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Protein Residue Contact Prediction Based on Deep Learning and Massive Statistical Features from Multi-Sequence Alignment 认领 引用 被引量：1
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作者 Huiling Zhang Min Hao +4 位作者 Hao Wu Hing-Fung Ting Yihong Tang Wenhui Xi Yanjie Wei 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2022年第5期843-854,共12页
Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3 D structure.An accurate residue-residue contact map is one of the essenti... Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3 D structure.An accurate residue-residue contact map is one of the essential elements for current ab initio prediction protocols of 3 D structure prediction.Recently,with the combination of deep learning and direct coupling techniques,the performance of residue contact prediction has achieved significant progress.However,a considerable number of current Deep-Learning(DL)-based prediction methods are usually time-consuming,mainly because they rely on different categories of data types and third-party programs.In this research,we transformed the complex biological problem into a pure computational problem through statistics and artificial intelligence.We have accordingly proposed a feature extraction method to obtain various categories of statistical information from only the multi-sequence alignment,followed by training a DL model for residue-residue contact prediction based on the massive statistical information.The proposed method is robust in terms of different test sets,showed high reliability on model confidence score,could obtain high computational efficiency and achieve comparable prediction precisions with DL methods that relying on multi-source inputs. 展开更多
关键词 multi-sequence alignment residue-residue contact prediction feature extraction statistical information Deep Learning(DL) high computational efficiency
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Classification and counting on multi-continued fractions and its application to multi-sequences 认领 引用
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作者 DAI ZongDuo FENG XiuTao 《Science in China(Series F)》 2007年第3期351-358,共8页
In the light of multi-continued fraction theories, we make a classification and counting for multi-strict continued fractions, which are corresponding to multi-sequences of multiplicity m and length n. Based on the ab... In the light of multi-continued fraction theories, we make a classification and counting for multi-strict continued fractions, which are corresponding to multi-sequences of multiplicity m and length n. Based on the above counting, we develop an iterative formula for computing fast the linear complexity distribution of multi-sequences. As an application, we obtain the linear complexity distributions and expectations of multi-sequences of any given length n and multiplicity m less than 12 by a personal computer. But only results of m=3 and 4 are given in this paper. 展开更多
关键词 multi-strict continued fractions multi-sequences linear complexity distribution
Mining High-Quantitative Periodic Frequent Patterns across Multiple Sequences 认领 引用
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作者 Yan Ge Zhenzhou Zhang Chien-Ming Chen 《Computers, Materials & Continua》 SCIE EI 2026年第8期977-1000,共24页
Periodic pattern mining plays an important role in revealing recurring behavioral regularities from temporal sequence data.Most existing approaches,however,are developed for single-sequence settings and rarely account... Periodic pattern mining plays an important role in revealing recurring behavioral regularities from temporal sequence data.Most existing approaches,however,are developed for single-sequence settings and rarely account for quantitative information or sequence-level constraints when patterns recur across multiple sequences.This limits their usefulness in practical scenarios,where a pattern is expected to be not only periodic but also quantitatively significant in a sufficiently large portion of sequences.In this work,we formulate the problem of mining High-Quantitative Periodic Frequent Patterns(HQPFPS)from multi-sequence databases and propose an efficient algorithm,termed MHQPFPS.The proposed method evaluates pattern significance through a quantitative ratio within each sequence and exploits a sequence-level upper bound to effectively prune unpromising candidates during pattern growth.To support efficient evaluation,a compact list-based structure is introduced to maintain support,periodicity,and quantitative statistics,thereby avoiding repeated scans of the database.These components are combined within a depth-first exploration framework to systematically generate valid patterns while discarding those that fail to satisfy the required periodic or quantitative constraints.Experimental results on both real-world and synthetic datasets show that MHQPFPS is able to extract meaningful high-quantitative periodic patterns across multiple sequences.Moreover,the results indicate that the proposed pruning strategies substantially reduce computational cost in terms of runtime and memory consumption under a wide range of parameter settings. 展开更多
关键词 Data mining high-quantitative periodic patterns multi-sequence databases quantitative pattern mining
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Multi-Firmware Comparison Based on Evolutionary Algorithm and Trusted Base Point 认领 引用
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作者 Wenbing Wang Yongwen Liu 《Computers, Materials & Continua》 SCIE EI 2025年第7期763-790,共28页
Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk.How-ever,the problem of matching functions between multiple firmwares has not been studied before.This paper proposes a multi... Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk.How-ever,the problem of matching functions between multiple firmwares has not been studied before.This paper proposes a multi-firmware comparison method based on evolutionary algorithms and trusted base points.We first model the multi-firmware comparison as a multi-sequence matching problem.Then,we propose an adaptation function and a population generation method based on trusted base points.Finally,we apply an evolutionary algorithm to find the optimal result.At the same time,we design the similarity of matching results as an evaluation metric to measure the effect of multi-firmware comparison.The experiments show that the proposed method outperforms Bindiff and the string-based method.Precisely,the similarity between the matching results of the proposed method and Bindiff matching results is 61%,and the similarity between the matching results of the proposed method and the string-based method is 62.8%.By sampling and manual verification,the accuracy of the matching results of the proposed method can be about 66.4%. 展开更多
关键词 Multi-firmware comparison evolutionary algorithm multi-sequence matching binary code comparison
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Development and Validation of a Deep Learning Predictive Model Combining Clinical and Radiomic Features for Short-Term Postoperative Facial Nerve Function in Acoustic Neuroma Patients 认领 引用 被引量：4
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作者 Meng-yang WANG Chen-guang JIA +4 位作者 Huan-qing XU Cheng-shi XU Xiang LI Wei WEI Jin-cao CHEN 《Current Medical Science》 SCIE CAS 2023年第2期336-343,共8页
Objective This study aims to construct and validate a predictable deep learning model associated with clinical data and multi-sequence magnetic resonance imaging(MRI)for short-term postoperative facial nerve function ... Objective This study aims to construct and validate a predictable deep learning model associated with clinical data and multi-sequence magnetic resonance imaging(MRI)for short-term postoperative facial nerve function in patients with acoustic neuroma.Methods A total of 110 patients with acoustic neuroma who underwent surgery through the retrosigmoid sinus approach were included.Clinical data and raw features from four MRI sequences(T1-weighted,T2-weighted,T1-weighted contrast enhancement,and T2-weighted-Flair images)were analyzed.Spearman correlation analysis along with least absolute shrinkage and selection operator regression were used to screen combined clinical and radiomic features.Nomogram,machine learning,and convolutional neural network(CNN)models were constructed to predict the prognosis of facial nerve function on the seventh day after surgery.Receiver operating characteristic(ROC)curve and decision curve analysis(DCA)were used to evaluate model performance.A total of 1050 radiomic parameters were extracted,from which 13 radiomic and 3 clinical features were selected.Results The CNN model performed best among all prediction models in the test set with an area under the curve(AUC)of 0.89(95%CI,0.84–0.91).Conclusion CNN modeling that combines clinical and multi-sequence MRI radiomic features provides excellent performance for predicting short-term facial nerve function after surgery in patients with acoustic neuroma.As such,CNN modeling may serve as a potential decision-making tool for neurosurgery. 展开更多
关键词 acoustic neuroma convolutional neural network facial nerve function machine learning multi-sequence magnetic resonance imaging
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High Utility Periodic Frequent Pattern Mining in Multiple Sequences 认领 引用 被引量：1
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作者 Chien-Ming Chen Zhenzhou Zhang +1 位作者 Jimmy Ming-Tai Wu Kuruva Lakshmanna 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第10期733-759,共27页
Periodic patternmining has become a popular research subject in recent years;this approach involves the discoveryof frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic pa... Periodic patternmining has become a popular research subject in recent years;this approach involves the discoveryof frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic patternmining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodicpatterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequencesis more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences isimportant. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To addressexisting problems, three new measures are defined: the utility, high support, and high-utility period sequenceratios. Further, a new upper bound, upSeqRa, and two new pruning properties were proposed. MHUPFPS usesa newly defined HUPFPS-list structure to significantly accelerate the reduction of the search space and improvethe overall performance of the algorithm. Furthermore, the proposed algorithmis evaluated using several datasets.The experimental results indicate that the algorithm is accurate and effective in filtering several non-high-utilityperiodic frequent patterns. 展开更多
关键词 Decision making frequent periodic pattern multi-sequence database sequential rules utility mining
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