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genome browser 72: Homo sapiens - Description Human Login/Register More ▼ Search Human Search all species Search Ensembl genomes Search Vega Search EMBL-EBI Search Sanger Favourite species All species Recent locations Clear history Recent jobs Human Homo sapiens What's New in Human release 83 More news... View karyotype Example region Genome assembly: GRCh38.p5 (GCA_000001405.20) More information and statistics Download DNA sequence (FASTA) Convert your data to GRCh38 coordinates Display your data in Ensembl Other assemblies Example gene Example transcript Gene annotation What can I find? More about this genebuild, including RNASeq gene expression models Download genes, cDNAs, ncRNA, proteins (FASTA) Update your old Ensembl IDs Additional manual annotation can be found in Vega Example gene tree Comparative genomics What can I find? More about comparative analysis Download alignments (EMF) Example variant Example phenotype Example structural variant Variation What can I find? More about variation in Ensembl Download all variants (GVF) Variant Effect Predictor Regulation Blog

The Human Protein Atlas MalaCards - human disease database human disease database MalaCards is an integrated searchable database of human maladies and their annotations, modeled on the architecture and richness of the popular GeneCards database of human genes. MalaCards leverages GeneCards and GeneDecks and their associated genes. Each "card" contains a variety of detailed sections. For example, see our Sample Malady. MalaCards search guide This page provides information about the various MalaCards sections and tables. MalaCards Disease List An offline process is responsible for generating the comprehensive integrated list of diseases by mining heterogeneous, partially overlapping sources (see below for list of sources), unifying names and acronyms, and organizing characterizations. Disease name unification is effected by transforming each name to a canonical form. For each malady a unique symbol is generated, composed of the first letter of its name, followed by the next two consonants, followed by a serial number. MalaCards Header Annotation schemes MalaCards Scores Genes

Why Most Published Research Findings Are False Summary There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Figures Published: August 30, 2005 Copyright: © 2005 John P. Bias

Retraction Watch | Tracking retractions as a window into the scientific process Falsification, Fabrication, and Plagiarism: The Unholy Trinity of Scientific Writing Article Outline One of the greatest, and sadly all too common, challenges facing a contemporary medical journal editor is the adjudication of ethical integrity issues. I had originally presumed that this would be just an occasional role, but it transpires that these problems are quite widespread, ranging from unconscious and unwitting naiveté to the conscious and willful betrayal of scientific trust. As a journal, we have no significant powers of investigation, and determining, often years after publication, what is truth and what is fiction can be impossibly hard. There has always been pressure on investigators, but in a time of economic hardship these are amplified. Fabrication Making up data or results and reporting them: you might think that this is so egregious no one would take a chance, but you would be wrong. Falsification Plagiarism Plagiarism means the appropriation of another’s ideas, results, or words without giving proper credit. References

Map: explore the human disease network. Dataset, interactive map and printable poster of gene-disease relationships. Curious about the Diseasome map? Here are some answers to the most common questions asked: I. Drawing the map The Diseasome map is composed of 516 diseases and 903 genes - using the data of Marc Vidal, Albert-Laszlo Barabasi and Michael Cusick. The diseases are divided into twenty-two different categories: BoneCancerCardiovascularConnective tissue disorderDermatologicalDevelopmentalEar,Nose,ThroatEndocrineGastrointestinalHematologicalImmunologicalMetabolicMuscularNeurologicalNutritionalOphthamologicalPsychiatricRenalRespiratorySkeletalMultipleUnclassified II. The Diseasome map’s default view is set to display all the categories and genes at once. III. As shown in the map’s navigation bar, a node’s color indicates the category it belongs to, and a disease node’s size indicates its hub degree (overall number of outbound links). The more links a node send to gene nodes shown on the map, the bigger it appears on the map. These positioning principles call for the following reading conventions: