The paper has been accepted by the ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM BCB) 2012. We want to cancel the submission because of the double entries of the paper in DBLP. Thank you for your understanding
Feature extraction is an unavoidable task, especially in the critical step of preprocessing biological sequences. This step consists for example in transforming the biological sequences into vectors of motifs where each motif is a subsequence that can be seen as a property (or attribute) characterizing the sequence. Hence, we obtain an object-property table where objects are sequences and properties are motifs extracted from sequences. This output can be used to apply standard machine learning tools to perform data mining tasks such as classification. Several previous works have described feature extraction methods for bio-sequence classification, but none of them discussed the robustness of these methods when perturbing the input data. In this work, we introduce the notion of stability of the generated motifs in order to study the robustness of motif extraction methods. We express this robustness in terms of the ability of the method to reveal any change occurring in the input data and also its ability to target the interesting motifs. We use these criteria to evaluate and experimentally compare four existing extraction methods for biological sequences.
A Linear Classifier Based on Entity Recognition Tools and a Statistical Approach to Method Extraction in the Protein-Protein Interaction Literature
Institute for Biotechnology and Bioengineering（生物技术与生物工程研究所）；University of Minho（米尼奥大学）；School of Informatics and Computing, Indiana University（印第安纳大学信息学与计算学院）；FLAD Computational Biology Collaboratorium（FLAD计算生物学合作中心）；Instituto Gulbenkian de Ciência（古尔本基安科学研究所）；Microsoft Corp.（微软公司）；University of Delaware（特拉华大学）；Center for Bioinformatics and Computational Biology, Delaware Biotechnology Institute, University of Delaware（特拉华大学生物技术研究所生物信息学与计算生物学中心）
Agentic Literature Extraction Augments Targeted Protein Degradation Databases
Department of Computer Science and Engineering（计算机科学与工程系）；Chalmers University of Technology（楚姆勒斯技术大学）；University of Gothenburg（哥德堡大学）；Yardi School of Artificial Intelligence（Yardi人工智能学院）；Indian Institute of Technology Delhi（德里印度理工学院）
Extracting Protein-Protein Interactions (PPIs) from Biomedical Literature using Attention-based Relational Context Information
Computational Science Initiative（计算科学计划）；Brookhaven National Laboratory（布鲁克海文国家实验室）；Joint Genome Institute（联合基因组研究所）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
Persistent Sheaf Laplacian Analysis of Protein Stability and Solubility Changes upon Mutation
Department of Mathematics, Michigan State University（数学系，密歇根州立大学）；The Frazer School（弗雷泽学校）；Lassiter High School（拉西特高中）；Department of Biochemistry and Molecular Biology, Michigan State University（生物化学与分子生物学系，密歇根州立大学）；Department of Electrical and Computer Engineering, Michigan State University（电气与计算机工程系，密歇根州立大学）
