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1. Full Text PseAAC-Builder: A cross-platform stand-alone program for generating various special Chou’s pseudo-amino acid compositions
by Du, Pufeng and Wang, Xin and Xu, Chao and Gao, Yang
Analytical biochemistry, ISSN 0003-2697, 06/2012, Volume 425, Issue 2, pp. 117 - 119
Cross-platform | Chou’s pseudo-amino acid composition | Stand-alone | PseACC server | Chou's pseudo-amino acid composition | Biochemistry & Molecular Biology | Physical Sciences | Chemistry | Life Sciences & Biomedicine | Biochemical Research Methods | Chemistry, Analytical | Science & Technology | Amino Acids - chemistry | Software | Proteins - chemistry | Databases, Protein | Index Medicus
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2. Full Text Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network
by Chen, Chao and Zhou, Xibin and Tian, Yuanxin and Zou, Xiaoyong and Cai, Peixiang
Analytical biochemistry, ISSN 0003-2697, 2006, Volume 357, Issue 1, pp. 116 - 121
Pair-coupled amino acid composition | Pseudo-amino acid composition | Amino acid composition | Support vector machine | Fusion | Protein structural class | Biochemistry & Molecular Biology | Physical Sciences | Chemistry | Life Sciences & Biomedicine | Biochemical Research Methods | Chemistry, Analytical | Science & Technology | Amino Acids - chemistry | Computing Methodologies | Proteins - classification | Algorithms | Proteins - chemistry | Amino acids | Index Medicus
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3. Full Text KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides
by Pandey, Poonam and Patel, Vinal and George, Nithin V and Mallajosyula, Sairam S
Journal of proteome research, ISSN 1535-3893, 09/2018, Volume 17, Issue 9, pp. 3214 - 3222
Prediction server | Sequence-based prediction | Feature vector | Hybrid features | Kernel extreme learning machine | Machine learning | Amino acid composition | Pseudo amino acid composition | Dipeptide amino acid composition | Cell-penetrating peptides | Biochemistry & Molecular Biology | Life Sciences & Biomedicine | Biochemical Research Methods | Science & Technology | Amino Acid Sequence | Datasets as Topic | Humans | Amino Acids - chemistry | Benchmarking | Eukaryotic Cells - metabolism | Models, Statistical | Machine Learning | Sequence Analysis, Protein | Animals | Cell Membrane Permeability - physiology | Eukaryotic Cells - cytology | Software | Internet | Cell-Penetrating Peptides - chemistry | Cell-Penetrating Peptides - metabolism | Neural Networks (Computer)
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4. Full Text Changes in human milk fatty acid composition and maternal lifestyle-related factors over a decade: A comparison between the two Ulm Birth Cohort Studies
by Siziba, Linda P and Lorenz, Leonie and Brenner, Hermann and Carr, Prudence and Stahl, Bernd and Mank, Marko and Marosvölgyi, Tamas and Decsi, Tamas and Szabo, Eva and Rothenbacher, Dietrich and Genuneit, Jon
British journal of nutrition, ISSN 0007-1145, 2020, pp. 1 - 8
human milk | dietary habits | fatty acid composition | lactation | Index Medicus
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5. Full Text Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC
by Javed, Faisal and Hayat, Maqsood
Genomics (San Diego, Calif.), ISSN 0888-7543, 12/2019, Volume 111, Issue 6, pp. 1325 - 1332
Pseudo Amino Acid Composition | Rank-SVM | SMOTE | ML-KNN | Split Amino Acid Composition | Genetics & Heredity | Life Sciences & Biomedicine | Biotechnology & Applied Microbiology | Science & Technology | Sequence Analysis, Protein - methods | Computational Biology - methods | Algorithms | Cells - chemistry | Proteins - analysis | Bacterial Proteins - analysis | Viral Proteins - analysis | Index Medicus
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6. Full Text Proteome-wide prediction and annotation of mitochondrial and sub-mitochondrial proteins by incorporating domain information
by Kumar, Ravindra and Kumari, Bandana and Kumar, Manish
Mitochondrion, ISSN 1567-7249, 09/2018, Volume 42, pp. 11 - 22
Amino acid composition | Split amino acid composition | Support vector machine | Dipeptide composition | Pfam domain | Arabidopsis thaliana | Proteins | Physiological aspects | Amino acids | Analysis | Index Medicus
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7. Full Text Using cellular automata images and pseudo amino acid composition to predict protein subcellular location
by Xiao, X and Xiao, X and Shao, S and Shao, S and Ding, Y and Ding, Y and Huang, Z and Huang, Z and Chou, K.-C and Chou, K.-C
Amino acids, ISSN 0939-4451, 2/2006, Volume 30, Issue 1, pp. 49 - 54
Life Sciences | Biochemistry, general | Analytical Chemistry | Life Sciences, general | Proteomics | Neurobiology | Biochemical Engineering | Cellular automata images – Pseudo amino-acid composition – Protein subcellular location – Complexity – Covariant-discriminant algorithm | Covariant-discriminant algorithm | Cellular automata images | Protein subcellular location | Pseudo amino-acid composition | Complexity | Life Sciences & Biomedicine | Biochemistry & Molecular Biology | Science & Technology | Proteins - genetics | Automation | Algorithms | Proteins - metabolism | Image Processing, Computer-Assisted | Amino Acids - analysis | Cells - metabolism | Proteins - chemistry | Subcellular Fractions - metabolism | Proteins | Amino acids | Genes | Images | Genetics | Cellular automata | Position (location) | Recognition | Extreme values | Index Medicus | Keywords: Cellular automata images – Pseudo amino-acid composition – Protein subcellular location – Complexity – Covariant-discriminant algorithm
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8. Full Text Prediction of metalloproteinase family based on the concept of Chou’s pseudo amino acid composition using a machine learning approach
by Mohammad Beigi, Majid and Behjati, Mohaddeseh and Mohabatkar, Hassan
Journal of structural and functional genomics, ISSN 1345-711X, 12/2011, Volume 12, Issue 4, pp. 191 - 197
Life Sciences | Biochemistry, general | Human Genetics | A disintegrin and metalloprotease | Chou’s pseudo amino acid composition | Matrix metalloproteinases | Microbial Genetics and Genomics | Support vector machine | Bioinformatics | Animal Genetics and Genomics | Plant Genetics & Genomics | Chou's pseudo amino acid composition | Computational Biology - methods | Reproducibility of Results | Algorithms | Artificial Intelligence | Sensitivity and Specificity | Metalloproteases - classification | Metalloproteases - chemistry | Proteins | Amino acids | Universities and colleges | Machine learning | Index Medicus | Learning algorithms | ADAM protein | Amino acid composition | Amino acid sequence | Data processing | Metalloproteinase | Matrix metalloproteinase | Structure-function relationships
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9. Full Text Nutlets of Tilia cordata Mill. and Tilia platyphyllos Scop. – Source of bioactive compounds
by Siger, Aleksander and Antkowiak, Wojciech and Dwiecki, Krzysztof and Rokosik, Ewa and Rudzińska, Magdalena
Food chemistry, ISSN 0308-8146, 06/2021, Volume 346, pp. 128888 - 128888
Fatty acid composition | Small-leaved lime | Carotenoids | Oils | Phytosterols | Large-leaved lime | Tocochromanols | Index Medicus
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10. Full Text Effect of degree of milling and defatting on proximate composition, functional and texture characteristics of gluten-free muffin of bran of long-grain indica rice cultivars
by Kaur, Amandeep and Virdi, Amardeep Singh and Singh, Narpinder and Singh, Amandeep and Kaler, Raj Sukhwinder Singh
Food chemistry, ISSN 0308-8146, 05/2021, Volume 345, pp. 128861 - 128861
Amino acid composition | Protein profiling | Proximate composition | Muffins | FTIR analysis | Rice bran | Index Medicus
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11. Full Text Using pseudo amino acid composition to predict protein subcellular location: approached with amino acid composition distribution
by Shi, J.-Y and Zhang, S.-W and Pan, Q and Zhou, G.-P
Amino acids, ISSN 0939-4451, 8/2008, Volume 35, Issue 2, pp. 321 - 327
Life Sciences | Biochemistry, general | Analytical Chemistry | Life Sciences, general | Biochemical Engineering | Proteomics | Neurobiology | Protein subcellular localization – Amino acid composition distribution – Pseudo amino acid composition – Support vector machines | Protein subcellular localization | Support vector machines | Pseudo amino acid composition | Amino acid composition distribution | Life Sciences & Biomedicine | Biochemistry & Molecular Biology | Science & Technology | Predictive Value of Tests | Computational Biology - methods | Intracellular Space - chemistry | Algorithms | Artificial Intelligence | Computer Simulation | Databases, Protein | Amino Acids - analysis | Proteins - chemistry | Pattern Recognition, Automated - methods | Peptides - analysis | Computer science | Proteins | Amino acids | Mathematical analysis | Segments | Genomes | Vectors (mathematics) | Localization | Position (location) | Index Medicus
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12. Full Text Metabolizable amino acids and energy requirements of Nellore and crossbred Angus × Nellore bulls fed rations of different crude protein concentrations
by Amaral, P.M and Mariz, L.D.S and Zanetti, D and Santos, S.A and Pacheco, M.V.C and Pereira, J.M.V and Silva, F.A.S and Filho, S.C. Valadares
Animal (Cambridge, England), ISSN 1751-7311, 01/2021, Volume 15, Issue 1
Fattening bulls | Amino acid composition | Beef cattle | Predictive models | CP
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13. Full Text Development of wafers with fillings made from organic raw materials with improved consumer properties
by Tkachenko, Alina and Syrokhman, Ivan and Skrypnyk, Vyacheslav and Birta, Gabriella and Burgu, Yuriy
Eastern-European journal of enterprise technologies, ISSN 1729-3774, 08/2020, Volume 4, Issue 11 (106), pp. 39 - 45
amino acid composition | organic products | pastry | wafers with fillings | fatty acid composition
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