Set Of Annotations Research Articles

Developing a Kinase Chemogenomic Set: Facilitating Investigation into Kinase Biology by Linking Phenotypes to Targets.

Advances in increasingly complex phenotypic screening with lower throughput have necessitated the screening of smaller more highly annotated sets. One such collection of compounds which has been recently assembled is the kinase chemogenomic set. This is a set of curated kinase inhibitors built upon previous iterations, PKIS and PKIS2, and donations from our partners. Each compound in the set has been carefully selected based on selectivity, potency, and kinome coverage. These compounds as a set have been made available to the scientific community, enabling phenotypic screens to identify kinases that drive novel biology. Additionally, the associated data deposited in the public domain have also been used to inform new inhibitor design. Further expansion of this set to complete kinome coverage will allow for a greater understanding of kinase biology and its role in disease.

Two American Poets—Wallace Stevens & William Carlos Williams: From the Collection of Alan M. Klein. The Grolier Club, New York, 2019.

<i>Two American Poets—Wallace Stevens &amp; William Carlos Williams: From the Collection of Alan M. Klein.</i> The Grolier Club, New York, 2019.

Transcriptomic Analysis of Whole Blood in Admixed Latinx Alzheimer Disease Cohorts

AbstractBackgroundIdentifying the genes and biological pathways involved in Alzheimer disease (AD) is critical in the effort to develop effective therapies. Significant work has identified genetic variants conferring risk and protection for AD in individuals of diverse ancestries, but identification of downstream functional effects including modulation of gene regulation is lacking, particularly in individuals of diverse ancestries. Therefore, to explore transcriptional changes between clinically diagnosed AD and cognitively intact age‐matched controls, herein we analyzed RNA sequencing data from peripheral blood collected from individuals of admixed genetic backgrounds.MethodTotal RNA was extracted from peripheral whole blood stored in PAXGene tubes from 47 Cubans (22 AD and 25 controls), 85 Peruvians (41 AD and 44 controls), and 168 Puerto Ricans (88 AD and 80 controls). PolyA selected mRNA was sequenced to more than 40 million paired end read per sample on the Illumina NovaSeq 6000. The bioinformatic pipeline included mapping to the human reference genome (GRCh38), gene quantifications against the GENCODE v35 annotation set, and differential expression was calculated using DESeq2 with sex, age at blood draw, and count of APOEe4 alleles as covariates. Functional categorization was performed by gene set enrichment of gene ontology and KEGG pathways.ResultAcross the cohorts, a total of 358 protein‐coding genes (FDR ≤ 0.05, Fold change ≥ 1.25) were differentially expressed with 238 down‐regulated and 120 up‐regulated in AD relative to controls. Despite the few genes overlapping between ethnicities, pathway analysis revealed common pathways including up‐regulation of genes involved in inflammation and RNA processing and down‐regulation of genes involved in cellular detoxification and lipid transport, among others. Interestingly, while few specific genes overlap in differential expression overlap with a published set of genes from non‐Hispanic Europeans and African Americans (Griswold et al, 2018), the pathways identified are similar.ConclusionOur analysis reveals a signature of gene expression that implicates increased inflammation and decreased cellular detoxification based on gene expression analysis in admixed Latinx AD. Convergence of pathways across these and African American and European cohorts supports the idea of distinct genes but similar underlying pathological processes contributing to AD across individuals of diverse ancestries.

Identification of genes and cellular response factors related to immunotherapy response in mismatch repair-proficient colorectal cancer: a bioinformatics analysis

Mismatch repair-proficient (pMMR) colorectal cancers (CRCs) are thought to be primarily resistant to immune checkpoint inhibitor (ICI) monotherapy. However, recent clinical trials have reported that early-to-mid stage (non-metastatic) CRC responds well to ICI monotherapy. We hypothesized that the efficacy of immunotherapy is linked to a series of gene expression profiles that can characterize the pMMR CRC disease stage. Using The Cancer Genome Atlas (TCGA) CRC data sets, we first investigated transcriptomic features that continuously changed (were continuously upregulated or downregulated) with pMMR CRC disease-stage progression. We defined these gene sets as stage-associated genes. The deconvolution algorithm then enriched these genes with the dynamic changes in the cell type populations of the CRC tumor microenvironment (TME). Finally, the stage-associated genes were cross-referenced to the current transcriptome profile data on ICI treatment of pMMR CRC, which revealed the gene set specifying an effective pMMR tumor response. In total, 774 genes were found to increase in expression and 845 genes to decrease in expression as the stage increased. Using deconvolution methods, we discovered 2 major disease stage-associated alterations in the cellular composition of pMMR CRCs, including changes in cell types involved in host immune responses and tumor cell metastasis. The central memory CD8+ T cell population decreased as the pMMR CRC disease stage increased, but the endothelial cell populations associated with proliferation and metastasis increased. Using a different cell type annotation set (LM22), we discovered that as the disease progressed, M1 macrophages and CD8+ T cells decreased in the TME. In mismatch repair-deficient patients with CRC, however, such a decrease was not observed. Finally, we identified 27 signature genes that can be used to assess ICI efficacy in treatment-naïve patients with pMMR CRC. The current study sought to identify the underlying molecular mechanisms, pathways, and cell landscapes that explain why early-to-mid stage pMMR CRC responds well to ICI treatment. This analysis might be valuable for the selection of patients who might benefit from immunotherapeutic strategies.

UTRdb 2.0: a comprehensive, expert curated catalog of eukaryotic mRNAs untranslated regions.

The 5' and 3' untranslated regions of eukaryotic mRNAs (UTRs) play crucial roles in the post-transcriptional regulation of gene expression through the modulation of nucleo-cytoplasmic mRNA transport, translation efficiency, subcellular localization, and message stability. Since 1996, we have developed and maintained UTRdb, a specialized database of UTR sequences. Here we present UTRdb 2.0, a major update of UTRdb featuring an extensive collection of eukaryotic 5' and 3' UTR sequences, including over 26 million entries from over 6 million genes and 573 species, enriched with a curated set of functional annotations. Annotations include CAGE tags and polyA signals to label the completeness of 5' and 3'UTRs, respectively. In addition, uORFs and IRES are annotated in 5'UTRs as well as experimentally validated miRNA targets in 3'UTRs. Further annotations include evolutionarily conserved blocks, Rfam motifs, ADAR-mediated RNA editing events, and m6A modifications. A web interface allowing a flexible selection and retrieval of specific subsets of UTRs, selected according to a combination of criteria, has been implemented which also provides comprehensive download facilities. UTRdb 2.0 is accessible at http://utrdb.cloud.ba.infn.it/utrdb/.

“Corpus of the Four Poets”: the digital dimension of the national heritage. Project assumptions

This paper presents the preliminary assumptions of the “Corpus of the Four Poets” project, carried out in cooperation between Polish philologists (IBL PAN, UKSW) and IT specialists (CLARIN-PL, Wrocław University of Technology). The aim of the project is to create a digital corpus containing all Polish-language texts by the authors of the so-called “Great Romanticism”: Adam Mickiewicz, Juliusz Słowacki, Zygmunt Krasiński, and Cyprian Norwid, as well as to develop IT tools integrated with the corpus. The final result will be an online open access database with software. The texts for the corpus have been selected from the print sources that are well-known to readers, which provides the most complete and accurate form of the texts and a relative completeness of the lexical base. The set of metadata and annotations developed in the subsequent stages of the project will enable retrieval of the data on the language of individual authors and their objectivised comparison. Keywords: Zygmunt Krasiński– Adam Mickiewicz – Cyprian Norwid – Juliusz Słowacki – poet–prophet – corpus – vocabulary – idiolect.

Individual differences in looking at persons in scenes.

Individuals freely viewing complex scenes vary in their fixation behavior. The most prominent and reliable dimension of such individual differences is the tendency to fixate faces. However, much less is known about how observers distribute fixations across other body parts of persons in scenes and how individuals may vary in this regard. Here, we aimed to close this gap. We expanded a popular annotated stimulus set (Xu, Jiang, Wang, Kankanhalli, & Zhao, 2014) with 6,365 hand-delineated pixel masks for the body parts of 1,136 persons embedded in 700 complex scenes, which we publish with this article (https://osf.io/ynujz/). This resource allowed us to analyze the person-directed fixations of 103 participants freely viewing these scenes. We found large and reliable individual differences in the distribution of fixations across person features. Individual fixation tendencies formed two anticorrelated clusters, one for the eyes, head, and the inner face and one for body features (torsi, arms, legs, and hands). Interestingly, the tendency to fixate mouths was independent of the face cluster. Finally, our results show that observers who tend to avoid person fixations in general, particularly do so for the face region. These findings underscore the role of individual differences in fixation behavior and reveal underlying dimensions. They are further in line with a recently proposed push-pull relationship between cortical tuning for faces and bodies. They may also aid the comparison of special populations to general variation.

Computer-Assisted Annotation of Digital H&E/SOX10 Dual Stains Generates High-Performing Convolutional Neural Network for Calculating Tumor Burden in H&E-Stained Cutaneous Melanoma.

Deep learning for the analysis of H&E stains requires a large annotated training set. This may form a labor-intensive task involving highly skilled pathologists. We aimed to optimize and evaluate computer-assisted annotation based on digital dual stains of the same tissue section. H&E stains of primary and metastatic melanoma (N = 77) were digitized, re-stained with SOX10, and re-scanned. Because images were aligned, annotations of SOX10 image analysis were directly transferred to H&E stains of the training set. Based on 1,221,367 annotated nuclei, a convolutional neural network for calculating tumor burden (CNNTB) was developed. For primary melanomas, precision of annotation was 100% (95%CI, 99% to 100%) for tumor cells and 99% (95%CI, 98% to 100%) for normal cells. Due to low or missing tumor-cell SOX10 positivity, precision for normal cells was markedly reduced in lymph-node and organ metastases compared with primary melanomas (p < 0.001). Compared with stereological counts within skin lesions, mean difference in tumor burden was 6% (95%CI, -1% to 13%, p = 0.10) for CNNTB and 16% (95%CI, 4% to 28%, p = 0.02) for pathologists. Conclusively, the technique produced a large annotated H&E training set with high quality within a reasonable timeframe for primary melanomas and subcutaneous metastases. For these lesion types, the training set generated a high-performing CNNTB, which was superior to the routine assessments of pathologists.

Machine Learning From Crowds Using Candidate Set-Based Labeling

Crowdsourcing is a popular and cheap alternative in machine learning for gathering information from a set of annotators. Learning from crowd-labeled data involves dealing with its inherent uncertainty and inconsistencies. In the classical framework, each annotator provides a single label per example, which fails to capture the complete knowledge of annotators. We propose candidate labeling, that is, to allow annotators to provide a set of candidate labels for each example and thus express their doubts. We propose an appropriate model for the annotators, and present two novel learning methods that deal with the two basic steps (label aggregation and model learning) sequentially or jointly. Our empirical study shows the advantage of candidate labeling and the proposed methods with respect to the classical framework.

Anotação de Entidades Mencionadas na área do Gaming

This paper aims to analyse the effects of including gaming entities in the performance of the NER system, for the English language and in a machine translation industrial context of customer support content. To identify and classify gaming entities (by the Named Entity Recognition (NER) model), three new categories were created and added to the already used annotation typology: GAME NAME, GAME FEATURE and GAME CURRENCY. A set of reference annotations (gold standard) was also developed, allowing not only the training of the NER system but also the evaluation of its performance and accuracy in a more objective way, namely by counting the number of entities that the system identifies and categorises correctly. In the scope of this work, 6618 sentences from 7 gaming clients were manually annotated, constituting the gold standard which was then used to train and evaluate the NER system. The objective of the experiments was to assess whether the existing NER system improved its performance when trained with the gold standard created specifically for the gaming domain and if it could handle the new gaming categories added to the typology by identifying and categorizing them correctly. The results of both experiments were auspicious and positive, demonstrating the relevance of greater investment in domain-specific entity recognition, namely in the context of customer service text processing.

TYRO protein tyrosine kinase-binding protein predicts favorable overall survival in osteosarcoma and correlates with antitumor immunity.

To explore the prognostic significance and underlying mechanism of TYRO protein tyrosine kinase-binding protein (TYROBP) in osteosarcoma. Firstly, the expression of TYROBP was analyzed using the t test. The Kaplan–Meier plotter analysis and a receiver operating characteristic curve were performed to evaluate the influence of TYROBP on overall survival (OS). Further, Cox regression analysis was conducted to predict the independent prognostic factors for OS of osteosarcoma patients, and a nomogram was constructed. Then, the relationship between TYROBP and clinicopathological characteristics was determined using statistical methods. Enrichment analyses were conducted to evaluate the biological functions of TYROBP. Finally, the ESTIMATE algorithm was used to assess the association of TYROBP with immune cell infiltration. TYROBP was significantly increased in osteosarcoma (all P < .001). However, the high expression of TYROBP was related to better OS in osteosarcoma patients. Cox regression analysis showed that TYROBP was an independent prognostic factor for predicting OS (P = .005), especially in patients of the male sex, age <18 years, metastasis, and tumor site leg/foot (all P < .05). Besides, TYROBP mRNA expression was significantly associated with the tumor site (P < .01) but had no remarkable relationship with age, gender, and metastasis status (all P > .05). Functional annotation and gene set enrichment analysis (GSEA) revealed that TYROBP was mainly involved in immune-related pathways. Importantly, TYROBP positively correlated with immune scores (P < .001, R = .87). TYROBP served as an independent prognostic biomarker for OS in osteosarcoma. High TYROBP expression might prolong the survival of osteosarcoma patients mainly through promoting antitumor immunity.

One Clinician Is All You Need-Cardiac Magnetic Resonance Imaging Measurement Extraction: Deep Learning Algorithm Development.

BackgroundCardiac magnetic resonance imaging (CMR) is a powerful diagnostic modality that provides detailed quantitative assessment of cardiac anatomy and function. Automated extraction of CMR measurements from clinical reports that are typically stored as unstructured text in electronic health record systems would facilitate their use in research. Existing machine learning approaches either rely on large quantities of expert annotation or require the development of engineered rules that are time-consuming and are specific to the setting in which they were developed.ObjectiveWe hypothesize that the use of pretrained transformer-based language models may enable label-efficient numerical extraction from clinical text without the need for heuristics or large quantities of expert annotations. Here, we fine-tuned pretrained transformer-based language models on a small quantity of CMR annotations to extract 21 CMR measurements. We assessed the effect of clinical pretraining to reduce labeling needs and explored alternative representations of numerical inputs to improve performance.MethodsOur study sample comprised 99,252 patients that received longitudinal cardiology care in a multi-institutional health care system. There were 12,720 available CMR reports from 9280 patients. We adapted PRAnCER (Platform Enabling Rapid Annotation for Clinical Entity Recognition), an annotation tool for clinical text, to collect annotations from a study clinician on 370 reports. We experimented with 5 different representations of numerical quantities and several model weight initializations. We evaluated extraction performance using macroaveraged F1-scores across the measurements of interest. We applied the best-performing model to extract measurements from the remaining CMR reports in the study sample and evaluated established associations between selected extracted measures with clinical outcomes to demonstrate validity.ResultsAll combinations of weight initializations and numerical representations obtained excellent performance on the gold-standard test set, suggesting that transformer models fine-tuned on a small set of annotations can effectively extract numerical quantities. Our results further indicate that custom numerical representations did not appear to have a significant impact on extraction performance. The best-performing model achieved a macroaveraged F1-score of 0.957 across the evaluated CMR measurements (range 0.92 for the lowest-performing measure of left atrial anterior-posterior dimension to 1.0 for the highest-performing measures of left ventricular end systolic volume index and left ventricular end systolic diameter). Application of the best-performing model to the study cohort yielded 136,407 measurements from all available reports in the study sample. We observed expected associations between extracted left ventricular mass index, left ventricular ejection fraction, and right ventricular ejection fraction with clinical outcomes like atrial fibrillation, heart failure, and mortality.ConclusionsThis study demonstrated that a domain-agnostic pretrained transformer model is able to effectively extract quantitative clinical measurements from diagnostic reports with a relatively small number of gold-standard annotations. The proposed workflow may serve as a roadmap for other quantitative entity extraction.

Deep learning based sentiment analysis of public perception of working from home through tweets.

Nowadays, we are witnessing a paradigm shift from the conventional approach of working from office spaces to the emerging culture of working virtually from home. Even during the COVID-19 pandemic, many organisations were forced to allow employees to work from their homes, which led to worldwide discussions of this trend on Twitter. The analysis of this data has immense potential to change the way we work but extracting useful information from this valuable data is a challenge. Hence in this study, the microblogging website Twitter is used to gather more than 450,000 English language tweets from 22nd January 2022 to 12th March 2022, consisting of keywords related to working from home. A state-of-the-art pre-processing technique is used to convert all emojis into text, remove duplicate tweets, retweets, username tags, URLs, hashtags etc. and then the text is converted to lowercase. Thus, the number of tweets is reduced to 358,823. In this paper, we propose a fine-tuned Convolutional Neural Network (CNN) model to analyse Twitter data. The input to our deep learning model is an annotated set of tweets that are effectively labelled into three sentiment classes, viz. positive negative and neutral using VADER (Valence Aware Dictionary for sEntiment Reasoning). We also use a variation in the input vector to the embedding layer, by using FastText embeddings with our model to train supervised word representations for our text corpus of more than 450,000 tweets. The proposed model uses multiple convolution and max pooling layers, dropout operation, and dense layers with ReLU and sigmoid activations to achieve remarkable results on our dataset. Further, the performance of our model is compared with some standard classifiers like Support Vector Machine (SVM), Naive Bayes, Decision Tree, and Random Forest. From the results, it is observed that on the given dataset, the proposed CNN with FastText word embeddings outperforms other classifiers with an accuracy of 0.925969. As a result of this classification, 54.41% of the tweets are found to show affirmation, 24.50% show a negative disposition, and 21.09% have neutral sentiments towards working from home.

Genomic-Analysis-Oriented Drug Repurposing in the Search for Novel Antidepressants.

From inadequate prior antidepressants that targeted monoamine neurotransmitter systems emerged the discovery of alternative drugs for depression. For instance, drugs targeted interleukin 6 receptor (IL6R) in inflammatory system. Genomic analysis-based drug repurposing using single nucleotide polymorphism (SNP) inclined a promising method for several diseases. However, none of the diseases was depression. Thus, we aimed to identify drug repurposing candidates for depression treatment by adopting a genomic-analysis-based approach. The 5885 SNPs obtained from the machine learning approach were annotated using HaploReg v4.1. Five sets of functional annotations were applied to determine the depression risk genes. The STRING database was used to expand the target genes and identify drug candidates from the DrugBank database. We validated the findings using the ClinicalTrial.gov and PubMed databases. Seven genes were observed to be strongly associated with depression (functional annotation score = 4). Interestingly, IL6R was auspicious as a target gene according to the validation outcome. We identified 20 drugs that were undergoing preclinical studies or clinical trials for depression. In addition, we identified sarilumab and satralizumab as drugs that exhibit strong potential for use in the treatment of depression. Our findings indicate that a genomic-analysis-based approach can facilitate the discovery of drugs that can be repurposed for treating depression.

A general deep learning model for bird detection in high-resolution airborne imagery.

Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing supervised models for ecological monitoring is challenging because it requires large amounts of human-labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting. This limits application across space and time. One solution is developing generalized models that can be applied across species and ecosystems. Using over 250,000 annotations from 13 projects from around the world, we develop a general bird detection model that achieves over 65% recall and 50% precision on novel aerial data without any local training despite differences in species, habitat, and imaging methodology. Fine-tuning this model with only 1000 local annotations increases these values to an average of 84% recall and 69% precision by building on the general features learned from other data sources. Retraining from the general model improves local predictions even when moderately large annotation sets are available and makes model training faster and more stable. Our results demonstrate that general models for detecting broad classes of organisms using airborne imagery are achievable. These models can reduce the effort, expertise, and computational resources necessary for automating the detection of individual organisms across large scales, helping to transform the scale of data collection in ecology and the questions that can be addressed.

Learning COVID-19 Pneumonia Lesion Segmentation From Imperfect Annotations via Divergence-Aware Selective Training.

Automatic segmentation of COVID-19 pneumonia lesions is critical for quantitative measurement for diagnosis and treatment management. For this task, deep learning is the state-of-the-art method while requires a large set of accurately annotated images for training, which is difficult to obtain due to limited access to experts and the time-consuming annotation process. To address this problem, we aim to train the segmentation network from imperfect annotations, where the training set consists of a small clean set of accurately annotated images by experts and a large noisy set of inaccurate annotations by non-experts. To avoid the labels with different qualities corrupting the segmentation model, we propose a new approach to train segmentation networks to deal with noisy labels. We introduce a dual-branch network to separately learn from the accurate and noisy annotations. To fully exploit the imperfect annotations as well as suppressing the noise, we design a Divergence-Aware Selective Training (DAST) strategy, where a divergence-aware noisiness score is used to identify severely noisy annotations and slightly noisy annotations. For severely noisy samples we use an regularization through dual-branch consistency between predictions from the two branches. We also refine slightly noisy samples and use them as supplementary data for the clean branch to avoid overfitting. Experimental results show that our method achieves a higher performance than standard training process for COVID-19 pneumonia lesion segmentation when learning from imperfect labels, and our framework outperforms the state-of-the-art noise-tolerate methods significantly with various clean label percentages.

SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease

SUMMARYWe assess contributions to autoimmune disease of genes whose regulation is driven by enhancer regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using several SNP-to-gene (S2G) strategies and apply heritability analyses to draw three conclusions about 11 autoimmune/blood-related diseases/traits. First, several characterizations of enhancer-related genes using functional genomics data are informative for autoimmune disease heritability after conditioning on a broad set of regulatory annotations. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2-fold stronger heritability signal and >2-fold stronger enrichment for drug targets, compared with the recently proposed enhancer domain score. In each case, functionally informed S2G strategies produced 4.1- to 13-fold stronger disease signals than conventional window-based strategies.

SumSTAAR: A flexible framework for gene-based association studies using GWAS summary statistics.

Gene-based association analysis is an effective gene-mapping tool. Many gene-based methods have been proposed recently. However, their power depends on the underlying genetic architecture, which is rarely known in complex traits, and so it is likely that a combination of such methods could serve as a universal approach. Several frameworks combining different gene-based methods have been developed. However, they all imply a fixed set of methods, weights and functional annotations. Moreover, most of them use individual phenotypes and genotypes as input data. Here, we introduce sumSTAAR, a framework for gene-based association analysis using summary statistics obtained from genome-wide association studies (GWAS). It is an extended and modified version of STAAR framework proposed by Li and colleagues in 2020. The sumSTAAR framework offers a wider range of gene-based methods to combine. It allows the user to arbitrarily define a set of these methods, weighting functions and probabilities of genetic variants being causal. The methods used in the framework were adapted to analyse genes with large number of SNPs to decrease the running time. The framework includes the polygene pruning procedure to guard against the influence of the strong GWAS signals outside the gene. We also present new improved matrices of correlations between the genotypes of variants within genes. These matrices estimated on a sample of 265,000 individuals are a state-of-the-art replacement of widely used matrices based on the 1000 Genomes Project data.

Annotating Music Therapy, Chiropractic and Aquatic Exercise Using Electronic Health Record.

Complementary and Integrative Health (CIH) has gained increasing popularity in the past decades. The overall goal of this study is to represent information pertinent to music therapy, chiropractic and aquatic exercise in an EHR system. A total of 300 clinical notes were randomly selected and manually annotated. Annotations were made for status, symptom and frequency of each approach. This set of annotations was used as a gold standard to evaluate performance of NLP systems used in this study (specifically BioMedICUS, MetaMap and cTAKES) for extracting CIH concepts. Three NLP systems achieved an average lenient match F1-score of 0.50 in all three CIH approaches. BioMedICUS achieved the best performance in music therapy with an F1-score of 0.73. This study is a pilot to investigate CIH representation in clinical note and lays a foundation for using EHR for clinical research for CIH approaches.

This Issue's Featured Solutions

Inside Precision MedicineVol. 9, No. 3 MarketplaceFree AccessThis Issue's Featured SolutionsPublished Online:18 Jun 2022https://doi.org/10.1089/ipm.09.03.18AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail Custom Reagents and Services For Molecular DiagnosticsAt Fortis Life Sciences, we know what it takes to succeed. We first work with you to understand your specific challenges and then our experts will design, develop, manufacture, and validate complete assay solutions for your molecular diagnostic assay.Decision SupportGenomOncology's precision oncology solutions enhance decision support by combining proprietary technologies, curation processes, data-delivery systems, and public and licensed annotation sets, with internal clinical dataOLINK® TARGETOlink® Target allows for more focused studies on a specific area of disease or key biological processes, targeting 96 or 48 biomarkers. The Target readout uses qPCR.Companion Diagnostic (CDx) ServicesQ2 Solutions® has a venerable track record of successfully supporting drug developers and in-vitro diagnostic (IVD) companies in many global CDx programs, from the early stages of biomarker validation through clinical trials testing and commercialization.FiguresReferencesRelatedDetails Volume 9Issue 3Jun 2022 InformationCopyright © GEN PublishingTo cite this article:This Issue's Featured Solutions.Inside Precision Medicine.Jun 2022.69-69.http://doi.org/10.1089/ipm.09.03.18Published in Volume: 9 Issue 3: June 18, 2022PDF download