Artificial intelligence (AI) in rare diseases: is the future brighter?

The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs' challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs' AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.info:eu-repo/semantics/publishedVersio

Cleft Palate Craniofac J

ObjectiveWith the current widespread use of 3D facial surface imaging in clinical and research environments, there is a growing demand for high quality craniofacial norms based on 3D imaging technology. The principal goal of the 3D Facial Norms (3DFN) project was to create an interactive, web-based repository of 3D facial images and measurements. Unlike other repositories, users can gain access to both summary-level statistics as well as individual-level data, including 3D facial landmark coordinates, 3D-derived anthropometric measurements, 3D facial surface images and genotypes from every individual in the dataset. The 3DFN database currently consists of 2454 male and female participants ranging in age from 3\u201340 years. These subjects were recruited at four US sites and screened for a history of craniofacial conditions. The goal of this paper is to introduce readers to the 3DFN repository by providing a general overview of the project, explaining the rationale behind the creation of the database, and describing the methods used to collect the data.SupplementSex and age-specific summary statistics (means and standard deviations) and growth curves for every anthropometric measurement in the 3DFN dataset are provided as a supplement. These summary statistics and growth curves can aid clinicians in the assessment of craniofacial dysmorphology.U01 DE020078/DE/NIDCR NIH HHS/United StatesR01 DE016148/DE/NIDCR NIH HHS/United StatesUL1 TR000423/TR/NCATS NIH HHS/United StatesR01 DD000295/DD/NCBDD CDC HHS/United StatesU01 DE020057/DE/NIDCR NIH HHS/United States2017-11-01T00:00:00Z26492185PMC4841760vault:1687

Opportunities, barriers, and recommendations in down syndrome research

Recent advances in medical care have increased life expectancy and improved the quality of life for people with Down syndrome (DS). These advances are the result of both pre-clinical and clinical research but much about DS is still poorly understood. In 2020, the NIH announced their plan to update their DS research plan and requested input from the scientific and advocacy community. The National Down Syndrome Society (NDSS) and the LuMind IDSC Foundation worked together with scientific and medical experts to develop recommendations for the NIH research plan. NDSS and LuMind IDSC assembled over 50 experts across multiple disciplines and organized them in eleven working groups focused on specific issues for people with DS. This review article summarizes the research gaps and recommendations that have the potential to improve the health and quality of life for people with DS within the next decade. This review highlights many of the scientific gaps that exist in DS research. Based on these gaps, a multidisciplinary group of DS experts has made recommendations to advance DS research. This paper may also aid policymakers and the DS community to build a comprehensive national DS research strategy

Spatial and Temporal Analysis of Gene Expression during Growth and Fusion of the Mouse Facial Prominences

Orofacial malformations resulting from genetic and/or environmental causes are frequent human birth defects yet their etiology is often unclear because of insufficient information concerning the molecular, cellular and morphogenetic processes responsible for normal facial development. We have, therefore, derived a comprehensive expression dataset for mouse orofacial development, interrogating three distinct regions – the mandibular, maxillary and frontonasal prominences. To capture the dynamic changes in the transcriptome during face formation, we sampled five time points between E10.5–E12.5, spanning the developmental period from establishment of the prominences to their fusion to form the mature facial platform. Seven independent biological replicates were used for each sample ensuring robustness and quality of the dataset. Here, we provide a general overview of the dataset, characterizing aspects of gene expression changes at both the spatial and temporal level. Considerable coordinate regulation occurs across the three prominences during this period of facial growth and morphogenesis, with a switch from expression of genes involved in cell proliferation to those associated with differentiation. An accompanying shift in the expression of polycomb and trithorax genes presumably maintains appropriate patterns of gene expression in precursor or differentiated cells, respectively. Superimposed on the many coordinated changes are prominence-specific differences in the expression of genes encoding transcription factors, extracellular matrix components, and signaling molecules. Thus, the elaboration of each prominence will be driven by particular combinations of transcription factors coupled with specific cell:cell and cell:matrix interactions. The dataset also reveals several prominence-specific genes not previously associated with orofacial development, a subset of which we externally validate. Several of these latter genes are components of bidirectional transcription units that likely share cis-acting sequences with well-characterized genes. Overall, our studies provide a valuable resource for probing orofacial development and a robust dataset for bioinformatic analysis of spatial and temporal gene expression changes during embryogenesis

Biomedical Discovery Acceleration, with Applications to Craniofacial Development

The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work

Opportunities, barriers, and recommendations in down syndrome research

BACKGROUND: Recent advances in medical care have increased life expectancy and improved the quality of life for people with Down syndrome (DS). These advances are the result of both pre-clinical and clinical research but much about DS is still poorly understood. In 2020, the NIH announced their plan to update their DS research plan and requested input from the scientific and advocacy community. OBJECTIVE: The National Down Syndrome Society (NDSS) and the LuMind IDSC Foundation worked together with scientific and medical experts to develop recommendations for the NIH research plan. METHODS: NDSS and LuMind IDSC assembled over 50 experts across multiple disciplines and organized them in eleven working groups focused on specific issues for people with DS. RESULTS: This review article summarizes the research gaps and recommendations that have the potential to improve the health and quality of life for people with DS within the next decade. CONCLUSIONS: This review highlights many of the scientific gaps that exist in DS research. Based on these gaps, a multidisciplinary group of DS experts has made recommendations to advance DS research. This paper may also aid policymakers and the DS community to build a comprehensive national DS research strategy

Genomic data analysis: populations, patients and pipelines

Methods for the ascertainment of genotype data have become more cost efficient by orders of magnitude with the use of high-density genotyping arrays and the advent of next generation sequencing (NGS). The resulting deluge of data has required ever advancing analytical approaches in order for the maximal information to be gleaned from these extensive data.In this work, many application of NGS to clinical research are discussed. This includes the application of targeted gene sequencing to a cohort of 83 patients with chronic kidney disease, whole-exome investigations of eight families with cleft lip/palate phenotypes, as well as five cases where analytical lessons can be learned from exome sequenced cases harbouring pathogenic variants refractory to identification. Additionally, a novel QC tool for the unambiguous tracking of samples undergoing exome sequencing is presented.Furthermore, work is presented investigating the linkage disequilibrium (LD) patterns in populations applying the Malecot-Morton model. We demonstrate that array genotyping is insufficient for the accurate determination of ne LD patterns in the human genome, with whole-genome sequencing providing more representative LD maps. Finally, we apply similar methods to Gallus gallus, generating the highest resolution maps of LD presented to date, showing that the patterns are highly discordant between commercial lines, and define features associated with recombination.Overall, we highlight the diversity of ways in which genetic data can be utilised effectively in the age of genomic `big data', and present tools which may be of benefit to other researchers utilising these technologies<br/