The Advances and Challenges of Deep Learning Application in Biological Big Data Processing

Li, Peng; Peng, Manman; Liao, Bo; Huang, Guohua; Li, Weibiao; Dingfeng, Xie

doi:10.2174/1574893612666170707095707

Cited by 102 publications

(49 citation statements)

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“…Based on this consideration, we tried to describe the protein fragment with features as concise as possible to make the predictor simpler and more efficient. After a series of experiments, we selected two types of encoding schemes to represent the protein fragment: one-hot code [28][29][30] and a position-specific scoring matrix (PSSM), where the former one encodes the residues arrangement and the latter one reflect the evolutionary profile. However, reducing the number of features will inevitably result in less information that may get by the predictor and cause performance deterioration.…”

Section: Methodsmentioning

confidence: 99%

TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues

et al. 2019

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Transmembrane proteins (TMPs) play vital and diverse roles in many biological processes, such as molecular transportation and immune response. Like other proteins, many major interactions with other molecules happen in TMPs' surface area, which is important for function annotation and drug discovery. Under the condition that the structure of TMP is hard to derive from experiment and prediction, it is a practical way to predict the TMP residues' surface area, measured by the relative accessible surface area (rASA), based on computational methods. In this study, we presented a novel deep learning-based predictor TMP-SSurface for both alpha-helical and beta-barrel transmembrane proteins (α-TMP and β-TMP), where convolutional neural network (CNN), inception blocks, and CapsuleNet were combined to construct a network framework, simply accepting one-hot code and position-specific score matrix (PSSM) of protein fragment as inputs. TMP-SSurface was tested against an independent dataset achieving appreciable performance with 0.584 Pearson correlation coefficients (CC) value. As the first TMP's rASA predictor utilizing the deep neural network, our method provided a referenceable sample for the community, as well as a practical step to discover the interaction sites of TMPs based on their sequence.

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Section: Methodsmentioning

confidence: 99%

TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues

et al. 2019

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“…Also, it has been shown that some modified and improved versions of the present approaches such as deep neural networks can yield better predictive models [16]. Overfitting and insufficient amounts of data are the main challenges in generating an appropriate predictive model [17,18].…”

Section: -Backgroundmentioning

confidence: 99%

Synthetic Repurposing of Drugs in Hypertension: a Datamining Method Based on Association Rules and a Novel Discrete Algorithm

Masoudi-Sobhanzadeh

Masoudi‐Nejad²

2020

Preprint

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Background Drug repurposing aims to detect the new benefits of the existing drugs and to reduce the spent time and cost of the drug development projects. Although synthetic repurposing of drugs may be more useful than single repurposing in terms of reducing toxicity and enhancing efficacy, the researchers have not taken it into account. To address the issue, a novel datamining method is introduced and applied to the repositioning of drugs in hypertension (HT), which is a serious medical condition and therefore needs to be dealt with effectively through making some improved treatment plans to help cure it. Results a novel two-step data mining method, which is based on the If-Then association rules and a novel discrete optimization algorithm, is proposed and applied to the synthetic repurposing of drugs in HT. The required data are extracted from DruhBank, KEGG, and DrugR+ databases. The outcomes presented that the proposed method outperforms the other state-of-the-art approaches in terms of different statistical criteria. In contrast to the previously proposed methods which failed to discover a list for some datasets, our method managed to suggest a combination of drugs for all the datasets. Conclusion The proposed synthetic method may revive some failed drug development projects and be a suitable plan for curing orphan and rare diseases due to using a low dosage of medicines. It is also essential to use some efficient computational methods to produce better results.

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“…As a representative branch of deep learning, CNN [34] had already made great achievements in various research fields, including protein sequence studies [35][36][37][38][39]. It can capture various nonlinear features by constructing neural networks consisting of convolution, pooling, and fully connected layers.…”

Section: Deep Learning Networkmentioning

confidence: 99%

IMPContact: An Interhelical Residue Contact Prediction Method

Fang

Jia

et al. 2020

BioMed Research International

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As an important category of proteins, alpha-helix transmembrane proteins (αTMPs) play an important role in various biological activities. Because the solved αTMP structures are inadequate, predicting the residue contacts among the transmembrane segments of an αTMP exhibits the basis of protein fold, which can be used to further discover more protein functions. A few efforts have been devoted to predict the interhelical residue contact using machine learning methods based on the prior knowledge of transmembrane protein structure. However, it is still a challenge to improve the prediction accuracy, while the deep learning method provides an opportunity to utilize the structural knowledge in a different insight. For this purpose, we proposed a novel αTMP residue-residue contact prediction method IMPContact, in which a convolutional neural network (CNN) was applied to recognize those interhelical contacts in a TMP using its specific structural features. There were four sequence-based TMP-specific features selected to descript a pair of residues, namely, evolutionary covariation, predicted topology structure, residue relative position, and evolutionary conservation. An up-to-date dataset was used to train and test the IMPContact; our method achieved better performance compared to peer methods. In the case studies, IHRCs in the regular transmembrane helixes were better predicted than in the irregular ones.

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The Advances and Challenges of Deep Learning Application in Biological Big Data Processing

Cited by 102 publications

References 0 publications

TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues

TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues

Synthetic Repurposing of Drugs in Hypertension: a Datamining Method Based on Association Rules and a Novel Discrete Algorithm

IMPContact: An Interhelical Residue Contact Prediction Method

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