Claverie, 2000 - Google Patents
From bioinformatics to computational biologyClaverie, 2000
View PDF- Document ID
- 7621703094337007886
- Author
- Claverie J
- Publication year
- Publication venue
- Genome research
External Links
Snippet
It is quite ironic that the uncertainty about the number of human genes (28,000– 120,000)(Ewing and Green 2000; Liang et al. 2000; Roest Crollius et al. 2000) appears to increase as the determination of the human genome sequence is nearing completion. I shall …
- 238000011160 research 0 abstract description 5
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
- G06F19/22—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
- 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
- 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
- G06F19/18—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
-
- 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
- G06F19/16—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
-
- 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
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12N—MICRO-ORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING OR MAINTAINING MICRO-ORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
- C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
- C12N15/09—Recombinant DNA-technology
- C12N15/10—Processes for the isolation, preparation or purification of DNA or RNA
- C12N15/1034—Isolating an individual clone by screening libraries
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES OR MICRO-ORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or micro-organisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or micro-organisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6869—Methods for sequencing
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wei et al. | Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework | |
| Aoki et al. | Convolutional neural networks for classification of alignments of non-coding RNA sequences | |
| Dao et al. | Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique | |
| Zhang et al. | CRIP: predicting circRNA–RBP-binding sites using a codon-based encoding and hybrid deep neural networks | |
| Chen et al. | MethyRNA: a web server for identification of N6-methyladenosine sites | |
| Feng et al. | iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC | |
| Qiang et al. | M6AMRFS: robust prediction of N6-methyladenosine sites with sequence-based features in multiple species | |
| Zhang et al. | RBPPred: predicting RNA-binding proteins from sequence using SVM | |
| Schweikert et al. | mGene: accurate SVM-based gene finding with an application to nematode genomes | |
| Zorita et al. | Starcode: sequence clustering based on all-pairs search | |
| Pham et al. | H2Opred: a robust and efficient hybrid deep learning model for predicting 2’-O-methylation sites in human RNA | |
| Zhang et al. | Predicting CTCF-mediated chromatin loops using CTCF-MP | |
| Claverie | From bioinformatics to computational biology | |
| Li et al. | HSM6AP: a high-precision predictor for the Homo sapiens N6-methyladenosine (m^ 6 A) based on multiple weights and feature stitching | |
| Zeng et al. | LncLocFormer: a transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism | |
| Huang et al. | Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data | |
| Cui et al. | Protein–DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data | |
| Brejová et al. | Finding patterns in biological sequences | |
| Jiang et al. | Basics of bioinformatics: Lecture notes of the graduate summer school on bioinformatics of China | |
| Metzner et al. | Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome | |
| Liu et al. | CAKE: a flexible self-supervised framework for enhancing cell visualization, clustering and rare cell identification | |
| Reddy et al. | HybridPPI: A Hybrid Machine Learning Framework for Protein-Protein Interaction Prediction | |
| Song et al. | Multi-task adaptive pooling enabled synergetic learning of RNA modification across tissue, type and species from low-resolution epitranscriptomes | |
| Si et al. | Improved protein contact prediction using dimensional hybrid residual networks and singularity enhanced loss function | |
| Alam et al. | Unveiling the potential pattern representation of rna 5-methyluridine modification sites through a novel feature fusion model leveraging convolutional neural network and tetranucleotide composition |