Multi-class Prediction Using Stochastic Logic Programs

Efficient Dynamic Analysis of Low-similarity Proteins for Structural Class Prediction

2020 28th European Signal Processing Conference (EUSIPCO) ◽

10.23919/eusipco47968.2020.9287619 ◽

2021 ◽

Author(s):

M.A. Zervou ◽

E. Doutsi ◽

P. Pavlidis ◽

P. Tsakalides

Keyword(s):

Dynamic Analysis ◽

Class Prediction ◽

Structural Class

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Generalized Disjunctive Well-Founded Semantics for Logic Programs

10.21236/ada232064 ◽

1990 ◽

Cited By ~ 19

Author(s):

Chitta Baral ◽

Jorge Lobo ◽

Jack Minker

Keyword(s):

Logic Programs

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Protein Structural Class Prediction Based on Distance-related Statistical Features from Graphical Representation of Predicted Secondary Structure

Letters in Organic Chemistry ◽

10.2174/1570178615666180914110451 ◽

2019 ◽

Vol 16 (4) ◽

pp. 317-324

Author(s):

Liang Kong ◽

Lichao Zhang ◽

Xiaodong Han ◽

Jinfeng Lv

Keyword(s):

Feature Extraction ◽

Secondary Structure ◽

Protein Sequence ◽

Function Analysis ◽

Superior Performance ◽

Support Vector ◽

Chaos Game Representation ◽

Class Prediction ◽

Structural Class ◽

Protein Structural Class

Protein structural class prediction is beneficial to protein structure and function analysis. Exploring good feature representation is a key step for this prediction task. Prior works have demonstrated the effectiveness of the secondary structure based feature extraction methods especially for lowsimilarity protein sequences. However, the prediction accuracies still remain limited. To explore the potential of secondary structure information, a novel feature extraction method based on a generalized chaos game representation of predicted secondary structure is proposed. Each protein sequence is converted into a 20-dimensional distance-related statistical feature vector to characterize the distribution of secondary structure elements and segments. The feature vectors are then fed into a support vector machine classifier to predict the protein structural class. Our experiments on three widely used lowsimilarity benchmark datasets (25PDB, 1189 and 640) show that the proposed method achieves superior performance to the state-of-the-art methods. It is anticipated that our method could be extended to other graphical representations of protein sequence and be helpful in future protein research.

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Logical Decision Problems and Complexity of Logic Programs

Fundamenta Informaticae ◽

10.3233/fi-1987-10102 ◽

1987 ◽

Vol 10 (1) ◽

pp. 1-33

Author(s):

Egon Börger ◽

Ulrich Löwen

Keyword(s):

Fixed Point ◽

Computational Complexity ◽

Decision Problem ◽

Decision Problems ◽

Complexity Classes ◽

Logic Programs ◽

Finite Structures ◽

Syntactic Restrictions

We survey and give new results on logical characterizations of complexity classes in terms of the computational complexity of decision problems of various classes of logical formulas. There are two main approaches to obtain such results: The first approach yields logical descriptions of complexity classes by semantic restrictions (to e.g. finite structures) together with syntactic enrichment of logic by new expressive means (like e.g. fixed point operators). The second approach characterizes complexity classes by (the decision problem of) classes of formulas determined by purely syntactic restrictions on the formation of formulas.

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Learning chemistry: exploring the suitability of machine learning for the task of structure-based chemical ontology classification

Journal of Cheminformatics ◽

10.1186/s13321-021-00500-8 ◽

2021 ◽

Vol 13 (1) ◽

Cited By ~ 1

Author(s):

Janna Hastings ◽

Martin Glauer ◽

Adel Memariani ◽

Fabian Neuhaus ◽

Till Mossakowski

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Short Term Memory ◽

Chemical Space ◽

Chemical Data ◽

Learning Approaches ◽

Class Prediction ◽

Chemical Structures ◽

Chemical Ontology ◽

Chemical Ontologies

AbstractChemical data is increasingly openly available in databases such as PubChem, which contains approximately 110 million compound entries as of February 2021. With the availability of data at such scale, the burden has shifted to organisation, analysis and interpretation. Chemical ontologies provide structured classifications of chemical entities that can be used for navigation and filtering of the large chemical space. ChEBI is a prominent example of a chemical ontology, widely used in life science contexts. However, ChEBI is manually maintained and as such cannot easily scale to the full scope of public chemical data. There is a need for tools that are able to automatically classify chemical data into chemical ontologies, which can be framed as a hierarchical multi-class classification problem. In this paper we evaluate machine learning approaches for this task, comparing different learning frameworks including logistic regression, decision trees and long short-term memory artificial neural networks, and different encoding approaches for the chemical structures, including cheminformatics fingerprints and character-based encoding from chemical line notation representations. We find that classical learning approaches such as logistic regression perform well with sets of relatively specific, disjoint chemical classes, while the neural network is able to handle larger sets of overlapping classes but needs more examples per class to learn from, and is not able to make a class prediction for every molecule. Future work will explore hybrid and ensemble approaches, as well as alternative network architectures including neuro-symbolic approaches.

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Y-Logic: A Framework for Reasoning About Chameleonic Programs with Inconsistent Completions

Fundamenta Informaticae ◽

10.3233/fi-1990-13405 ◽

1990 ◽

Vol 13 (4) ◽

pp. 465-483

Author(s):

V.S. Subrahmanian

Keyword(s):

Classical Logic ◽

Logic Program ◽

Logic Programs ◽

Large Subset ◽

General Logic ◽

Traditional Sense

Large logic programs are normally designed by teams of individuals, each of whom designs a subprogram. While each of these subprograms may have consistent completions, the logic program obtained by taking the union of these subprograms may not. However, the resulting program still serves a useful purpose, for a (possibly) very large subset of it still has a consistent completion. We argue that “small” inconsistencies may cause a logic program to have no models (in the traditional sense), even though it still serves some useful purpose. A semantics is developed in this paper for general logic programs which ascribes a very reasonable meaning to general logic programs irrespective of whether they have consistent (in the classical logic sense) completions.

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