ProtDec-LTR2.0: an improved method for protein remote homology detection by combining pseudo protein and supervised Learning to Rank

Chen, Junjie; Guo, Mingyue; Li, Shumin; Liu, Bin

doi:10.1093/bioinformatics/btx429

Cited by 37 publications

(15 citation statements)

References 21 publications

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“…Unsupervised learning, also known as clustering, adopts k-means to establish a centriole and reduce error through iteration and descent to achieve classification. With the development of ML, more and more fields have begun to use this technique for research (Chen W. et al, 2016, Chen et al, 2017a,d, 2018a,b,e,f,g; Li et al, 2016; Zou et al, 2016, 2017; Ding et al, 2017a,b; Feng et al, 2017a; Yu et al, 2017a; Zeng et al, 2017a, 2018; Liu et al, 2018; Pan et al, 2018; Wei et al, 2018a,b; Yang et al, 2018a; Zhao et al, 2018b; He et al, 2019; Zhang et al, 2019), for example, drug repositioning (Yu et al, 2016b, 2017b), disease-related microRNA (Chen and Huang, 2017; Chen et al, 2017d, 2018b,e,g; Zhao et al, 2018a,c) identification, and disease-related long non-coding RNA identification (Chen and Yan, 2013; Chen et al, 2017e, 2018c; Hu et al, 2017, 2018). There are four main steps in developing ML algorithms (Oudah and Henschel, 2018).…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Application of Machine Learning in Microbiology

et al. 2019

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Microorganisms are ubiquitous and closely related to people’s daily lives. Since they were first discovered in the 19th century, researchers have shown great interest in microorganisms. People studied microorganisms through cultivation, but this method is expensive and time consuming. However, the cultivation method cannot keep a pace with the development of high-throughput sequencing technology. To deal with this problem, machine learning (ML) methods have been widely applied to the field of microbiology. Literature reviews have shown that ML can be used in many aspects of microbiology research, especially classification problems, and for exploring the interaction between microorganisms and the surrounding environment. In this study, we summarize the application of ML in microbiology.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Application of Machine Learning in Microbiology

et al. 2019

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“…Feature extraction is a key step for constructing a predictor [ 53 , 54 , 55 , 56 , 57 ]. The construction of IDP–CRF is based on transition and state features.…”

Section: Methodsmentioning

confidence: 99%

IDP–CRF: Intrinsically Disordered Protein/Region Identification Based on Conditional Random Fields

Liu

Wang

Liu

2018

IJMS

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Accurate prediction of intrinsically disordered proteins/regions is one of the most important tasks in bioinformatics, and some computational predictors have been proposed to solve this problem. How to efficiently incorporate the sequence-order effect is critical for constructing an accurate predictor because disordered region distributions show global sequence patterns. In order to capture these sequence patterns, several sequence labelling models have been applied to this field, such as conditional random fields (CRFs). However, these methods suffer from certain disadvantages. In this study, we proposed a new computational predictor called IDP–CRF, which is trained on an updated benchmark dataset based on the MobiDB database and the DisProt database, and incorporates more comprehensive sequence-based features, including PSSMs (position-specific scoring matrices), kmer, predicted secondary structures, and relative solvent accessibilities. Experimental results on the benchmark dataset and two independent datasets show that IDP–CRF outperforms 25 existing state-of-the-art methods in this field, demonstrating that IDP–CRF is a very useful tool for identifying IDPs/IDRs (intrinsically disordered proteins/regions). We anticipate that IDP–CRF will facilitate the development of protein sequence analysis.

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“…Previous studies (Zou et al, 2015; Liu et al, 2016a; Chen et al, 2016b; Chen et al, 2017a; Chen et al, 2017b; Liu et al, 2018a) have demonstrated that fusing a series of individual predictors by a voting strategy can improve the predictive performance. In this regard, in this study an ensemble predictors was constructed by fusing 10 top performing individual predictors constructed by different parameter combinations of PseKGCC (see Supplementary Information S1 ), which can be represented as (Liu et al, 2016a):…”

Section: Manuscript Formattingmentioning

confidence: 99%

iRO-PsekGCC: Identify DNA Replication Origins Based on Pseudo k-Tuple GC Composition

et al. 2019

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Summary: Identification of replication origins is playing a key role in understanding the mechanism of DNA replication. This task is of great significance in DNA sequence analysis. Because of its importance, some computational approaches have been introduced. Among these predictors, the iRO-3wPseKNC predictor is the first discriminative method that is able to correctly identify the entire replication origins. For further improving its predictive performance, we proposed the Pseudo k-tuple GC Composition (PsekGCC) approach to capture the “GC asymmetry bias” of yeast species by considering both the GC skew and the sequence order effects of k-tuple GC Composition (k-GCC) in this study. Based on PseKGCC, we proposed a new predictor called iRO-PsekGCC to identify the DNA replication origins. Rigorous jackknife test on two yeast species benchmark datasets (Saccharomyces cerevisiae, Pichia pastoris) indicated that iRO-PsekGCC outperformed iRO-3wPseKNC. It can be anticipated that iRO-PsekGCC will be a useful tool for DNA replication origin identification. Availability and implementation: The web-server for the iRO-PsekGCC predictor was established, and it can be accessed at .

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ProtDec-LTR2.0: an improved method for protein remote homology detection by combining pseudo protein and supervised Learning to Rank

Abstract: bliu@hit.edu.cn.

Cited by 37 publications

References 21 publications

Application of Machine Learning in Microbiology

Application of Machine Learning in Microbiology

IDP–CRF: Intrinsically Disordered Protein/Region Identification Based on Conditional Random Fields

iRO-PsekGCC: Identify DNA Replication Origins Based on Pseudo k-Tuple GC Composition

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