Article
Biochemistry & Molecular Biology
Jaie Woodard, Sumaiya Iqbal, Alireza Mashaghi
Summary: This study analyzes the circuit topological environments of 21 K mutations and finds that the number of contacts involving the mutated residue in specific circuit topological arrangements can determine the pathogenicity of human variants. The results also show that circuit topology provides nonredundant information on protein structures and the pathogenicity of mutations.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2022)
Article
Computer Science, Information Systems
Zichen Zhang, Shifei Ding, Yuting Sun
Summary: This paper introduces a new method called multiple birth support vector regression (MBSVR), which constructs the regressor from multiple hyperplanes obtained by solving small quadratic programming problems, aiming for faster computation and better fitting precision.
INFORMATION SCIENCES
(2021)
Article
Environmental Sciences
Zhihao Wang, Alexander Brenning
Summary: Using active learning with uncertainty sampling can reduce the time and cost needed by experts under limited data conditions, improve model performance, and is particularly suitable for emergency response settings and landslide susceptibility modeling.
Article
Computer Science, Artificial Intelligence
Liming Liu, Maoxiang Chu, Rongfen Gong, Li Zhang
Summary: The improved nonparallel support vector machine (INPSVM) proposed in this article inherits the advantages of nonparallel support vector machine (NPSVM) while also offering incomparable benefits over twin support vector machine (TSVM). INPSVM effectively eliminates noise effects and achieves higher classification accuracy for both linear and nonlinear datasets compared to other algorithms. Experimental results demonstrate the superior efficiency, accuracy, and robustness of INPSVM.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Biochemical Research Methods
Chaolu Meng, Ying Ju, Hua Shi
Summary: This study used machine learning to explore the mechanism and important components of protein thermostability, and provided an accessible web server.
ANALYTICAL BIOCHEMISTRY
(2022)
Article
Computer Science, Artificial Intelligence
Chen Ding, Tian-Yi Bao, He-Liang Huang
Summary: The study proposes a quantum-inspired classical algorithm for LS-SVM, utilizing an improved sampling technique for classification. The theoretical analysis indicates that the algorithm can achieve classification with logarithmic runtime for low-rank, low-condition number, and high-dimensional data matrices.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Review
Computer Science, Information Systems
Arijit Chakraborty, Sajal Mitra, Debashis De, Anindya Jyoti Pal, Ferial Ghaemi, Ali Ahmadian, Massimiliano Ferrara
Summary: Protein-Protein Interaction (PPI) is a crucial network in biology that requires fast, accurate, and critical analysis, with Support Vector Machine (SVM) being an effective tool for solving complex classification problems.
Article
Computer Science, Information Systems
Ceren Atik, Recep Alp Kut, Reyat Yilmaz, Derya Birant
Summary: This paper proposes a novel method called support vector machine chains (SVMC) that involves chaining together multiple SVM classifiers in a special structure, decrementing one feature at each stage. The paper also introduces a new voting mechanism called tournament voting, where classifiers' outputs compete in groups and the winning class label of the final round is assigned as the prediction. Experimental results show that SVMC outperforms SVM in terms of accuracy and achieves a 6.88% improvement over state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Matteo Avolio, Antonio Fuduli
Summary: This paper introduces a novel approach for binary multiple instance learning classification, combining the strengths of SVM and PSVM, aiming to discriminate between positive and negative instances by generating a hyperplane placed in the middle between two parallel hyperplanes.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Review
Biochemical Research Methods
Shahid Iqbal, Fuyi Li, Tatsuya Akutsu, David B. Ascher, Geoffrey Webb, Jiangning Song
Summary: This study provides a comprehensive overview of computational tools for predicting protein stability changes upon mutations, evaluating their performance on different datasets based on mutation location and type. Results indicate that predictor performance is influenced by mutation location and type, with varying performance under different conditions for different tools.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Computer Science, Information Systems
Lili Zhu, Petros Spachos
Summary: Food quality and safety are crucial for human health and social stability. This study proposed a mobile visual system to grade bananas, achieving high accuracy rates in the grading process. The complex process of ensuring food quality involves all stages from cultivation to consumption.
INTERNET OF THINGS
(2021)
Review
Agriculture, Multidisciplinary
Zhi Hong Kok, Abdul Rashid Mohamed Shariff, Meftah Salem M. Alfatni, Siti Khairunniza-Bejo
Summary: The Support Vector Machine (SVM) shows excellent performance in precision agriculture (PA), with comparisons to other machine learning algorithms highlighting its strengths and weaknesses in model performance and characteristics.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Computer Science, Artificial Intelligence
Zongmin Liu, Yitian Xu
Summary: In this paper, a novel multi-task nonparallel support vector machine (MTNPSVM) is proposed, which effectively avoids matrix inversion operation and takes full advantage of the kernel trick by introducing epsilon-insensitive loss instead of square loss. The alternating direction method of multipliers (ADMM) is employed to improve computational efficiency, and the properties and sensitivity of the model parameters are further explored.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Chunling Lou, Xijiong Xie
Summary: Two novel multi-view intuitionistic fuzzy support vector machines with insensitive pinball loss are proposed in this paper, which can handle general multi-view classification problems and be robust to noisy data. The pinball loss is incorporated into the multi-view learning to maximize the quantization distance. Intuitionistic fuzzy score is introduced to assign weights to the multi-view samples to effectively utilize multi-view information.
Article
Environmental Sciences
Kouao Laurent Kouadio, Loukou Nicolas Kouame, Coulibaly Drissa, Binbin Mi, Kouamelan Serge Kouamelan, Serge Pacome Deguine Gnoleba, Hongyu Zhang, Jianghai Xia
Summary: This study applied support vector machines (SVMs) to predict flow rates in groundwater exploration, aiming to minimize unsuccessful drillings. The SVM models achieved prediction accuracies of 77% and 83% on multiclass and binary datasets, respectively. The use of optimal polynomial and radial basis function kernels resulted in higher accuracies of 81.61% and 87.36%. Learning curves showed that larger training data could improve prediction performance on the multiclass dataset, but not necessarily on the binary dataset.
WATER RESOURCES RESEARCH
(2022)
Article
Genetics & Heredity
Kunal Kundu, Lipika R. Pal, Yizhou Yin, John Moult
Article
Genetics & Heredity
Lipika R. Pal, Kunal Kundu, Yizhou Yin, John Moult
Article
Genetics & Heredity
Lipika R. Pal, Kunal Kundu, Yizhou Yin, John Moult
Biographical-Item
Biochemistry & Molecular Biology
John Moult, Krzysztof Fidelis, Andriy Kryshtafovych, Torsten Schwede
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2018)
Article
Biochemistry & Molecular Biology
Andriy Kryshtafovych, Reinhard Albrecht, Arnaud Basle, Pedro Bule, Alessandro T. Caputo, Ana Luisa Carvalho, Kinlin L. Chao, Ron Diskin, Krzysztof Fidelis, Carlos M. G. A. Fontes, Folmer Fredslund, Harry J. Gilbert, Celia W. Goulding, Marcus D. Hartmann, Christopher S. Hayes, Osnat Herzberg, Johan C. Hill, Andrzej Joachimiak, Gert-Wieland Kohring, Roman I. Koning, Leila Lo Leggio, Marco Mangiagalli, Karolina Michalska, John Moult, Shabir Najmudin, Marco Nardini, Valentina Nardone, Didier Ndeh, Thanh-Hong Nguyen, Guido Pintacuda, Sandra Postel, Mark J. van Raaij, Pietro Roversi, Amir Shimon, Abhimanyu K. Singh, Eric J. Sundberg, Kaspars Tars, Nicole Zitzmann, Torsten Schwede
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2018)
Article
Biochemistry & Molecular Biology
John Moult, Krzysztof Fidelis, Andriy Kryshtafovych, Torsten Schwede, Anna Tramontano
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2018)
Article
Biochemistry & Molecular Biology
Andriy Kryshtafovych, Bohdan Monastyrskyy, Krzysztof Fidelis, John Moult, Torsten Schwede, Anna Tramontano
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2018)
Article
Biochemical Research Methods
Lindley Darden, Kunal Kundu, Lipika R. Pal, John Moult
PLOS COMPUTATIONAL BIOLOGY
(2018)
Article
Genetics & Heredity
Gaia Andreoletti, Lipika R. Pal, John Moult, Steven E. Brenner
Article
Genetics & Heredity
Lipika R. Pal, Kunal Kundu, Yizhou Yin, John Moult
Review
Biochemistry & Molecular Biology
Andriy Kryshtafovych, Torsten Schwede, Maya Topf, Krzysztof Fidelis, John Moult
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2019)
Article
Biochemical Research Methods
Kunal Kundu, Lindley Darden, John Moult
Summary: MecCog is a graphical framework for building integrated representations of genetic disease mechanisms, displaying the propagation of system perturbations across stages of biological organization. It utilizes graphical notations, hyperlinked evidence tagging, a mechanism ontology, and depiction of knowledge gaps and uncertainties to enhance utility. The web platform enables users to construct, store, publish, browse, query, and comment on schemas, facilitating the identification of potential biomarkers and therapeutic intervention sites.
Article
Biochemistry & Molecular Biology
Andriy Kryshtafovych, John Moult, Wendy M. Billings, Dennis Della Corte, Krzysztof Fidelis, Sohee Kwon, Kliment Olechnoyic, Chaok Seok, Ceslovas Venclovas, Jonghun Won
Summary: CASP aims to advance protein structure prediction technology and successfully conducted a project to compute the structures of proteins in the SARS-CoV-2 genome. Models from the AlphaFold2 group showed good agreement with experimental structures.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2021)
Article
Biochemistry & Molecular Biology
Andriy Kryshtafovych, Torsten Schwede, Maya Topf, Krzysztof Fidelis, John Moult
Summary: CASP is a community experiment aimed at advancing methods for computing three-dimensional protein structure, including rigorous blind testing and evaluation by independent assessors. In the recent CASP14 experiment, deep-learning methods from one research group consistently delivered computed structures rivaling the corresponding experimental ones in accuracy. These results represent a solution to the classical protein-folding problem, at least for single proteins.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2021)
Article
Biochemistry & Molecular Biology
Andriy Kryshtafovych, Torsten Schwede, Maya Topf, Krzysztof Fidelis, John Moult
Summary: CASP15 experiment made significant progress in simulating protein structures, especially with the outstanding accuracy of deep learning methods in computing single proteins and protein complexes. However, traditional approaches still outperform deep learning methods in computing RNA structures and protein-ligand complexes.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2023)
Article
Biochemistry & Molecular Biology
Ankita Chadda, Alexander G. Kozlov, Binh Nguyen, Timothy M. Lohman, Eric A. Galburt
Summary: In this study, it was found that the DNA damage response in Mycobacterium tuberculosis differs from well-studied model bacteria. The DNA repair helicase UvrD1 in Mtb is activated through a redox-dependent process and is closely associated with the homo-dimeric Ku protein. Additionally, Ku protein is shown to stimulate the helicase activity of UvrD1.
JOURNAL OF MOLECULAR BIOLOGY
(2024)