References in zbMATH (referenced in 247 articles )

Showing results 1 to 20 of 247.
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  1. Berenice Talamantes-Becerra, Jason Carling, Arthur Georges: omicR: A tool to facilitate BLASTn alignments for sequence data (2021) not zbMATH
  2. Ding, Chenchen; Han, Haitao; Li, Qianyue; Yang, Xiaoxia; Liu, Taigang: iT3SE-PX: identification of bacterial type III secreted effectors using PSSM profiles and XGBoost feature selection (2021)
  3. Zhang, Dan; Chen, Hua-Dong; Zulfiqar, Hasan; Yuan, Shi-Shi; Huang, Qin-Lai; Zhang, Zhao-Yue; Deng, Ke-Jun: iBLP: an XGBoost-based predictor for identifying bioluminescent proteins (2021)
  4. Ahmad, Jamal; Hayat, Maqsood: MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components (2019)
  5. Randić, Milan: Mathematical chemistry illustrations: a personal view of less known results (2019)
  6. Rout, Subhashree; Mahapatra, Rajani Kanta: \textitInsilico analysis of \textitplasmodiumfalciparum CDPK5 protein through molecular modeling, docking and dynamics (2019)
  7. Tao, Jin; Liu, Xiaoqing; Yang, Siqian; Bao, Chaohui; He, Pingan; Dai, Qi: An efficient genomic signature ranking method for genomic island prediction from a single genome (2019)
  8. Wang, Lei; Yan, Xin; Liu, Meng-Lin; Song, Ke-Jian; Sun, Xiao-Fei; Pan, Wen-Wen: Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method (2019)
  9. Abboud, Amir; Rubinstein, Aviad: Fast and deterministic constant factor approximation algorithms for LCS imply new circuit lower bounds (2018)
  10. Kinjo, Akira R.: Cooperative “folding transition” in the sequence space facilitates function-driven evolution of protein families (2018)
  11. Liang, Yunyun; Zhang, Shengli: Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback-Leibler divergence (2018)
  12. Tian, Kun; Zhao, Xin; Yau, Stephen S.-T.: Convex hull analysis of evolutionary and phylogenetic relationships between biological groups (2018)
  13. Zhang, Shengli; Liang, Yunyun: Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC (2018)
  14. Abboud, Amir; Backurs, Arturs: Towards hardness of approximation for polynomial time problems (2017)
  15. Dehzangi, Abdollah; López, Yosvany; Lal, Sunil Pranit; Taherzadeh, Ghazaleh; Michaelson, Jacob; Sattar, Abdul; Tsunoda, Tatsuhiko; Sharma, Alok: PSSM-Suc: accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction (2017)
  16. Pai, Priyadarshini P.; Dash, Tirtharaj; Mondal, Sukanta: Sequence-based discrimination of protein-RNA interacting residues using a probabilistic approach (2017)
  17. Shatabda, Swakkhar; Saha, Sanjay; Sharma, Alok; Dehzangi, Abdollah: iPHLoc-ES: identification of bacteriophage protein locations using evolutionary and structural features (2017)
  18. Beg, Ismat; Rashid, Tabasam: Intuitionistic fuzzy similarity measure: theory and applications (2016)
  19. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)
  20. Jiao, Ya-Sen; Du, Pu-Feng: Prediction of Golgi-resident protein types using general form of Chou’s pseudo-amino acid compositions: approaches with minimal redundancy maximal relevance feature selection (2016)

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