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Biomedical subjects

Michael Province

Publications and source records attributed to Michael Province.

11 recordsLinked to original sources
PubMed2026

BioMedGraphica: an all-in-one platform for joint textual biomedical prior knowledge and numeric graph generation.

MOTIVATION: Multiomics data analysis is essential for scientific discovery in precision medicine. However, translating analysis results of omics data analysis into novel scientific hypotheses remains a significant challenge. Human experts must manually review analysis results and generate new hypotheses based on extensive and interconnected biomedical prior knowledge, which is subjective and not scalable. While large language models can accelerate the discovery, their reasoning improves when grounded in structured, auditable, and comprehensive biomedical prior knowledge. However, biomedical knowledge is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of artificial intelligence systems to fully leverage biomedical data for scientific discovery. RESULTS: We developed BioMedGraphica, a novel all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified textual prior knowledge graph containing 2 306 921 entities and 27 232 091 relations. In addition, we present a novel textual-numeric graph (TNG) data structure concept, where textual information captures prior biological knowledge (e.g. transcription start sites, functions, mechanisms), numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data structure for developing novel graph analysis models. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph database can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

Humans↗
PubMed2025

BioMedGraphica: An All-in-One Platform for Joint Textual Biomedical Prior Knowledge and Numeric Graph Generation.

Multi-omic data analysis is essential for scientific discovery in precision medicine. However, translating statistical results of omic data analysis into novel scientific hypothesis remains a significant challenge. Human experts must manually review analysis results and generate new hypothesis based on extensive and inter-connected biomedical prior knowledge, which is subjective and not scalable. While large language models (LLMs) can accelerate the discovery, their reasoning improves when grounded in structured, auditable and comprehensive biomedical prior knowledge. Biomedical knowledge, however, is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of AI systems to fully leverage biomedical data for scientific discovery. To address these challenges, we developed BioMedGraphica , an all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified knowledge graph containing 2,306,921 entities and 27,232,091 relations. In addition, to the best of our knowledge, this is the first work to propose a novel Textual-Numeric Graph (TNG) data-structure for multi-omics data analysis. In TNG, textual information captures prior biological knowledge (e.g., transcription start sites, functions, mechanisms), while numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data-structure for the development of graph foundation models, with the potential to improve prediction performance and interpretability, while also augmenting LLMs by supplying graph-structured mechanistic context to strengthen reasoning. The details for BioMedGraphica code can be accessed by github link: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph data can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

biomedical knowledge graph↗
PubMed2006

An updated meta-analysis of genome scans for hypertension and blood pressure in the NHLBI Family Blood Pressure Program (FBPP).

A meta-analysis of the results from a multicenter genome-wide linkage study for hypertension and blood pressure (BP) based on an initial sample of 6,245 individuals was published in 2003. We report here a combined linkage analysis of hypertension and BP using the complete Family Blood Pressure Program (FBPP) dataset, which includes a total of 12,028 genotyped individuals. Genome-wide linkage analyses for hypertension and BP were first performed in each of the studied ethnic group within each network and the results were combined with a meta-analysis using a modified Fisher's method of combining P values. Our meta-analysis of genome scans for the latest FBPP dataset reveals suggestive linkage for hypertension and BP at several regions on the human genome. Strong evidence for linkage at two of these regions, 2p14 and 3p14.1, have also been published in previous meta-analyses, making them good candidate locations for susceptibility variants.

Adult↗
PubMed2005

Genome-wide linkage analysis for loci affecting pulse pressure: the Family Blood Pressure Program.

Pulse pressure, the difference between systolic and diastolic blood pressure, is an independent risk factor for cardiovascular disease. Increased pulse pressure reflects reduced compliance of arteries and is a marker of atherosclerosis. To locate genes that affect pulse pressure, a genome-wide linkage scan for quantitative trait loci influencing pulse pressure was performed using variance components methods as implemented in sequential oligogenic linkage analysis routines. The analysis sample included 10 798 participants in 3320 families who were recruited as part of the Family Blood Pressure Program and were phenotyped with an oscillometric blood pressure measurement device using a consistent protocol across centers. Pulse pressure was adjusted for the effects of sex, age, age2, age-by-sex interaction, age2-by-sex interaction, body mass index, and field center to remove sources of variation other than the genetic effects related to pulse pressure. Significant linkage was observed on chromosome 18 (logarithm of odds [LOD]=3.2) in a combined racial sample, chromosome 20 (LOD=4.4), and 17 (LOD=3.6) in Hispanics, chromosome 21 (LOD=4.3) in whites, chromosome 19 (LOD=3.1) in a combined sample of blacks and whites, and chromosome 7 (logarithm of odds [LOD]=3.1) in blacks from the GenNet Network. Our genome scan shows significant evidence for linkage for pulse pressure in multiple areas of the genome, supporting previous published linkage studies. The identification of these loci for pulse pressure and the apparent congruence with other blood pressure phenotypes provide increased support that these regions contain genes influencing blood pressure phenotypes.

Adult↗
PubMed2005

Inheritance pattern of Beckwith-Wiedemann syndrome is heterogeneous in 291 families with an affected proband.

Beckwith-Wiedemann syndrome (BWS) is congenital disorder whose molecular etiology is related to genetic and epigenetic mutations on 11p15. The majority of cases of BWS are sporadic, but a substantial proportion are familial, with an unknown inheritance pattern, although autosomal dominant and sex-dependent inheritance have been proposed. We tested the hypothesis that in familial BWS, autosomal dominant inheritance is the primary mode of transmission underlying familial instances. Segregation analysis was performed in 291 families ascertained with an affected child. Individuals were considered to have BWS if they had two of five major features: macroglossia, macrosomia, hypoglycemia at birth, abdominal wall defect, and ear pits or creases. Models of inheritance were tested using pedigree analysis package (PAP) parameterized for a discrete trait. A total of 291 families of an affected proband were included in the study. The analysis was based on a revised general model that included a boundary solution. Sporadic and environmental models were rejected. Overall, the results suggested Mendelian inheritance but under recessive or additive mode of inheritance, which fit the data equally well rather than dominant inheritance. However, the presence of families in the cohort consistent with dominant and sex-dependent inheritance suggest familial BWS may be a heterogeneous group comprised of different inheritance patterns. Familial BWS does not appear to be consistent with autosomal dominant transmission, and is likely a complex mixture of different inheritance patterns.

Beckwith-Wiedemann Syndrome↗
PubMed2005

A haplotype similarity based transmission/disequilibrium test under founder heterogeneity.

Taking advantage of increasingly available high-density single nucleotide polymorphisms (SNP) markers across the genome, various types of transmission/disequilibrium tests (TDT) using haplotype information have been developed. A practical challenge arising in such studies is the possibility that transmitted haplotypes have inherited disease-causing mutations from different ancestral chromosomes, or do not bear any disease-causing mutations (founder heterogeneity). To reduce the loss of signal strength due to founder heterogeneity, we propose an SP-TDT test that combines a sequential peeling procedure with the haplotype similarity based TDT method. The proposed SP-TDT method is applicable to any size of nuclear family with or without ambiguous phase information. Simulation studies suggest that the SP-TDT method has the correct type I error rate in stratified populations, and enhanced power compared with some existing haplotype similarity based TDT methods. Finally, we apply the proposed method to study the association of the leptin gene with obesity from the National Heart, Lung, and Blood Institute Family Heart Study.

Algorithms↗
PubMed2005

Using tree-based recursive partitioning methods to group haplotypes for increased power in association studies.

Motivated by the increasing availability of high-density single nucleotide polymorphism (SNP) markers across the genome, various haplotype-based methods have been developed for candidate gene association studies, and even for genome-wide association studies. Although haplotype approaches dramatically reduce the multiple comparisons problem (as compared to single SNP analysis), even the number of existing haplotypes is relatively large, which increases the degrees of freedom and decreases the power for the corresponding test statistic. Grouping haplotypes is a way to reduce the degrees of freedom. We propose a procedure that uses a tree-based recursive partitioning algorithm to group haplotypes into a small number of clusters, and conducts the association test based on groups of haplotypes instead of individual haplotypes. The method can be used for both population-based and family-based association studies, with known or ambiguous phase information. Simulation studies suggest that the proposed method has the right type I error rate, and is more powerful than some existing haplotype-based tests.

Algorithms↗
PubMed2004

Evidence for a gene influencing fasting LDL cholesterol and triglyceride levels on chromosome 21q.

High levels of low-density lipoprotein (LDL) cholesterol, low levels of high-density lipoprotein (HDL) cholesterol, and high levels of triglycerides (TG) are strong predictors of cardiovascular disease risk. Motivated by previous evidence for pleiotropy between cholesterol and TG levels, we conducted bivariate linkage analysis of LDL cholesterol and TG concentration among participants of the Hypertension Genetic Epidemiolgy Network (HyperGEN), one of four networks in the NHLBI sponsored Family Blood Pressure Program Project. All available hypertensive siblings and their first-degree relatives were recruited. Both phenotypes were similarly adjusted for ethnicity, study center, sex, age, age-by-sex interactions, smoking, alcohol consumption, hormone use, diabetes medication use, and waist circumference. Variance component linkage analysis was performed as implemented in SOLAR, using ethnicity-specific marker allele frequencies derived from founders and multipoint IBDs calculated in MERLIN. A maximum genome-wide empirical LOD score of 3.9 was detected on chromosome 21 at 54cM, between markers D21S2055 and D21S1446. This signal overlaps with suggestive and/or significant linkages for total cholesterol, LDL cholesterol, and apolipoprotein B in three other studies and is suggestive of one or more genes on chromosome 21q jointly regulating LDL cholesterol and TG concentration.

Aged↗
PubMed2004

Genome-wide linkage analysis replicates susceptibility locus for fasting plasma triglycerides: NHLBI Family Heart Study.

Recent reports implicate chromosomal regions linked to inter-individual variation in plasma triglycerides. We conducted genome-wide scans to replicate these linkages and/or identify other loci influencing plasma triglycerides in the NHLBI Family Heart Study (FHS). Data were obtained for 501 three-generational families. Genotyping was done by the Utah Molecular Genetics Laboratory and NHLBI Mammalian Genotyping Service; markers from both were placed on one genetic map. Analysis was done using multipoint variance components linkage. Fasting plasma triglycerides were log-transformed and age-, sex-, and field center-adjusted; suggestive linkage evidence was found on chromosome 8 (LOD=2.80 at 89 cM, marker D8S1141). Further adjustment for waist girth, BMI, diabetes, hypertension, and lipid-lowering drugs suggested linkage regions on chromosomes 6 (LOD=2.29 at 79 cM, marker D6S295) and 15 (LOD=1.85 at 43 cM, marker D15S659). Since HDL is correlated with triglycerides and because it was linked to this region on chromosome 15 in FHS, we created a composite triglyceride-HDL phenotype. The combined phenotype LOD score was 3.0 at the same marker on chromosome 15. Chromosome 15 likely harbors a susceptibility locus with an influence on triglycerides and HDL. Regions on chromosomes 6 and 8 may also contain loci contributing to inter-individual variation in plasma triglycerides.

Chromosome Mapping↗