI) and stable MCI (sMCI) at multiple time points. The classification accuracies in AD vs. NC and sMCI vs.pMCI reached 93.5% and 75.5%, respectively.

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Section: The Improved Methods Proposed In This Papermentioning

confidence: 99%

Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network

Sun

2022

IJERPH

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“…Still, the research progress is relatively lagging, mainly due to the lack of large-scale and high-quality medical annotation data. In addition, the current electronic medical record-based tumor-related medical event extraction method, or the use of a large number of rules, either significantly reduces the generalization ability of the extraction method [39,40], or relies highly on pre-training language models [41] and external resources, which increase the demand for computing resources and domain knowledge of the extraction method, hindering the actual application of the extraction method.…”

Section: Related Researchmentioning

confidence: 99%

A Novel Machine-Learning-Based Hybrid CNN Model for Tumor Identification in Medical Image Processing

Juneja¹,

Viriyasitavat

Mohafez

et al. 2022

Sustainability

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The popularization of electronic clinical medical records makes it possible to use automated methods to extract high-value information from medical records quickly. As essential medical information, oncology medical events are composed of attributes that describe malignant tumors. In recent years, oncology medicine event extraction has become a research hotspot in academia. Many academic conferences publish it as an evaluation task and provide a series of high-quality annotation data. This article aims at the characteristics of discrete attributes of tumor-related medical events and proposes a medical event. The standard extraction method realizes the combined extraction of the primary tumor site and primary tumor size characteristics, as well as the extraction of tumor metastasis sites. In addition, given the problems of the small number and types of annotation texts for tumor-related medical events, a key-based approach is proposed. A pseudo-data-generation algorithm that randomly replaces information in the whole domain improves the transfer learning ability of the standard extraction method for different types of tumor-related medical event extractions. The proposed method won third place in the clinical medical event extraction and evaluation task of the CCKS2020 electronic medical record. A large number of experiments on the CCKS2020 dataset verify the effectiveness of the proposed method.

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“…Alternatively, deep learning (DL)-based algorithms have emerged as critical tools for automatically learning complex patterns when the domain is particularly difficult. The use of these models has exponentially grown over the last decade in domains such as bioinformatics and medicine [ 21 , 22 ]), as well as in proteomics in particular. DL applications and their limitations in proteomics are discussed in [ 23 , 24 , 25 ], to cite a few.…”

Section: Introductionmentioning

confidence: 99%

Layer-Wise Relevance Analysis for Motif Recognition in the Activation Pathway of the β2-Adrenergic GPCR Receptor

Gutiérrez-Mondragón

König

Vellido

2023

IJMS

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G-protein-coupled receptors (GPCRs) are cell membrane proteins of relevance as therapeutic targets, and are associated to the development of treatments for illnesses such as diabetes, Alzheimer’s, or even cancer. Therefore, comprehending the underlying mechanisms of the receptor functional properties is of particular interest in pharmacoproteomics and in disease therapy at large. Their interaction with ligands elicits multiple molecular rearrangements all along their structure, inducing activation pathways that distinctly influence the cell response. In this work, we studied GPCR signaling pathways from molecular dynamics simulations as they provide rich information about the dynamic nature of the receptors. We focused on studying the molecular properties of the receptors using deep-learning-based methods. In particular, we designed and trained a one-dimensional convolution neural network and illustrated its use in a classification of conformational states: active, intermediate, or inactive, of the β2-adrenergic receptor when bound to the full agonist BI-167107. Through a novel explainability-oriented investigation of the prediction results, we were able to identify and assess the contribution of individual motifs (residues) influencing a particular activation pathway. Consequently, we contribute a methodology that assists in the elucidation of the underlying mechanisms of receptor activation–deactivation.

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Recent Advances of Deep Learning in Biology

Cited by 5 publications

References 83 publications

Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network

Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network

A Novel Machine-Learning-Based Hybrid CNN Model for Tumor Identification in Medical Image Processing

Layer-Wise Relevance Analysis for Motif Recognition in the Activation Pathway of the β2-Adrenergic GPCR Receptor

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