Andrew D. Boyd

Bio: Andrew D. Boyd is an academic researcher from University of Illinois at Chicago. The author has contributed to research in topics: Medicine & Diagnosis code. The author has an hindex of 16, co-authored 71 publications receiving 2457 citations. Previous affiliations of Andrew D. Boyd include University of Illinois at Urbana–Champaign & University of Michigan.

Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data.

Abstract: Genome-wide expression profiling is a powerful tool for implicating novel gene ensembles in cellular mechanisms of health and disease The most popular platform for genome-wide expression profiling is the Affymetrix GeneChip However, its selection of probes relied on earlier genome and transcriptome annotation which is significantly different from current knowledge The resultant informatics problems have a profound impact on analysis and interpretation the data Here, we address these critical issues and offer a solution We identified several classes of problems at the individual probe level in the existing annotation, under the assumption that current genome and transcriptome databases are more accurate than those used for GeneChip design We then reorganized probes on more than a dozen popular GeneChips into gene-, transcript- and exon-specific probe sets in light of up-to-date genome, cDNA/EST clustering and single nucleotide polymorphism information Comparing analysis results between the original and the redefined probe sets reveals ∼30–50% discrepancy in the genes previously identified as differentially expressed, regardless of analysis method Our results demonstrate that the original Affymetrix probe set definitions are inaccurate, and many conclusions derived from past GeneChip analyses may be significantly flawed It will be beneficial to re-analyze existing GeneChip data with updated probe set definitions

Artificial Intelligence for Healthcare in Africa.

Abstract: Digital technology will play a significant role in achieving sustainable human development worldwide. In 2015, United Nations Member States set 17 goals, the Sustainable Development Goals (SDGs), to provide a road map for the achievement of Earth's peace and human prosperity by 2030. SDG 3, as one of the goals which is aimed at ensuring healthy lives and promoting well-being for all at all ages, will greatly benefit from the implementation of digital technology. With over a billion people, Africa can be better positioned to surmount its health challenges—especially regarding maternal and child health and infectious and non-communicable diseases—using digital technology including artificial intelligence (AI). AI is defined as the automation of activities associated with human thinking such as decision-making, problem-solving, and learning (1). AI was first used in medicine in the 1970s when medical expert systems—based on Bayesian statistics and decision theory—diagnosed and recommended treatments for glaucoma and infectious disease (2). Progress in Bayesian networks, artificial neural networks, and hybrid intelligent systems in the late 1990s has scaled up bioinformatics research, thereby expanding uptake of medical artificial intelligence (MAI) (3). Global investment in MAI is projected to hit about $6.6 billion by 2021 as it is anticipated that AI implementations in healthcare can help save $150 billion in costs by 2026 (4). At present, a more meaningful application of MAI occurs in developed nations compared with what is obtained in Africa. The United Nations in two different forums has signaled a need to change this narrative by bringing stakeholders together to discuss how AI can be used to deliver critical public services and help in the journey toward achieving the SDGs (5). In this paper, we briefly highlight contemporary MAI use in Africa, along with its opportunities, challenges, and likely prospects.

The prognostic landscape of genes and infiltrating immune cells across human cancers

Abstract: Molecular profiles of tumors and tumor-associated cells hold great promise as biomarkers of clinical outcomes. However, existing data sets are fragmented and difficult to analyze systematically. Here we present a pan-cancer resource and meta-analysis of expression signatures from ∼18,000 human tumors with overall survival outcomes across 39 malignancies. By using this resource, we identified a forkhead box MI (FOXM1) regulatory network as a major predictor of adverse outcomes, and we found that expression of favorably prognostic genes, including KLRB1 (encoding CD161), largely reflect tumor-associated leukocytes. By applying CIBERSORT, a computational approach for inferring leukocyte representation in bulk tumor transcriptomes, we identified complex associations between 22 distinct leukocyte subsets and cancer survival. For example, tumor-associated neutrophil and plasma cell signatures emerged as significant but opposite predictors of survival for diverse solid tumors, including breast and lung adenocarcinomas. This resource and associated analytical tools (http://precog.stanford.edu) may help delineate prognostic genes and leukocyte subsets within and across cancers, shed light on the impact of tumor heterogeneity on cancer outcomes, and facilitate the discovery of biomarkers and therapeutic targets.

xCell: digitally portraying the tissue cellular heterogeneity landscape

Abstract: Tissues are complex milieus consisting of numerous cell types. Several recent methods have attempted to enumerate cell subsets from transcriptomes. However, the available methods have used limited sources for training and give only a partial portrayal of the full cellular landscape. Here we present xCell, a novel gene signature-based method, and use it to infer 64 immune and stromal cell types. We harmonized 1822 pure human cell type transcriptomes from various sources and employed a curve fitting approach for linear comparison of cell types and introduced a novel spillover compensation technique for separating them. Using extensive in silico analyses and comparison to cytometry immunophenotyping, we show that xCell outperforms other methods. xCell is available at http://xCell.ucsf.edu/ .

Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage

Abstract: Tissue fibrosis is a major cause of mortality that results from the deposition of matrix proteins by an activated mesenchyme. Macrophages accumulate in fibrosis, but the role of specific subgroups in supporting fibrogenesis has not been investigated in vivo. Here, we used single-cell RNA sequencing (scRNA-seq) to characterize the heterogeneity of macrophages in bleomycin-induced lung fibrosis in mice. A novel computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes (SingleR) enabled the subclustering of macrophages and revealed a disease-associated subgroup with a transitional gene expression profile intermediate between monocyte-derived and alveolar macrophages. These CX3CR1+SiglecF+ transitional macrophages localized to the fibrotic niche and had a profibrotic effect in vivo. Human orthologs of genes expressed by the transitional macrophages were upregulated in samples from patients with idiopathic pulmonary fibrosis. Thus, we have identified a pathological subgroup of transitional macrophages that are required for the fibrotic response to injury.

Genome-wide analysis of estrogen receptor binding sites

Abstract: The estrogen receptor is the master transcriptional regulator of breast cancer phenotype and the archetype of a molecular therapeutic target. We mapped all estrogen receptor and RNA polymerase II binding sites on a genome-wide scale, identifying the authentic cis binding sites and target genes, in breast cancer cells. Combining this unique resource with gene expression data demonstrates distinct temporal mechanisms of estrogen-mediated gene regulation, particularly in the case of estrogen-suppressed genes. Furthermore, this resource has allowed the identification of cis-regulatory sites in previously unexplored regions of the genome and the cooperating transcription factors underlying estrogen signaling in breast cancer.