Rueda et al., 2008 - Google Patents
Clustering time-series gene expression data with unequal time intervalsRueda et al., 2008
View PDF- Document ID
- 8776684421391334599
- Author
- Rueda L
- Bari A
- Ngom A
- Publication year
- Publication venue
- Transactions on Computational Systems Biology X
External Links
Snippet
Clustering gene expression data given in terms of time-series is a challenging problem that imposes its own particular constraints, namely exchanging two or more time points is not possible as it would deliver quite different results, and also it would lead to erroneous …
- 230000014509 gene expression 0 title abstract description 325
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/20—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for hybridisation or gene expression, e.g. microarrays, sequencing by hybridisation, normalisation, profiling, noise correction models, expression ratio estimation, probe design or probe optimisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/24—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/12—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for modelling or simulation in systems biology, e.g. probabilistic or dynamic models, gene-regulatory networks, protein interaction networks or metabolic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/22—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for sequence comparison involving nucleotides or amino acids, e.g. homology search, motif or SNP [Single-Nucleotide Polymorphism] discovery or sequence alignment
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
- G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Johansson et al. | A multivariate approach applied to microarray data for identification of genes with cell cycle-coupled transcription | |
| Zhang et al. | Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome | |
| US9020934B2 (en) | Method, an arrangement and a computer program product for analysing a biological or medical sample | |
| Yang et al. | iEnhancer-RD: identification of enhancers and their strength using RKPK features and deep neural networks | |
| Ahmad et al. | Integrating heterogeneous omics data via statistical inference and learning techniques | |
| Agarwal et al. | Data denoising and post-denoising corrections in single cell RNA sequencing | |
| Rueda et al. | Clustering time-series gene expression data with unequal time intervals | |
| Arya et al. | Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications | |
| Reddy et al. | Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites | |
| Han et al. | Using matrix of thresholding partial correlation coefficients to infer regulatory network | |
| Nair et al. | Probabilistic partitioning methods to find significant patterns in ChIP-Seq data | |
| Liu et al. | The power of matrix factorization: methods for deconvoluting genetic heterogeneous data at expression level | |
| Subhani et al. | Microarray time-series data clustering via multiple alignment of gene expression profiles | |
| US20180181705A1 (en) | Method, an arrangement and a computer program product for analysing a biological or medical sample | |
| Moskowitz et al. | Nonparametric analysis of contributions to variance in genomics and epigenomics data | |
| Liu et al. | Assessing agreement of clustering methods with gene expression microarray data | |
| Tian et al. | Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification | |
| Rueda et al. | Clustering temporal gene expression data with unequal time intervals | |
| Gandolfi et al. | A computational approach for the functional classification of the epigenome | |
| Marczyk et al. | Single-cell transcriptomics | |
| van Bakel et al. | A tutorial for DNA microarray expression profiling | |
| Kiranmai et al. | Supervised techniques in proteomics | |
| Gao et al. | DreamDIA-XMBD: deep representation features improve the analysis of data-independent acquisition proteomics | |
| Zhang et al. | A Comprehensive Review of Machine Learning-based Clustering Methods for Single-Cell RNA Sequencing Data: Advantages and Challenges | |
| Huang et al. | NanoLoop: A deep learning framework leveraging Nanopore sequencing for chromatin loop prediction |