Biology subjects

Xie, L.

Publications and source records attributed to Xie, L..

18 recordsLinked to original sources

bioRxiv2018

The mitochondrial DNA content can not predict the embryo viability

ObjectiveTo investigate whether the mitochondrial DNA content could predict the embryo viability\n\nDesignRetrospective analysis.\n\nSettingReproductive genetics laboratory\n\nPatient(s)A total of 421 biopsied samples obtained from 129 patients\n\nIntervention(s)Embryo biopsies samples underwent whole genome amplification (WGA) and were tested by next generation sequencing (NGS) and array Comparative Genomic Hybridization (aCGH), 30 samples were selected randomly to undergo quantitative real-time polymerase chain reaction (qPCR).\n\nMain Outcome Measure(s)Those embryos which obtained the consistent chromosome status determined both aCGH and NGS platform were further classified. We investigated the relationship of mtDNA content with several factors including female patient age, embryo morphology, chromosome status, and live birth rate of both blastocysts and blastomeres.\n\nResult(s)A total of 386 (110 blastomeres and 276 blastocysts) out of 399 embryos showed consistent chromosome status outcome. We found no statistically difference was observed in aneuploid and euploid blastocysts (p=0.14), the same phenomenon was observed in aneuploid and euploid blastomeres (p=0.89). Similarly, the mtDNA content was independent of female patient age, embryo morphology and live birth rate.\n\nConclusion(s)The mtDNA content did not provide a reliable prediction of the viability of blastocysts to initiate a pregnancy.

ecology↗

bioRxiv2018

Dynamic Brain Connectivity Alternation Detection via Matrix-variate Differential Network Model

MotivationNowadays brain connectivity analysis has attracted tremendous attention and has been at the foreground of neuroscience research. Brain functional connectivity reveals the synchronization of brain systems through correlations in neurophysiological measures of brain activity. Growing evidence now suggests that the brain connectivity network experiences alternations with the presence of numerous neurological disorders, thus differential brain network analysis may provides new insights into disease pathologies. For the matrix-valued data in brain connectivity analysis, existing graphical model estimation methods assume a vector normal distribution that in essence requires the columns of the matrix data to be independent. It is obviously not true, they have limited applications. Among the few solutions on graphical model estimation under a matrix normal distribution, none of them tackle the estimation of differential graphs across different populations. This motivates us to consider the differential network for matrix-variate data to detect the brain connectivity alternation.\n\nResultsThe primary interest is to detect spatial locations where the connectivity, in terms of the spatial partial correlation, differ across the two groups. To detect the brain connectivity alternation, we innovatively propose a Matrix-Variate Differential Network (MVDN) model. MVDN assumes that the matrix-variate data follows a matrix-normal distribution. We exploit the D-trace loss function and a Lasso-type penalty to directly estimate the spatial differential partial correlation matrix where the temporal information is fully excavated. We propose an ADMM algorithm for the Lasso penalized D-trace loss optimization problem. We investigate theoretical properties of the estimator. We show that under mild and regular conditions, the proposed method can identify all differential edges accurately with probability tending to 1 in high-dimensional setting where dimensions of matrix-valued data p, q and sample size n are all allowed to go to infinity. Simulation studies demonstrate that MVDN provides more accurate differential network estimation than that achieved by other state-of-the-art methods. We apply MVDN to Electroencephalography (EEG) dataset, which consists of 77 alcoholic individuals and 45 controls. The hub genes and differential interaction patterns identified are consistent with existing experimental studies.\n\nContactheyong@sdufe.edu.cn\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2018

Gata4 drives Hh-signaling for second heart field migration and outflow tract development

Dominant mutations of Gata4, an essential cardiogenic transcription factor (TF), cause outflow tract (OFT) defects in both human and mouse. We investigated the molecular mechanism underlying this requirement. Gata4 happloinsufficiency in mice caused OFT defects including double outlet right ventricle (DORV) and conal ventricular septum defects (VSDs). We found that Gata4 is required within Hedgehog (Hh)-receiving second heart field (SHF) progenitors for normal OFT alignment. Increased Pten-mediated cell-cycle transition, rescued atrial septal defects but not OFT defects in Gata4 heterozygotes. SHF Hh-receiving cells failed to migrate properly into the proximal OFT cushion in Gata4 heterozygote embryos. We find that Hh signaling and Gata4 genetically interact for OFT development. Gata4 and Smo double heterozygotes displayed more severe OFT abnormalities including persistent truncus arteriosus (PTA) whereas restoration of Hedgehog signaling rescued OFT defects in Gata4-mutant mice. In addition, enhanced expression of the Gata6 was observed in the SHF of the Gata4 heterozygotes. These results suggested a SHF regulatory network comprising of Gata4, Gata6 and Hh-signaling for OFT development. This study indicates that Gata4 potentiation of Hh signaling is a general feature of Gata4-mediated cardiac morphogenesis and provides a model for the molecular basis of CHD caused by dominant transcription factor mutations.\n\nAuthor SummaryGata4 is an important protein that controls the development of the heart. Human who possess a single copy of Gata4 mutation display congenital heart defects (CHD), including the double outlet right ventricle (DORV). DORV is an alignment problem in which both the Aorta and Pulmonary Artery originate from the right ventricle, instead of originating from the left and the right ventricles, respectively. To study how Gata4 mutation causes DORV, we used a Gata4 mutant mouse model, which displays DORV. We showed that Gata4 is required in the cardiac precursor cells for the normal alignment of the great arteries. Although Gata4 mutation inhibits the rapid increase in number of the cardiac precursor cells, rescuing this defects does not recover the normal alignment of the great arteries. In addition, there is a movement problem of the cardiac precursor cells when migrating toward the great arteries during development. We further showed that a specific molecular signaling, Hh-signaling, is responsible to the Gata4 action in the cardiac precursor cells. Importantly, over-activating the Hh-signaling rescues the DORV in the Gata4 mutant embryos. This study provides an explanation for the ontogeny of CHD.

developmental biology↗

bioRxiv2018

A new insight into underlying disease mechanism through semi-parametric latent differential network model

BackgroundIn genomic studies, to investigate how the structure of a genetic network differs between two experiment conditions is a very interesting but challenging problem, especially in high-dimensional setting. Existing literatures mostly focus on differential network modelling for continuous data. However, in real application, we may encounter discrete data or mixed data, which urges us to propose a unified differential network modelling for various data types.\n\nResultsWe propose a unified latent Gaussian copula differential network model which provides deeper understanding of the unknown mechanism than that among the observed variables. Adaptive rank-based estimation approaches are proposed with the assumption that the true differential network is sparse. The adaptive estimation approaches do not require precision matrices to be sparse, and thus can allow the individual networks to contain hub nodes. Theoretical analysis shows that the proposed methods achieve the same parametric convergence rate for both the difference of the precision matrices estimation and differential structure recovery, which means that the extra modeling flexibility comes at almost no cost of statistical efficiency. Besides theoretical analysis, thorough numerical simulations are conducted to compare the empirical performance of the proposed methods with some other state-of-the-art methods. The result shows that the proposed methods work quite well for various data types. The proposed method is then applied on gene expression data associated with lung cancer to illustrate its empirical usefulness.\n\nConclusionsThe proposed latent variable differential network models allows for various data-types and thus are more flexible, which also provide deeper understanding of the unknown mechanism than that among the observed variables. Theoretical analysis, numerical simulation and real application all demonstrate the great advantages of the latent differential network modelling and thus are highly recommended.

bioinformatics↗

bioRxiv2018

Resolving challenges in quantitative modeling of microbial community dynamics

Microbial communities can perform biochemical activities that monocultures cannot. Controlling communities requires an understanding of community dynamics. Here, we mathematically predict the growth rate of an engineered community consisting of two S. cerevisiae strains, each releasing a metabolite required and consumed by the partner. Initial model parameters were based on strain phenotypes measured in batch mono-cultures with zero or excess metabolite, and failed to quantitatively predict experimental results. To resolve model-experiment discrepancy, we chemically identified the correct exchanged metabolites, but this did not improve model performance. We then re-measured strain phenotypes in chemostats mimicking the metabolite-limited community environments, while mitigating or incorporating effects of rapid evolution. Almost all phenotypes we measured varied significantly with the metabolite environment. Once we used parameters measured in community-like chemostat environments, prediction agreed with experimental results. In summary, using a simplified community, we uncovered, and devised means to resolve, modeling challenges that are likely general.

microbiology↗

bioRxiv2018

Microscopy quantification of microbial birth and death dynamics

Microbes live in dynamic environments where nutrient concentrations fluctuate. Quantifying fitness (birth and death) in a wide range of environments is critical for understanding microbial evolution as well as ecological interactions where one species alters the fitness of another. Here, using high-throughput time-lapse microscopy, we have quantified how Saccharomyces cerevisiae mutants incapable of synthesizing an essential metabolite grow or die in various concentrations of the required metabolite. We establish that cells normally expressing fluorescent proteins lose fluorescence upon death and that the total fluorescence in an imaging frame is proportional to the number of live cells even when cells form multiple layers. We validate our microscopy approach of measuring birth and death rates using flow cytometry, cell counting, and chemostat culturing. For lysine-requiring cells, very low concentrations of lysine are not detectably consumed and do not support cell birth, but delay the onset of death phase and reduce the death rate. In contrast, in low hypoxanthine, hypoxanthine-requiring cells can produce new cells, yet also die faster than in the absence of hypoxanthine. For both strains, birth rates under various metabolite concentrations are better described by the sigmoidal-shaped Moser model than the well-known Monod model, while death rates depend on the metabolite concentration and can vary with time. Our work reveals how time-lapse microscopy can be used to discover non-intuitive microbial dynamics and to quantify growth rates in many environments.

microbiology↗

bioRxiv2018

Reducing compositional fluctuations facilitates artificial selection of microbial community function

Multi-species microbial communities often display functions - biochemical activities unattainable by member species alone, such as fighting pathogens. To improve community function, we can artificially select communities by growing \"Newborn\" communities over \"maturation time\" into \"Adult\" communities, and selecting highest-functioning Adults to \"reproduce\" by diluting each into multiple Newborns of the next cycle. Community selection has been attempted a few times on complex communities, often generating mixed results that are difficult to interpret. Here, we ask how costly community function may be improved via mutations and community selection. We simulate selection of two-species communities where Helpers digest Waste and generate Byproduct essential to Manufacturers; Manufacturers divert a fraction of their growth to make Product. Community function, the total Product in an \"Adult\", is sub-optimal even when both species have been pre-optimized as monocultures. If we dilute an Adult into Newborns by pipetting (a common experimental procedure), stochastic fluctuations in Newborn composition prevents community function from improving. Reducing fluctuations via cell sorting allows selection to work. Our conclusions hold regardless of whether H and M are commensal or mutualistic, or variations in model assumptions.

evolutionary biology↗

bioRxiv2018

Community function landscape and steady state species composition shape the eco-evolutionary dynamics of arti1cial community selection

Microbial communities often perform important functions that arise from interactions among member species. Community functions can be improved via artificial selection: Many communities are repeatedly grown, mutations arise, and communities with the highest desired function are chosen to reproduce where each is partitioned into multiple offspring communities for the next cycle. Since selection efficacy is often unimpressive in published experiments and since multiple experimental parameters need to be tuned, we sought to use computer simulations to learn how to design effective selection strategies. We simulated community selection to improve a community function that requires two species and imposes a fitness cost on one of the species. This simplified case allowed us to distill community function down to two fundamental and orthogonal components: a heritable determinant and a nonheritable determinant. We then visualize a "community function landscape" relating community function to these two determinants, and demonstrate that the evolutionary trajectory on the landscape is restricted along a path designated by ecological interactions. This path can prevent the attainment of maximal community function, and trap communities in landscape locations where community function has low heritability. Exploiting these observations, we devise a species spiking approach to shift the path to improve community function heritability and consequently selection efficacy. We show that our approach is applicable to communities with complex and unknown function landscapes.

evolutionary biology↗

bioRxiv2018

A stable mode of bookmarking by TBP recruits RNA Polymerase II to mitotic chromosomes

How a cell maintains transcriptional fidelity across mitosis has remained an enduring mystery in biology. One challenge arises during mitosis when chromatin becomes condensed and transcription is shut off. How do the daughter cells re-establish the original transcription program? Here, we report that the TATA-binding protein (TBP), a key component of the core transcriptional machinery, remains bound globally to active promoters in ES cells during mitosis. Using live-cell single-molecule imaging, we observed that TBP mitotic binding is highly stable, with an average residence time of minutes. This stable binding is in stark contrast to typical TFs with residence times of seconds. To test the functional effect of mitotic TBP binding, we used a drug-inducible degron system and found that TBP promotes the association of RNA Polymerase II with mitotic chromosomes, and facilitates transcriptional reactivation following mitosis. These results suggest that the core transcriptional machinery maintains global transcriptional memory during mitosis.

molecular biology↗

bioRxiv2017

Building a schizophrenia genetic network: Evidence that Transcription Factor 4 regulates schizophrenia risk genes.

The transcription factor 4 (TCF4) locus is a robust association finding with schizophrenia (SZ), but little is known about the genes regulated by the encoded transcription factor. Therefore, we conducted chromatin immunoprecipitation sequencing (ChIP-seq) of TCF4 in neural-derived (SH-SY5Y) cells to identify genome-wide TCF4 binding sites, followed by data integration with SZ association findings. We identified 11,322 TCF4 binding sites overlapping in two ChIP-seq experiments. These sites are significantly enriched for the TCF4 Ebox binding motif (>85% having [≥]1 Ebox) and implicate a gene set enriched for genes down-regulated in TCF4 siRNA knockdown experiments, indicating the validity of our findings. The TCF4 gene set was also enriched among 1) Gene Ontology categories such as axon/neuronal development, 2) genes preferentially expressed in brain, in particular pyramidal neurons of the somatosensory cortex, and 3) genes down-regulated in post-mortem brain tissue from SZ patients (OR=2.8, permutation p<4x10-5). Considering genomic alignments, TCF4 binding sites significantly overlapped those for neural DNA binding proteins such as FOXP2 and the SZ-associated EP300. TCF4 binding sites were modestly enriched among SZ risk loci from the Psychiatric Genomic Consortium (OR=1.56, p=0.03). In total, 130 TCF4 binding sites occurred in 39 of the 108 regions published in 2014. Thirteen genes within the 108 loci had both a TCF4 binding site {+/-}10kb and were differentially expressed in siRNA knockdown experiments of TCF4, suggesting direct TCF4 regulation. These findings confirm TCF4 as an important regulator of neural genes and point towards functional interactions with potential relevance for SZ.

genetics↗

bioRxiv2017

ANTENNA, a Multi-Rank, Multi-Layered Recommender System for Inferring Reliable Drug-Gene-Disease Associations: Repurposing Diazoxide as a Targeted Anti-Cancer Therapy

Existing1drug discovery process follows a reductionist model of \"one-drug-one-gene-one-disease,\" which is not adequate to tackle complex diseases that involve multiple malfunctioned genes. The availability of big omics data offers new opportunities to transform the drug discovery process into a new paradigm of systems pharmacology that focuses on designing drugs to target molecular interaction networks instead of a single gene. Here, we develop a reliable multi-rank, multi-layered recommender system, ANTENNA, to mine large-scale chemical genomics and disease association data for the prediction of novel drug-gene-disease associations. ANTENNA integrates a novel tri-factorization based dual-regularized weighted and imputed One Class Collaborative Filtering (OCCF) algorithm, tREMAP, with a statistical framework that is based on Random Walk with Restart and can assess the reliability of a specific prediction. In the benchmark study, tREMAP clearly outperforms the single rank OCCF. We apply ANTENNA to a real-world problem: repurposing old drugs for new clinical indications that have yet had an effective treatment. We discover that FDA-approved drug diazoxide can inhibit multiple kinase genes whose malfunction is responsible for many diseases including cancer, and kill triple negative breast cancer (TNBC) cells effectively at a low concentration (IC50 = 0.87 M). The TNBC is a deadly disease that currently does not have effective targeted therapies. Our finding demonstrates the power of big data analytics in drug discovery, and has a great potential toward developing a targeted therapy for the effective treatment of TNBC.

bioinformatics↗

bioRxiv2017

Mitotic chromosomes fold by condensin-dependent helical winding of chromatin loop arrays

During mitosis, chromosomes fold into compacted rod shaped structures. We combined imaging and Hi-C of synchronous DT40 cell cultures with polymer simulations to determine how interphase chromosomes are converted into compressed arrays of loops characteristic of mitotic chromosomes. We found that the interphase organization is disassembled within minutes of prophase entry and by late prophase chromosomes are already folded as arrays of consecutive loops. During prometaphase, this array reorganizes to form a helical arrangement of nested loops. Polymer simulations reveal that Hi-C data are inconsistent with solenoidal coiling of the entire chromatid, but instead suggest a centrally located helically twisted axis from which consecutive loops emanate as in a spiral staircase. Chromosomes subsequently shorten through progressive helical winding, with the numbers of loops per turn increasing so that the size of a helical turn grows from around 3 Mb (~40 loops) to ~12 Mb (~150 loops) in fully condensed metaphase chromosomes. Condensin is essential to disassemble the interphase chromatin conformation. Analysis of mutants revealed differing roles for condensin I and II during these processes. Either condensin can mediate formation of loop arrays. However, condensin II was required for helical winding during prometaphase, whereas condensin I modulated the size and arrangement of loops inside the helical turns. These observations identify a mitotic chromosome morphogenesis pathway in which folding of linear loop arrays produces long thin chromosomes during prophase that then shorten by progressive growth of loops and helical winding during prometaphase.\n\nOne Sentence SummaryMitotic chromosome morphogenesis occurs through condensin-mediated disassembly of the interphase conformation and formation of extended prophase loop arrays that then shorten by loop growth and condensin-dependent helical winding.

genomics↗

bioRxiv2017

Rapid degradation and 3D CLEM of condensin reveal chromatin compaction uncoupled from chromosome architecture in mitosis

The requirement for condensin in chromosome formation in somatic cells remains unclear as imperfectly condensed chromosomes do form in conventional condensindepleted cells. Here we have dissected the role of condensin at different stages of vertebrate mitosis by combining auxin-mediated rapid depletion of condensin subunit SMC2 with chemical genetics to obtain near-synchronous mitotic entry of chicken DT40 cells. We analysed the outcomes by live and fixed-cell microscopy methods, including 3D correlative light and serial block face scanning electron microscopy. Following rapid depletion of condensin, chromosomal defects were obvious. The chromatin was compacted normally, but formed a single mass of mitotic chromosomes clustered at one side of a bent mitotic spindle. Cultures arrest at prometaphase, eventually exiting mitosis without segregating chromosomes. Experiments titrating the auxin concentration suggest a previously unsuspected dual role of condensin, as different condensin levels are required for anaphase chromosome segregation and formation of a normal chromosome architecture.

cell biology↗

bioRxiv2017

Predicting serious rare adverse reactions of novel chemicals

Adverse drug reactions (ADRs) are one of the main causes of death and a major financial burden on the worlds economy. Due to the limitations of the animal model, computational prediction of serious, rare ADRs is invaluable. However, current state-of-the-art computational methods do not yield significantly better predictions of rare ADRs than random guessing. We present a novel method, based on the theory of \"compressed sensing\", which can accurately predict serious side-effects of candidate and market drugs. Not only is our method able to infer new chemical-ADR associations using existing noisy, biased, and incomplete databases, but our data also demonstrates that the accuracy of our approach in predicting a serious adverse reaction (ADR) for a candidate drug increases with increasing knowledge of other ADRs associated with the drug. In practice, this means that as the candidate drug moves up the different stages of clinical trials, the prediction accuracy of our method will increase accordingly. Thus, the compressed sensing based computational method reported here represents a major advance in predicting severe rare ADRs, and may facilitate reducing the time and cost of drug discovery and development.

pharmacology and toxicology↗

bioRxiv2017

SWELL1 is a glucose sensor required for β-cell excitability and insulin secretion

Insulin secretion from the pancreatic {beta}-cell initiated by activation of voltage-gated Ca2+ channels (VGCC) to trigger Ca2+-mediated insulin vesicle fusion with the {beta}-cell plasma membrane. The firing of VGCC depends on the {beta}-cell membrane potential, which is in turn mediated by the balance of depolarizing (excitatory) and hyperpolarizing (inhibitory) ionic currents1-3. While much attention has focused on inhibitory potassium currents4-10 there is little knowledge about the excitatory currents required to depolarize the {beta}-cell, including the molecular identity of these excitatory currents3. Here we show that SWELL1 (LRRC8a) mediates a swell-activated, depolarizing chloride current (ICl,SWELL) in {beta}-cells. Hypotonic and glucose-stimulated {beta}-cell swelling activates SWELL1-mediated ICl,SWELL and this is required for both glucose-stimulated and hypotonic swell-mediated activation of VGCC-dependent intracellular calcium signaling in {beta}-cells. SWELL1 KO MIN6 cells and {beta}-cell targeted SWELL1 KO murine islets exhibit significantly impaired glucose-stimulated insulin secretion, with preserved insulin content in vitro. Tamoxifen-inducible {beta}-cell targeted SWELL1 KO mice have normal fasting insulin levels but display markedly impaired glucose-stimulated insulin secretion. Our results reveal a physiological role for SWELL1 as a glucose sensor - linking glucose-mediated {beta}-cell swelling to SWELL1-dependent activation of VGCC-triggered calcium signaling, and highlights SWELL1-mediated \"swell-secretion\" coupling as required for glucose-stimulated insulin secretion.

cell biology↗

bioRxiv2017

Intestinal cell kinase regulates chondrocyte proliferation and maturation during skeletal development

An autosomal recessive loss-of-function mutation R272Q in human ICK (intestinal cell kinase) gene causes profound multiplex developmental defects in human ECO (endocrine-cerebro-osteodysplasia) syndrome. ECO patients exhibit a wide variety of skeletal abnormalities, yet the underlying cellular and molecular mechanisms by which ICK regulates skeletal development remain largely unknown. The goal of this study is to understand the structural and mechanistic basis underlying skeletal anomalies caused by ICK dysfunction. Ick R272Q knock in transgenic mouse model not only recapitulated major ECO skeletal defects such as short limbs and polydactyly but also revealed a deformed spine with deficient intervertebral disc. Loss of ICK functions markedly reduces mineralization in the spinal column, ribs, and long bones. Ick mutants show a significant decrease in the number of proliferating chondrocytes and type X collagen-expressing hypertrophic chondrocytes in the spinal column and the growth plate of long bones. Our results demonstrate that ICK plays an important role in bone and intervertebral disc development by promoting chondrocyte proliferation and maturation, and thus provide novel mechanistic insights into the skeletal phenotypes of human ECO syndrome.

developmental biology↗

bioRxiv2017

Gut microbiota analysis in rats with methamphetamine-induced conditioned place preference

Methamphetamine abuse is a major public health crisis. Because accumulating evidence supports the hypothesis that the gut microbiota plays an important role in central nervous system (CNS) function, and research on the roles of the microbiome in CNS disorders holds conceivable promise for developing novel therapeutic avenues for treating CNS disorders, we sought to determine whether administration of methamphetamine leads to alterations in the intestinal microbiota. In this study, the gut microbiota profiles of rats with methamphetamine-induced conditioned place preference (CPP) were analysed through 16S rRNA gene sequencing. The faecal microbial diversity was slightly higher in the METH CPP group. The propionate-producing genus Phascolarctobacterium was attenuated in the METH CPP group, and the family Ruminococcaceae was elevated in the METH CPP group. Short chain fatty acid analysis revealed that the concentrations of propionate were decreased in the faecal matter of METH-administered rats. These findings provide direct evidence that administration of METH causes gut dysbiosis, enable a better understanding of the function of gut microbiota in the process of drug abuse, and provide a new paradigm for addiction treatment.

microbiology↗

bioRxiv2017

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

MotivationA complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application.\n\nResultsWe propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data.\n\nAvailabilityR scripts available at https://github.com/jijiadong/JDINAC\n\nContactlxie@iscb.org\n\nSupplementary informationSupplementary data are available at bioRxiv online.

systems biology↗