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The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further microbiome characterization. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications. Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by Ruminococcus bromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches.

Breast cancer largely dominates the global cancer burden statistics; however, there are striking disparities in mortality rates across countries. While socioeconomic factors contribute to population-based differences in mortality, they do not fully explain disparity among women of African ancestry (AA) and Arab ancestry (ArA) compared to women of European ancestry (EA). In this study, we sought to identify molecular differences that could provide insight into the biology of ancestry-associated disparities in clinical outcomes. We applied a unique approach that combines the use of curated survival data from The Cancer Genome Atlas (TCGA) Pan-Cancer clinical data resource, improved single-nucleotide polymorphism-based inferred ancestry assignment, and a novel breast cancer subtype classification to interrogate the TCGA and a local Arab breast cancer dataset. We observed an enrichment of BasalMyo tumors in AA patients (38 vs 16.5% in EA, p = 1.30E - 10), associated with a significant worse overall (hazard ratio (HR) = 2.39, p = 0.02) and disease-specific survival (HR = 2.57, p = 0.03). Gene set enrichment analysis of BasalMyo AA and EA samples revealed differences in the abundance of T-regulatory and T-helper type 2 cells, and enrichment of cancer-related pathways with prognostic implications (AA: PI3K-Akt-mTOR and ErbB signaling; EA: EGF, estrogen-dependent and DNA repair signaling). Strikingly, AMPK signaling was associated with opposing prognostic connotation (AA: 10-year HR = 2.79, EA: 10-year HR = 0.34). Analysis of ArA patients suggests enrichment of BasalMyo tumors with a trend for differential enrichment of T-regulatory cells and AMPK signaling. Together, our findings suggest that the disparity in the clinical outcome of AA breast cancer patients is likely related to differences in cancer-related and microenvironmental features.

Peptide- and protein-based therapeutics are becoming a promising treatment regimen for myriad diseases. Toxicity of proteins is the primary hurdle for protein-based therapies. Thus, there is an urgent need for accurate in silico methods for determining toxic proteins to filter the pool of potential candidates. At the same time, it is imperative to precisely identify non-toxic proteins to expand the possibilities for protein-based biologics. To address this challenge, we proposed an ensemble framework, called VISH-Pred, comprising models built by fine-tuning ESM2 transformer models on a large, experimentally validated, curated dataset of protein and peptide toxicities. The primary steps in the VISH-Pred framework are to efficiently estimate protein toxicities taking just the protein sequence as input, employing an under sampling technique to handle the humongous class-imbalance in the data and learning representations from fine-tuned ESM2 protein language models which are then fed to machine learning techniques such as Lightgbm and XGBoost. The VISH-Pred framework is able to correctly identify both peptides/proteins with potential toxicity and non-toxic proteins, achieving a Matthews correlation coefficient of 0.737, 0.716 and 0.322 and F1-score of 0.759, 0.696 and 0.713 on three non-redundant blind tests, respectively, outperforming other methods by over |$10\%$| on these quality metrics. Moreover, VISH-Pred achieved the best accuracy and area under receiver operating curve scores on these independent test sets, highlighting the robustness and generalization capability of the framework. By making VISH-Pred available as an easy-to-use web server, we expect it to serve as a valuable asset for future endeavors aimed at discerning the toxicity of peptides and enabling efficient protein-based therapeutics. [ABSTRACT FROM AUTHOR]

Technology-mediated group toxicity polarization is a major socio-technological issue of our time. For better large-scale monitoring of polarization among social media news content, we quantify the toxicity of news video comments using a Toxicity Polarization Score. For polarizing news videos, our premise is that the comments' toxicity approximates either an "M" or "U" shaped distribution--that is, there is unevenly balanced toxicity among the comments. We evaluate our premises through a case study using a dataset of ~180,000 YouTube comments on ~3,700 real news videos from an international online news organization. Toward polarization-mitigating information systems, we build a predictive machine learning model to score the toxicity polarization of news content even when its comments are disabled or not available, as it is a current trend among news publishers to disable comments. Findings imply that the most engaging news content is also often the most polarizing, which we associate with increasing research on clickbait content and the detrimental effect of attention-based metrics on the health of online social media communities, especially news communities. [ABSTRACT FROM AUTHOR]

Author(s): Vronique Frattini [1]; Stefano M. Pagnotta [1, 2]; Tala [1]; Jerry J. Fan [3, 4]; Marco V. Russo [1]; Sang Bae Lee [1]; Luciano Garofano [1, 2, 5]; Jing [...]

Non-alcoholic fatty liver disease (NAFLD) is a common liver lesion that is untreatable with medications. Glucagon-like peptide-1 receptor (GLP-1R) agonists have recently emerged as a potential NAFLD pharmacotherapy. However, the molecular mechanisms underlying these drugs' beneficial effects are not fully understood. Using Fourier transform infrared (FTIR) spectroscopy, we sought to investigate the biochemical changes in a steatosis cell model treated or not with the GLP-1R agonist Exendin-4 (Ex-4). HepG2 cells were made steatotic with 400 µM of oleic acid and then treated with 200 nM Ex-4 in order to reduce lipid accumulation. We quantified steatosis using the Oil Red O staining method. We investigated the biochemical alterations induced by steatosis and Ex-4 treatment using Fourier transform infrared (FTIR) spectroscopy and chemometric analyses. Analysis of the Oil Red O staining showed that Ex-4 significantly reduces steatosis. This reduction was confirmed by FTIR analysis, as the phospholipid band (C=O) at 1740 cm−1 in Ex-4 treated cells is significantly decreased compared to steatotic cells. The principal component analysis score plots for both the lipid and protein regions showed that the untreated and Ex-4-treated samples, while still separated, are clustered close to each other, far from the steatotic cells. The biochemical and structural changes induced by OA-induced lipotoxicity are at least partially reversed upon Ex-4 treatment. FTIR and chemometric analyses revealed that Ex-4 significantly reduces OA-induced lipid accumulation, and Ex-4 also restored the lipid and protein biochemical alterations caused by lipotoxicity-induced oxidative stress. In combination with chemometric analyses, FTIR spectroscopy may offer new approaches for investigating the mechanisms underpinning NAFLD. [ABSTRACT FROM AUTHOR]

Background: Obesity coexists with variable features of metabolic syndrome, which is associated with dysregulated metabolic pathways. We assessed potential associations between serum metabolites and features of metabolic syndrome in Arabic subjects with obesity. Methods: We analyzed a dataset of 39 subjects with obesity only (OBO, n = 18) age-matched to subjects with obesity and metabolic syndrome (OBM, n = 21). We measured 1069 serum metabolites and correlated them to clinical features. Results: A total of 83 metabolites, mostly lipids, were significantly different (p < 0.05) between the two groups. Among lipids, 22 sphingomyelins were decreased in OBM compared to OBO. Among non-lipids, quinolinate, kynurenine, and tryptophan were also decreased in OBM compared to OBO. Sphingomyelin is negatively correlated with glucose, HbA1C, insulin, and triglycerides but positively correlated with HDL, LDL, and cholesterol. Differentially enriched pathways include lysine degradation, amino sugar and nucleotide sugar metabolism, arginine and proline metabolism, fructose and mannose metabolism, and galactose metabolism. Conclusions: Metabolites and pathways associated with chronic inflammation are differentially expressed in subjects with obesity and metabolic syndrome compared to subjects with obesity but without the clinical features of metabolic syndrome. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), continues to cause substantial morbidity and mortality in the ongoing global pandemic. Understanding the fundamental mechanisms that govern innate immune and inflammatory responses during SARS-CoV-2 infection is critical for developing effective therapeutic strategies. Whereas interferon (IFN)–based therapies are generally expected to be beneficial during viral infection, clinical trials in COVID-19 have shown limited efficacy and potential detrimental effects of IFN treatment during SARS-CoV-2 infection. However, the underlying mechanisms responsible for this failure remain unknown. In this study, we found that IFN induced Z-DNA-binding protein 1 (ZBP1)–mediated inflammatory cell death, PANoptosis, in human and murine macrophages and in the lungs of mice infected with β-coronaviruses, including SARS-CoV-2 and mouse hepatitis virus (MHV). In patients with COVID-19, expression of the innate immune sensor ZBP1 was increased in immune cells from those who succumbed to the disease compared with those who recovered, further suggesting a link between ZBP1 and pathology. In mice, IFN-β treatment after β-coronavirus infection increased lethality, and genetic deletion of Zbp1 or its Zα domain suppressed cell death and protected the mice from IFN-mediated lethality during β-coronavirus infection. Overall, our results identify that ZBP1 induced during coronavirus infection limits the efficacy of IFN therapy by driving inflammatory cell death and lethality. Therefore, inhibiting ZBP1 activity may improve the efficacy of IFN therapy, paving the way for the development of new and critically needed therapeutics for COVID-19 as well as other infections and inflammatory conditions where IFN-mediated cell death and pathology occur. ZBP1 fans the flames of cytokine storm: Type I interferons (IFNs) provide robust innate immune protection against β-coronaviruses including SARS-CoV-2 when available soon after infection. Paradoxically, therapeutic use of type I IFNs in patients with severe COVID-19 may promote increased pathology and clinical deterioration. Using a mouse model of respiratory β-coronavirus infection with mouse hepatitis virus (MHV), Karki et al. found that the detrimental effects of type I IFNs in later stages of infection were related to IFN-induced expression of ZBP1, an innate immune sensor also capable of inducing PANoptosis, an inflammatory form of cell death. Mice lacking ZBP1 were protected from IFN-induced lethality in the MHV infection model. These findings suggest that ZBP1 could be targeted therapeutically in severe COVID-19 to ward off deleterious effects of type I IFNs including cytokine storm. [ABSTRACT FROM AUTHOR]

Background: Obesity-associated dysglycemia is associated with metabolic disorders. MicroRNAs (miRNAs) are known regulators of metabolic homeostasis. We aimed to assess the relationship of circulating miRNAs with clinical features in obese Qatari individuals. Methods: We analyzed a dataset of 39 age-matched patients that includes 18 subjects with obesity only (OBO) and 21 subjects with obesity and metabolic syndrome (OBM). We measured 754 well-characterized human microRNAs (miRNAs) and identified differentially expressed miRNAs along with their significant associations with clinical markers in these patients. Results: A total of 64 miRNAs were differentially expressed between metabolically healthy obese (OBO) versus metabolically unhealthy obese (OBM) patients. Thirteen out of 64 miRNAs significantly correlated with at least one clinical trait of the metabolic syndrome. Six out of the thirteen demonstrated significant association with HbA1c levels; miR-331-3p, miR-452-3p, and miR-485-5p were over-expressed, whereas miR-153-3p, miR-182-5p, and miR-433-3p were under-expressed in the OBM patients with elevated HbA1c levels. We also identified, miR-106b-3p, miR-652-3p, and miR-93-5p that showed a significant association with creatinine; miR-130b-5p, miR-363-3p, and miR-636 were significantly associated with cholesterol, whereas miR-130a-3p was significantly associated with LDL. Additionally, miR-652-3p's differential expression correlated significantly with HDL and creatinine. Conclusions: MicroRNAs associated with metabolic syndrome in obese subjects may have a pathophysiologic role and can serve as markers for obese individuals predisposed to various metabolic diseases like diabetes. [ABSTRACT FROM AUTHOR]

A cancer immune phenotype characterized by an active T-helper 1 (Th1)/cytotoxic response is associated with responsiveness to immunotherapy and favorable prognosis across different tumors. However, in some cancers, such an intratumoral immune activation does not confer protection from progression or relapse. Defining mechanisms associated with immune evasion is imperative to refine stratification algorithms, to guide treatment decisions and to identify candidates for immune-targeted therapy. Molecular alterations governing mechanisms for immune exclusion are still largely unknown. The availability of large genomic datasets offers an opportunity to ascertain key determinants of differential intratumoral immune response. We follow a network-based protocol to identify transcription regulators (TRs) associated with poor immunologic antitumor activity. We use a consensus of four different pipelines consisting of two state-of-the-art gene regulatory network inference techniques, regularized gradient boosting machines and ARACNE to determine TR regulons, and three separate enrichment techniques, including fast gene set enrichment analysis, gene set variation analysis and virtual inference of protein activity by enriched regulon analysis to identify the most important TRs affecting immunologic antitumor activity. These TRs, referred to as master regulators (MRs), are unique to immune-silent and immune-active tumors, respectively. We validated the MRs coherently associated with the immune-silent phenotype across cancers in The Cancer Genome Atlas and a series of additional datasets in the Prediction of Clinical Outcomes from Genomic Profiles repository. A downstream analysis of MRs specific to the immune-silent phenotype resulted in the identification of several enriched candidate pathways, including NOTCH1, TGF- |$\beta $|⁠ , Interleukin-1 and TNF- |$\alpha $| signaling pathways. TGFB1I1 emerged as one of the main negative immune modulators preventing the favorable effects of a Th1/cytotoxic response. [ABSTRACT FROM AUTHOR]

Motivation A global effort is underway to identify compounds for the treatment of COVID-19. Since de novo compound design is an extremely long, time-consuming and expensive process, efforts are underway to discover existing compounds that can be repurposed for COVID-19 and new viral diseases. We propose a machine learning representation framework that uses deep learning induced vector embeddings of compounds and viral proteins as features to predict compound-viral protein activity. The prediction model in-turn uses a consensus framework to rank approved compounds against viral proteins of interest. Results Our consensus framework achieves a high mean Pearson correlation of 0.916, mean R2 of 0.840 and a low mean squared error of 0.313 for the task of compound-viral protein activity prediction on an independent test set. As a use case, we identify a ranked list of 47 compounds common to three main proteins of SARS-COV-2 virus (PL-PRO, 3CL-PRO and Spike protein) as potential targets including 21 antivirals, 15 anticancer, 5 antibiotics and 6 other investigational human compounds. We perform additional molecular docking simulations to demonstrate that majority of these compounds have low binding energies and thus high binding affinity with the potential to be effective against the SARS-COV-2 virus. Availability and implementation All the source code and data is available at: https://github.com/raghvendra5688/Drug-Repurposing and https://dx.doi.org/10.17632/8rrwnbcgmx.3. We also implemented a web-server at: https://machinelearning-protein.qcri.org/index.html. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Aims/Introduction: The progression from prediabetes to type 2 diabetes is preventable by lifestyle intervention and/or pharmacotherapy in a large fraction of individuals with prediabetes. Our objective was to develop a risk score to screen for prediabetes in the Middle East, where diabetes prevalence is one of the highest in the world. Materials and Methods: In this cross‐sectional, case–control study, we used data of 4,895 controls and 2,373 prediabetic adults obtained from the Qatar Biobank cohort. Significant risk factors were identified by logistic regression and other machine learning methods. The receiver operating characteristic was used to calculate the area under curve, cut‐off point, sensitivity, specificity, positive and negative predictive values. The prediabetes risk score was developed from data of Qatari citizens, as well as long‐term (≥15 years) residents. Results: The significant risk factors for the Prediabetes Risk Score in Qatar were age, sex, body mass index, waist circumference and blood pressure. The risk score ranges from 0 to 45. The area under the curve of the score was 80% (95% confidence interval 78–83%), and the cut‐off point of 16 yielded sensitivity and specificity of 86.2% (95% confidence interval 82.7–89.2%) and 57.9% (95% confidence interval 65.5–71.4%), respectively. Prediabetes Risk Score in Qatar performed equally in Qatari nationals and long‐term residents. Conclusions: Prediabetes Risk Score in Qatar is the first prediabetes screening score developed in a Middle Eastern population. It only uses risk factors measured non‐invasively, is simple, cost‐effective, and can be easily understood by the general public and health providers. Prediabetes Risk Score in Qatar is an important tool for early detection of prediabetes, and can help tremendously in curbing the diabetes epidemic in the region. [ABSTRACT FROM AUTHOR]

Motivation X-ray crystallography has facilitated the majority of protein structures determined to date. Sequence-based predictors that can accurately estimate protein crystallization propensities would be highly beneficial to overcome the high expenditure, large attrition rate, and to reduce the trial-and-error settings required for crystallization. Results In this study, we present a novel model, BCrystal, which uses an optimized gradient boosting machine (XGBoost) on sequence, structural and physio-chemical features extracted from the proteins of interest. BCrystal also provides explanations, highlighting the most important features for the predicted crystallization propensity of an individual protein using the SHAP algorithm. On three independent test sets, BCrystal outperforms state-of-the-art sequence-based methods by more than 12.5% in accuracy, 18% in recall and 0.253 in Matthew's correlation coefficient, with an average accuracy of 93.7%, recall of 96.63% and Matthew's correlation coefficient of 0.868. For relative solvent accessibility of exposed residues, we observed higher values to associate positively with protein crystallizability and the number of disordered regions, fraction of coils and tripeptide stretches that contain multiple histidines associate negatively with crystallizability. The higher accuracy of BCrystal enables it to accurately screen for sequence variants with enhanced crystallizability. Availability and implementation Our BCrystal webserver is at https://machinelearning-protein.qcri.org/ and source code is available at https://github.com/raghvendra5688/BCrystal. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Protein structure determination has primarily been performed using X-ray crystallography. To overcome the expensive cost, high attrition rate and series of trial-and-error settings, many in-silico methods have been developed to predict crystallization propensities of proteins based on their sequences. However, the majority of these methods build their predictors by extracting features from protein sequences, which is computationally expensive and can explode the feature space. We propose DeepCrystal, a deep learning framework for sequence-based protein crystallization prediction. It uses deep learning to identify proteins which can produce diffraction-quality crystals without the need to manually engineer additional biochemical and structural features from sequence. Our model is based on convolutional neural networks, which can exploit frequently occurring k -mers and sets of k -mers from the protein sequences to distinguish proteins that will result in diffraction-quality crystals from those that will not. Results Our model surpasses previous sequence-based protein crystallization predictors in terms of recall, F -score, accuracy and Matthew's correlation coefficient (MCC) on three independent test sets. DeepCrystal achieves an average improvement of 1.4, 12.1% in recall, when compared to its closest competitors, Crysalis II and Crysf, respectively. In addition, DeepCrystal attains an average improvement of 2.1, 6.0% for F-score, 1.9, 3.9% for accuracy and 3.8, 7.0% for MCC w.r.t. Crysalis II and Crysf on independent test sets. Availability and implementation The standalone source code and models are available at https://github.com/elbasir/DeepCrystal and a web-server is also available at https://deeplearning-protein.qcri.org. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Recent years have witnessed a growing effort in engineering and tuning the properties of hybrid halide perovskites as light absorbers. These have led to the successful enhancement of their stability, a feature that is often counterbalanced by a reduction of their power-conversion efficiency. In order to provide a systematic analysis of the structure–property relationships of this class of compounds we have performed density functional theory calculations exploring fully inorganic ABC3 chalcogenide (I–V–VI3), halide (I–II–VII3) and hybrid perovskites. Special attention has been given to structures featuring three-dimensional BC6 octahedral networks because of their efficient carrier transport properties. In particular we have carefully analyzed the role of BC6 octahedral deformations, rotations and tilts in the thermodynamic stability and optical properties of the compounds. By using machine learning algorithms we have estimated the relations between the octahedral deformation and the bandgap, and established a similarity map among all the calculated compounds. [ABSTRACT FROM AUTHOR]

Fourier Transform Infrared Spectroscopy (FTIR) is a non-destructive analytical technique that has been employed in this research to characterize the biochemical make-up of various rat brain regions. The sensorimotor cortex, caudate putamen, thalamus, and the hippocampus were found to have higher olefinic content—an indicator of a higher degree of unsaturated fatty acids—rich in short-chain fatty acids, and low in ester and lipid contents. While the regions of the corpus callosum, internal, and external capsule were found to contain long-chained and higher-esterified saturated fatty acids. These molecular differences may reflect the roles of the specific regions in information processing and can provide a unique biochemical platform for future studies on the earlier detection of pathology development in the brain, as a consequence of disease or injury. Laser Ablation Inductively Coupled Plasma Mass Spectroscopy (LA-ICP-MS) is another vital analytical technique that was used in this work to analyze the elements' distribution patterns in various regions of the brain. The complementary data sets allowed the characterization of the brain regions, the chemical dominating groups, and the elemental composition. This set-up may be used for the investigation of changes in the brain caused by diseases and help create a deeper understanding of the interactions between the organic and elemental composition. [ABSTRACT FROM AUTHOR]

Objective: Stroke is the main cause of adult disability in the world, leaving more than half of the patients dependent on daily assistance. Understanding the post-stroke biochemical and molecular changes are critical for patient survival and stroke management. The aim of this work was to investigate the photo-thrombotic ischemic stroke in male rats with particular focus on biochemical and elemental changes in the primary stroke lesion in the somatosensory cortex and surrounding areas, including the corpus callosum. Materials and Methods: FT-IR imaging spectroscopy and LA-ICPMS techniques examined stroke brain samples, which were compared with standard immunohistochemistry studies. Results: The FTIR results revealed that in the lesioned gray matter the relative distribution of lipid, lipid acyl and protein contents decreased significantly. Also at this locus, there was a significant increase in aggregated protein as detected by high-levels Aβ1-42. Areas close to the stroke focus experienced decrease in the lipid and lipid acyl contents associated with an increase in lipid ester, olefin, and methyl bio-contents with a novel finding of Aβ1-42 in the PL-GM and L-WM. Elemental analyses realized major changes in the different brain structures that may underscore functionality. Conclusion: In conclusion, FTIR bio-spectroscopy is a non-destructive, rapid, and a refined technique to characterize oxidative stress markers associated with lipid degradation and protein denaturation not characterized by routine approaches. This technique may expedite research into stroke and offer new approaches for neurodegenerative disorders. The results suggest that a good therapeutic strategy should include a mechanism that provides protective effect from brain swelling (edema) and neurotoxicity by scavenging the lipid peroxidation end products. [ABSTRACT FROM AUTHOR]

Motivation Protein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imperative to develop novel, highly accurate in silico sequence-based protein solubility predictors. In this work we propose, DeepSol, a novel Deep Learning-based protein solubility predictor. The backbone of our framework is a convolutional neural network that exploits k-mer structure and additional sequence and structural features extracted from the protein sequence. Results DeepSol outperformed all known sequence-based state-of-the-art solubility prediction methods and attained an accuracy of 0.77 and Matthew’s correlation coefficient of 0.55. The superior prediction accuracy of DeepSol allows to screen for sequences with enhanced production capacity and can more reliably predict solubility of novel proteins. Availability and implementation DeepSol’s best performing models and results are publicly deposited at https://doi.org/10.5281/zenodo.1162886 (Khurana and Mall, 2018). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Protein solubility can be a decisive factor in both research and production efficiency and in silico sequence-based predictors that can accurately estimate solubility outcomes are highly sought. Results: In this study, we present a novel approach termed PRotein SolubIlity Predictor (PaRSnIP), which uses a gradient boosting machine algorithm as well as an approximation of sequence and structural features of the protein of interest. Based on an independent test set, PaRSnIP outperformed other state-of-the-art sequence-based methods by more than 9% in accuracy and 0.17 in Matthew's correlation coefficient, with an overall accuracy of 74% and Matthew's correlation coefficient of 0.48. Additionally, PaRSnIP provides importance scores for all features used in training. We observed higher fractions of exposed residues to associate positively with protein solubility and tripeptide stretches with multiple histidines to associate negatively with solubility. The improved prediction accuracy of PaRSnIP should enable it to predict protein solubility with greater reliability and to screen for sequence variants with enhanced manufacturability. [ABSTRACT FROM AUTHOR]

Background: Biological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses, the gene expression network undergoes some amount of localized re-wiring. The ability to detect statistical relevant changes in the interaction patterns induced by the progression of the disease can lead to the discovery of novel relevant signatures. Several procedures have been recently proposed to detect sub-network differences in pairwise labeled weighted networks. Methods: In this paper, we propose an improvement over the state-of-the-art based on the Generalized Hamming Distance adopted for evaluating the topological difference between two networks and estimating its statistical significance. The proposed procedure exploits a more effective model selection criteria to generate p-values for statistical significance and is more efficient in terms of computational time and prediction accuracy than literature methods. Moreover, the structure of the proposed algorithm allows for a faster parallelized implementation. Results: In the case of dense random geometric networks the proposed approach is 10-15x faster and achieves 5-10% higher AUC, Precision/Recall, and Kappa value than the state-of-the-art. We also report the application of the method to dissect the difference between the regulatory networks of IDH-mutant versus IDH-wild-type glioma cancer. In such a case our method is able to identify some recently reported master regulators as well as novel important candidates. Conclusions: We show that our network differencing procedure can effectively and efficiently detect statistical significant network re-wirings in different conditions. When applied to detect the main differences between the networks of IDH-mutant and IDH-wild-type glioma tumors, it correctly selects sub-networks centered on important key regulators of these two different subtypes. In addition, its application highlights several novel candidates that cannot be detected by standard single network-based approaches. [ABSTRACT FROM AUTHOR]