What is she best known for?
The fields of study she is best known for:
Computational biology, Gene expression profiling, Genomics, Genetics and RNA-Seq are her primary areas of study.
Her work in Computational biology addresses subjects such as KEGG, which are connected to disciplines such as TRANSFAC, Cluster analysis and Proteomics.
Her Gene expression profiling research incorporates elements of Deep sequencing, Microarray, DNA microarray, Feature selection and Time course.
Her studies in Genomics integrate themes in fields like Annotation, Visualization and Blast2GO.
The Visualization study combines topics in areas such as UniGene and Sequence analysis.
Her Blast2GO research is multidisciplinary, incorporating perspectives in Computer graphics, De novo transcriptome assembly, World Wide Web, Directed acyclic graph and InterProScan.
Her most cited work include:
- Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research (8042 citations)
- High-throughput functional annotation and data mining with the Blast2GO suite. (2551 citations)
- Blast2GO: A comprehensive suite for functional analysis in plant genomics. (1471 citations)
What are the main themes of her work throughout her whole career to date?
Ana Conesa mainly investigates Computational biology, Gene, Genetics, Gene expression and Transcriptome.
Her work deals with themes such as RNA-Seq, Gene expression profiling, Alternative splicing, Gene isoform and Genomics, which intersect with Computational biology.
Her primary area of study in Genomics is in the field of Functional genomics.
Ana Conesa has included themes like Annotation and Visualization in her Functional genomics study.
Her Visualization study integrates concerns from other disciplines, such as Data science and Blast2GO.
Ana Conesa interconnects Data mining, Function and Virology in the investigation of issues within Gene.
She most often published in these fields:
- Computational biology (25.14%)
- Gene (25.14%)
- Genetics (18.29%)
What were the highlights of her more recent work (between 2018-2021)?
- Computational biology (25.14%)
- Gene (25.14%)
- Gene expression (17.71%)
In recent papers she was focusing on the following fields of study:
Ana Conesa spends much of her time researching Computational biology, Gene, Gene expression, Multi omics and Gene isoform.
Her Computational biology study combines topics from a wide range of disciplines, such as RNA-Seq, Annotation, Metabolomics, Genomics and Chromatin.
Her studies deal with areas such as Identification and Gene expression profiling as well as RNA-Seq.
Her Annotation research is multidisciplinary, incorporating elements of Human genetics and Identification.
Her Genomics study incorporates themes from Information retrieval, Knowledge extraction and Molecular pathway.
Her Regulation of gene expression study integrates concerns from other disciplines, such as Genome and Cellular differentiation.
Between 2018 and 2021, her most popular works were:
- Making multi-omics data accessible to researchers. (26 citations)
- Harmonization of quality metrics and power calculation in multi-omic studies. (20 citations)
- Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self Organizing Maps. (18 citations)
In her most recent research, the most cited papers focused on:
Her primary areas of study are Computational biology, Cellular differentiation, Data integration, RNA splicing and Data type.
The various areas that she examines in her Computational biology study include RNA-Seq and Alternative splicing.
Her RNA-Seq research incorporates themes from Proteomics, Identification, Metabolomics and Genomics.
Ana Conesa works mostly in the field of Cellular differentiation, limiting it down to topics relating to Regulation of gene expression and, in certain cases, Progenitor cell, Transcription factor, Developmental biology, Housekeeping gene and Cell biology.
The subject of her Data integration research is within the realm of Data mining.
Her studies in RNA splicing integrate themes in fields like Annotation, Human genetics and Identification.
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