Improved drug response prediction by drug target data integration via network-based profiling.

Drug response prediction (DRP) is important for precision medicine to predict how a patient would react to a drug before administration. Existing studies take the cell line transcriptome data, and the chemical structure of drugs as input and predict drug response as IC50 or AUC values. Intuitively, use of drug target interaction (DTI) information can be useful for DRP. However, use of DTI is difficult because existing drug response database such as CCLE and GDSC do not have information about transcriptome after drug treatment. Although transcriptome after drug treatment is not available, if we can compute the perturbation effects by the pharmacologic modulation of target gene, we can utilize the DTI information in CCLE and GDSC. In this study, we proposed a framework that can improve existing deep learning-based DRP models by effectively utilizing drug target information. Our framework includes NetGP, a module to compute gene perturbation scores by the network propagation technique on a network. NetGP produces genes in a ranked list in terms of gene perturbation scores and the ranked genes are input to a multi-layer perceptron to generate a fixed dimension vector for the integration with existing DRP models. This integration is done in a model-agnostic way so that any existing DRP tool can be incorporated. As a result, our framework boosts the performance of existing DRP models, in 64 of 72 comparisons. The performance gains are larger especially for test scenarios with samples with unseen drugs by large margins up to 34% in Pearson's correlation coefficient.

Transfer learning of condition-specific perturbation in gene interactions improves drug response prediction.

SUMMARY: Drug response is conventionally measured at the cell level, often quantified by metrics like IC50. However, to gain a deeper understanding of drug response, cellular outcomes need to be understood in terms of pathway perturbation. This perspective leads us to recognize a challenge posed by the gap between two widely used large-scale databases, LINCS L1000 and GDSC, measuring drug response at different levels-L1000 captures information at the gene expression level, while GDSC operates at the cell line level. Our study aims to bridge this gap by integrating the two databases through transfer learning, focusing on condition-specific perturbations in gene interactions from L1000 to interpret drug response integrating both gene and cell levels in GDSC. This transfer learning strategy involves pretraining on the transcriptomic-level L1000 dataset, with parameter-frozen fine-tuning to cell line-level drug response. Our novel condition-specific gene-gene attention (CSG2A) mechanism dynamically learns gene interactions specific to input conditions, guided by both data and biological network priors. The CSG2A network, equipped with transfer learning strategy, achieves state-of-the-art performance in cell line-level drug response prediction. In two case studies, well-known mechanisms of drugs are well represented in both the learned gene-gene attention and the predicted transcriptomic profiles. This alignment supports the modeling power in terms of interpretability and biological relevance. Furthermore, our model's unique capacity to capture drug response in terms of both pathway perturbation and cell viability extends predictions to the patient level using TCGA data, demonstrating its expressive power obtained from both gene and cell levels. AVAILABILITY AND IMPLEMENTATION: The source code for the CSG2A network is available at https://github.com/eugenebang/CSG2A.

GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype.

MOTIVATION: Asthma is a heterogeneous disease where various subtypes are established and molecular biomarkers of the subtypes are yet to be discovered. Recent availability of multi-omics data paved a way to discover molecular biomarkers for the subtypes. However, multi-omics biomarker discovery is challenging because of the complex interplay between different omics layers. RESULTS: We propose a deep attention model named Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network (GOAT) for identifying molecular biomarkers for eosinophilic asthma subtypes with multi-omics data. GOAT identifies genes that discriminate subtypes using a graph neural network by modeling complex interactions among genes as the attention mechanism in the deep learning model. In experiments with multi-omics profiles of the COREA (Cohort for Reality and Evolution of Adult Asthma in Korea) asthma cohort of 300 patients, GOAT outperforms existing models and suggests interpretable biological mechanisms underlying asthma subtypes. Importantly, GOAT identified genes that are distinct only in terms of relationship with other genes through attention. To better understand the role of biomarkers, we further investigated two transcription factors, CTNNB1 and JUN, captured by GOAT. We were successful in showing the role of the transcription factors in eosinophilic asthma pathophysiology in a network propagation and transcriptional network analysis, which were not distinct in terms of gene expression level differences. AVAILABILITY AND IMPLEMENTATION: Source code is available https://github.com/DabinJeong/Multi-omics_biomarker. The preprocessed data underlying this article is accessible in data folder of the github repository. Raw data are available in Multi-Omics Platform at http://203.252.206.90:5566/, and it can be accessible when requested.

Metheor: Ultrafast DNA methylation heterogeneity calculation from bisulfite read alignments.

Phased DNA methylation states within bisulfite sequencing reads are valuable source of information that can be used to estimate epigenetic diversity across cells as well as epigenomic instability in individual cells. Various measures capturing the heterogeneity of DNA methylation states have been proposed for a decade. However, in routine analyses on DNA methylation, this heterogeneity is often ignored by computing average methylation levels at CpG sites, even though such information exists in bisulfite sequencing data in the form of phased methylation states, or methylation patterns. In this study, to facilitate the application of the DNA methylation heterogeneity measures in downstream epigenomic analyses, we present a Rust-based, extremely fast and lightweight bioinformatics toolkit called Metheor. As the analysis of DNA methylation heterogeneity requires the examination of pairs or groups of CpGs throughout the genome, existing softwares suffer from high computational burden, which almost make a large-scale DNA methylation heterogeneity studies intractable for researchers with limited resources. In this study, we benchmark the performance of Metheor against existing code implementations for DNA methylation heterogeneity measures in three different scenarios of simulated bisulfite sequencing datasets. Metheor was shown to dramatically reduce the execution time up to 300-fold and memory footprint up to 60-fold, while producing identical results with the original implementation, thereby facilitating a large-scale study of DNA methylation heterogeneity profiles. To demonstrate the utility of the low computational burden of Metheor, we show that the methylation heterogeneity profiles of 928 cancer cell lines can be computed with standard computing resources. With those profiles, we reveal the association between DNA methylation heterogeneity and various omics features. Source code for Metheor is at https://github.com/dohlee/metheor and is freely available under the GPL-3.0 license.

Prediction of tumor purity from gene expression data using machine learning.

MOTIVATION: Bulk tumor samples used for high-throughput molecular profiling are often an admixture of cancer cells and non-cancerous cells, which include immune and stromal cells. The mixed composition can confound the analysis and affect the biological interpretation of the results, and thus, accurate prediction of tumor purity is critical. Although several methods have been proposed to predict tumor purity using high-throughput molecular data, there has been no comprehensive study on machine learning-based methods for the estimation of tumor purity. RESULTS: We applied various machine learning models to estimate tumor purity. Overall, the models predicted the tumor purity accurately and showed a high correlation with well-established gold standard methods. In addition, we identified a small group of genes and demonstrated that they could predict tumor purity well. Finally, we confirmed that these genes were mainly involved in the immune system. AVAILABILITY: The machine learning models constructed for this study are available at https://github.com/BonilKoo/ML_purity.

A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective.

Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations.

Oxidation Induced Doping of Nanoparticles Revealed by in Situ X-ray Absorption Studies.

Doping is a well-known approach to modulate the electronic and optical properties of nanoparticles (NPs). However, doping at nanoscale is still very challenging, and the reasons for that are not well understood. We studied the formation and doping process of iron and iron oxide NPs in real time by in situ synchrotron X-ray absorption spectroscopy. Our study revealed that the mass flow of the iron triggered by oxidation is responsible for the internalization of the dopant (molybdenum) adsorbed at the surface of the host iron NPs. The oxidation induced doping allows controlling the doping levels by varying the amount of dopant precursor. Our in situ studies also revealed that the dopant precursor substantially changes the reaction kinetics of formation of iron and iron oxide NPs. Thus, in the presence of dopant precursor we observed significantly faster decomposition rate of iron precursors and substantially higher stability of iron NPs against oxidation. The same doping mechanism and higher stability of host metal NPs against oxidation was observed for cobalt-based systems. Since the internalization of the adsorbed dopant at the surface of the host NPs is driven by the mass transport of the host, this mechanism can be potentially applied to introduce dopants into different oxidized forms of metal and metal alloy NPs providing the extra degree of compositional control in material design.

How "hollow" are hollow nanoparticles?

Diamond anvil cell (DAC), synchrotron X-ray diffraction (XRD), and small-angle X-ray scattering (SAXS) techniques are used to probe the composition inside hollow γ-Fe(3)O(4) nanoparticles (NPs). SAXS experiments on 5.2, 13.3, and 13.8 nm hollow-shell γ-Fe(3)O(4) NPs, and 6 nm core/14.8 nm hollow-shell Au/Fe(3)O(4) NPs, reveal the significantly high (higher than solvent) electron density of the void inside the hollow shell. In high-pressure DAC experiments using Ne as pressure-transmitting medium, formation of nanocrystalline Ne inside hollow NPs is not detected by XRD, indicating that the oxide shell is impenetrable. Also, FTIR analysis on solutions of hollow-shell γ-Fe(3)O(4) NPs fragmented upon refluxing shows no evidence of organic molecules from the void inside, excluding the possibility that organic molecules get through the iron oxide shell during synthesis. High-pressure DAC experiments on Au/Fe(3)O(4) core/hollow-shell NPs show good transmittance of the external pressure to the gold core, indicating the presence of the pressure-transmitting medium in the gap between the core and the hollow shell. Overall, our data reveal the presence of most likely small fragments of iron and/or iron oxide in the void of the hollow NPs. The iron oxide shell seems to be non-porous and impenetrable by gases and liquids.

Hollow iron oxide nanoparticles for application in lithium ion batteries.

Material design in terms of their morphologies other than solid nanoparticles can lead to more advanced properties. At the example of iron oxide, we explored the electrochemical properties of hollow nanoparticles with an application as a cathode and anode. Such nanoparticles contain very high concentration of cation vacancies that can be efficiently utilized for reversible Li ion intercalation without structural change. Cycling in high voltage range results in high capacity (∼132 mAh/g at 2.5 V), 99.7% Coulombic efficiency, superior rate performance (133 mAh/g at 3000 mA/g) and excellent stability (no fading at fast rate during more than 500 cycles). Cation vacancies in hollow iron oxide nanoparticles are also found to be responsible for the enhanced capacity in the conversion reactions. We monitored in situ structural transformation of hollow iron oxide nanoparticles by synchrotron X-ray absorption and diffraction techniques that provided us clear understanding of the lithium intercalation processes during electrochemical cycling.

Copper selenide nanocrystals for photothermal therapy.

Ligand-stabilized copper selenide (Cu(2-x)Se) nanocrystals, approximately 16 nm in diameter, were synthesized by a colloidal hot injection method and coated with amphiphilic polymer. The nanocrystals readily disperse in water and exhibit strong near-infrared (NIR) optical absorption with a high molar extinction coefficient of 7.7 × 10(7) cm(-1) M(-1) at 980 nm. When excited with 800 nm light, the Cu(2-x)Se nanocrystals produce significant photothermal heating with a photothermal transduction efficiency of 22%, comparable to nanorods and nanoshells of gold (Au). In vitro photothermal heating of Cu(2-x)Se nanocrystals in the presence of human colorectal cancer cell (HCT-116) led to cell destruction after 5 min of laser irradiation at 33 W/cm(2), demonstrating the viabilitiy of Cu(2-x)Se nanocrystals for photothermal therapy applications.

Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set Selection.

Patient stratification is a clinically important task because it allows us to establish and develop efficient treatment strategies for particular groups of patients. Molecular subtypes have been successfully defined using transcriptomic profiles, and they are used effectively in clinical practice, e.g., PAM50 subtypes of breast cancer. Survival prediction contributed to understanding diseases and also identifying genes related to prognosis. It is desirable to stratify patients considering these two aspects simultaneously. However, there are no methods for patient stratification that consider molecular subtypes and survival outcomes at once. Here, we propose a methodology to deal with the problem. A genetic algorithm is used to select a gene set from transcriptome data, and their expression quantities are utilized to assign a risk score to each patient. The patients are ordered and stratified according to the score. A gene set was selected by our method on a breast cancer cohort (TCGA-BRCA), and we examined its clinical utility using an independent cohort (SCAN-B). In this experiment, our method was successful in stratifying patients with respect to both molecular subtype and survival outcome. We demonstrated that the orders of patients were consistent across repeated experiments, and prognostic genes were successfully nominated. Additionally, it was observed that the risk score can be used to evaluate the molecular aggressiveness of individual patients.

Synthesis of CuInSe(2) nanocrystals with trigonal pyramidal shape.

The synthesis of monodisperse chalcopyrite (tetragonal) CuInSe(2) nanocrystals is reported. The nanocrystals have trigonal pyramidal shape, and they exhibited a common crystallographic orientation when drop-cast onto carbon substrates. A crystallographic model for the nanocrystals was developed. The nanocrystals are bounded by one polar {112} surface facet and three nonpolar {114} surface facets.

Synthesis of Cu(2)ZnSnS(4) nanocrystals for use in low-cost photovoltaics.

Cu(2)ZnSnS(4) (CZTS) is a promising new material for thin-film solar cells. Nanocrystal dispersions, or solar paints, present an opportunity to significantly reduce the production cost of photovoltaic devices. This communication demonstrates the colloidal synthesis of CZTS nanocrystals and their use in fabricating prototype solar cells with a power conversion efficiency of 0.23% under AM 1.5 illumination.

Structural characterization and temperature-dependent photoluminescence of linear CdTe/CdSe/CdTe heterostructure nanorods.

Linear CdTe|CdSe|CdTe heterostructure nanorods are synthesized by using a colloidal sequential reactant injection technique [Shieh et al., J. Phys. Chem. B 2005, 109, 8538-8542]. The composition profiles of the individual nanorods are verified by using nanobeam elemental mapping by energy dispersive X-ray spectroscopy (EDS) and the photoluminescence emission spectra of the linear CdTe|CdSe|CdTe heterostructure nanorods are measured as a function of the temperature (down to 5 K). Photoluminescence is observed to occur from electron-hole recombination in both the CdSe core and across the heterojunction. Thermally activated trapping is found to influence both luminescence processes, thereby being more significant for the type II recombination across the CdSe|CdTe interface.

Simultaneous phase- and size-controlled synthesis of TiO(2) nanorods via non-hydrolytic sol-gel reaction of syringe pump delivered precursors.

The simultaneous phase- and size-controlled synthesis of TiO(2) nanorods was achieved via the non-hydrolytic sol-gel reaction of continuously delivered two titanium precursors using two separate syringe pumps. As the injection rate was decreased, the length of the TiO(2) nanorods was increased and their crystalline phase was simultaneously transformed from anatase to rutile. When the reaction was performed by injecting titanium precursors contained in two separate syringes into a hot oleylamine surfactant solution with an injection rate of 30 mL/h, anatase TiO(2) nanorods with dimensions of 6 nm (thickness) x 50 nm (length) were produced. When the injection rate was decreased to 2.5 mL/h, star-shaped rutile TiO(2) nanorods with dimensions of 25 nm x 200 nm and a small fraction of rod-shaped anatase TiO(2) nanorods with dimensions of 9 nm x 100 nm were synthesized. Pure star-shaped rutile TiO(2) nanorods with dimensions of 25 nm x 450 nm were synthesized when the injection rate was further decreased to 1.25 mL/h. The simultaneous phase transformation and length elongation of the TiO(2) nanorods were achieved. Under optimized reaction conditions, as much as 3.5 g of TiO(2) nanorods were produced. The TiO(2) nanorods were used to produce dye-sensitized solar cells, and the photoconversion efficiency of the mixture composed of star-shaped rutile TiO(2) nanorods and a small fraction of anatase nanorods were comparable to that of Degussa P-25.

Synthesis and magnetic properties of silica-coated FePt nanocrystals.

Colloidal FePt nanocrystals, 6 nm in diameter, were synthesized and then coated with silica (SiO2) shells. The silica shell thickness could be varied from 10 to 25 nm. As-made FePt@SiO2 nanocrystals have low magnetocrystalline anisotropy due to a compositionally disordered FePt core. When films of FePt@SiO2 particles are annealed under hydrogen at 650 degrees C or above, the FePt core transforms to the compositionally ordered L1(0) phase, and superparamagnetic blocking temperatures exceeding room temperature are obtained. The SiO2 shell prevents FePt coalescence at annealing temperatures up to approximately 850 degrees C. Annealing under air or nitrogen does not induce the FePt phase transition. The silica shell limits magnetic dipole coupling between the FePt nanocrystals; however, low temperature (5 K) and room temperature magnetization scans show slightly constricted hysteresis loops with coercivities that decrease systematically with decreased shell thickness, possibly resulting from differences in magnetic dipole coupling between particles.

Surface functionalization of semiconductor and oxide nanocrystals with small inorganic oxoanions (PO4(3-), MoO4(2-)) and polyoxometalate ligands.

In this work, we study the functionalization of the nanocrystal (NC) surface with inorganic oxo ligands, which bring a new set of functionalities to all-inorganic colloidal nanomaterials. We show that simple inorganic oxoanions, such as PO4(3-) and MoO4(2-), exhibit strong binding affinity to the surface of various II-VI and III-V semiconductor and metal oxide NCs. ζ-Potential titration offered a useful tool to differentiate the binding affinities of inorganic ligands toward different NCs. Direct comparison of the binding affinity of oxo and chalcogenidometallate ligands revealed that the former ligands form a stronger bond with oxide NCs (e.g., Fe2O3, ZnO, and TiO2), while the latter prefer binding to metal chalcogenide NCs (e.g., CdSe). The binding between NCs and oxo ligands strengthens when moving from small oxoanions to polyoxometallates (POMs). We also show that small oxo ligands and POMs make it possible to tailor NC properties. For example, we observed improved stability upon Li(+)-ion intercalation into the films of Fe2O3 hollow NCs when capped with MoO4(2-) ligands. We also observed lower overpotential and enhanced exchange current density for water oxidation using Fe2O3 NCs capped with [P2Mo18O62](6-) ligands and even more so for [{Ru4O4(OH)2(H2O)4}(γ-SiW10O36)2] with POM as the capping ligand.

Toward lithium ion batteries with enhanced thermal conductivity.

As batteries become more powerful and utilized in diverse applications, thermal management becomes one of the central problems in their application. We report the results on thermal properties of a set of different Li-ion battery electrodes enhanced with multiwalled carbon nanotubes. Our measurements reveal that the highest in-plane and cross-plane thermal conductivities achieved in the carbon-nanotube-enhanced electrodes reached up to 141 and 3.6 W/mK, respectively. The values for in-plane thermal conductivity are up to 2 orders of magnitude higher than those for conventional electrodes based on carbon black. The electrodes were synthesized via an inexpensive scalable filtration method, and we demonstrate that our approach can be extended to commercial electrode-active materials. The best performing electrodes contained a layer of γ-Fe2O3 nanoparticles on carbon nanotubes sandwiched between two layers of carbon nanotubes and had in-plane and cross-plane thermal conductivities of ∼50 and 3 W/mK, respectively, at room temperature. The obtained results are important for thermal management in Li-ion and other high-power-density batteries.

Coalescence and interface diffusion in linear CdTe/CdSe/CdTe heterojunction nanorods.

Colloidal nanorods with linear CdTe/CdSe/CdTe heterojunctions were synthesized by sequential reactant injection. After CdTe deposition at the ends of initially formed CdSe nanorods, continued heating in solution leads to Se-Te interdiffusion across the heterojunctions and coalescence to decreased aspect ratio. The Se-Te interdiffusion rates were measured by mapping the composition profile using nanobeam energy dispersive X-ray spectroscopy (EDS) along the lengths of individual nanorods aged in hot solvent for different amounts of time. The rate of nanorod coalescence was also measured and compared to model predictions using a continuum viscous flow model, which appears to provide a reasonable estimate of the coalescence rate.