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An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data

Fundamental Research 2024年第4期4卷 770-776页

作者： Yan Hong Hanshuang Li Chunshen Long Pengfei Liang Jian Zhou Yongchun Zuo State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock Institutes of Biomedical SciencesCollege of Life SciencesInner Mongolia UniversityHohhot010020China

The increasing emergence of the time-series single-cell RNA sequencing(scRNA-seq)data,inferring developmental trajectory by connecting transcriptome similar cell states(i.e.,cell types or clusters)has become a major *** existing computational methods are designed for individual cells and do not take into account the available time series *** present IDTI based on the increment of diversity for Trajectory Inference,which combines time series information and the minimum increment of diversity method to infer cell state trajectory of time-series scRNA-seq *** apply IDTI to simulated and three real diverse tissue development datasets,and compare it with six other commonly used trajectory inference methods in terms of topology similarity and branching *** results have shown that the IDTI method accurately constructs the cell state trajectory without the requirement of starting *** the performance test,we further demonstrate that IDTI has the advantages of high accuracy and strong robustness.

关键词： increment of diversity Time-series scRNA-seq data Cell state trajectory inference Topology similarity Branching accuracy

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Prediction of Protein Structural Classes Using the Theory of increment of diversity and Support Vector Machine

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Wuhan University Journal of Natural Sciences 2011年第3期16卷 260-264页

作者： WANG Fangping WANG Zhijian LI Hong YANG Keli School of Physics and Information Science Tianshui Normal University Tianshui 741001Gansu China College of Sciences and Technology Inner Mongolia University Hohhot 010021 Inner Mongolia China

Based on the concept of the pseudo amino acid composition （PseAAC）, protein structural classes are predicted by using an approach of increment of diversity combined with support vector machine （ID-SVM）, in which the dipeptide amino acid composition of proteins is used as the source of diversity. Jackknife test shows that total prediction accuracy is 96.6% and higher than that given by other approaches. Besides, the specificity （Sp） and the Matthew＇s correlation coefficient （MCC） are also calculated for each protein structural class, the Sp is more than 88%, the MCC is higher than 92%, and the higher MCC and Sp imply that it is credible to use ID-SVM model predicting protein structural class. The results indicate that： 1 the choice of the source of diversity is reasonable, 2 the predictive performance of IDSVM is excellent, and3 the amino acid sequences of proteins contain information of protein structural classes.

关键词： dipeptide amino acid composition increment of diversity support vector machines protein structure classes

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Using long-range contact number information for protein secondary structure prediction

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International Journal of Biomathematics 2014年第5期7卷 83-93页

作者： Yonge Feng Liaofu Luo College of ScienceInner Mongolia Agriculture University Hohhot 010018 P. R. China School of Physical Science and Technology Inner Mongolia University Hohhot 010021 P. R. China

In this paper, we first combine tetra-peptide structural words with contact number for protein secondary structure prediction. We used the method of increment of diversity combined with quadratic discriminant analysis to predict the structure of central residue for a sequence fragment. The method is used tetra-peptide structural words and long- range contact number as information resources. The accuracy of Q3 is over 83% in 194 proteins. The accuracies of predicted secondary structures for 20 amino acid residues are ranged from 81% to 88%. Moreover, we have introduced the residue long-range contact, which directly indicates the separation of contacting residue in terms of the position in the sequence, and examined the negative influence of long-range residue interactions on predicting secondary structure in a protein. The method is also compared with existing prediction methods. The results show that our method is more effective in protein secondary structures prediction.

关键词： Tetra-peptide structural words protein secondary structure long-rangecontact increment of diversity quadratic discriminant analysis.

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Discrimination of intra/extracellular sidedness segments of transmembrane proteins

Discrimination of intra/extracellular sidedness segments of ...

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第十一次中国生物物理学术大会暨第九届全国会员代表大会

作者： Wei Chen,Liao-fu Luo School of Physical Science and Technology,Inner Mongolia University,Hohhot 010021,China

＜正＞Topology of the transmembrane protein is closely related to its ***,it is desiderated to depict the topology of transmembrane *** this work,an effective approach of

关键词： transmembrane protein topology increment of diversity quadratic discriminant analysis

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PredictingβαβMotifs Based on SVM Algorithm by Using the ID and MS values

PredictingβαβMotifs Based on SVM Algorithm by Using the I...

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第五届全国生物信息学与系统生物学学术大会暨国际生物信息学前沿研讨会高通量时代的生物信息学与系统生物学

作者： Lixia Sun Xiuzhen Hu College of Sciences Inner Mongolia University of Technology

Background:βαβmotif is an important super secondary structure in *** strand- loop-helix-loop-strand structures,if two parallelβ-strands are connected by a a-helix,and there are one or more hydrogen bonds between two adjacent strands,then the structure is called asβαβmotifs,otherwise it is considered as non-βαβ*** function of proteins is closely related with their structure,thus study of protein structure is very important to understand its *** the high-throughput era,by using the theoretical method to predict protein structure has become one of the important ways in biology *** is very difficult to directly predict the tertiary structure from *** secondary structure, especiallyβαβmotif,is a bridge between secondary structure and tertiary structure.βαβmotifs often appear in the bacillus subtilis protease,and many function sites occur in theβαβmotifs,therefore,prediction ofβαβmotif has very important *** provides theoretical direction for drug molecules design. Methods:We constructed a new dataset,which contains 4277βαβmotifs and 3366 nonβαβmotifs with sequence identity increment of diversity(ID) values, matrix scoring(MS) values and amino acids component as parameters. Results:In this paper,by using the ID values,MS values and amino acids component to express the sequence information,then we combined these parameters as SVM input. The predictive performance is *** overall accuracy and Matthew’s correlation coefficient of 5-fold cross-validation achieve 77.7%and 0.527,the sensitivity ofβαβmotifs and non-βαβmotifs achieve respectively 83.6%and 68.4%,the specificity ofβαβmotifs and non-βαβmotifs achieve respectively 79.1%and 74.4%. Conclusions:The MS values can reflect the conservatism of the amino acids sequence, and ID value is the sequence information’s secondary *** SVM algorithm is a convex quadrati

关键词： βαβmotifs amino acids component increment of diversity scoring matrix Support Vector Machine algorithm

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