- by Moradi, O., Maghsoudi, A., Masoudi, A. A., Torshizi, R. V.Background: Antibiotic resistance among pathogens common to human beings and animals, which include Brucella melitensis, has end up a significant worldwide health task. Traditional antibiotic treatments for brucellosis, along with lengthy-time period regimens of doxycycline and rifampicin, are going through increasing boundaries because of rising resistance, affected person adherence issues, and considerable side results. Methods: This observe investigates the capacity of targeting the periplasmic D-ribose-binding protein (DBP), a key component of the bacterial ATP-binding cassette (ABC) delivery system, as a […]
- by Atarsaikhan, G., Mogollon, I., Valimaki, K., iCAN,, Mirtti, T., Pellinen, T., Paavolainen, L.Multiplexed immunofluorescence microscopy provides detailed insights into the spatial architecture of cancer tissue samples by using multiple fluorescent markers. Classical analysis approaches focus on single-cell data but can be limited by segmentation accuracy and the representative power of extracted features, potentially overlooking crucial spatial interrelationships among proteins or cells. We developed a hierarchical self-supervised deep learning approach to learn feature representations from multiplexed microscopy images without expert annotations. The method encodes tissue samples at both the local (cellular) level and […]
- by Zhang, W., Huang, H., Wang, L., Brian, L. D., Chen, S. X.High-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic measurements. Integrating these diverse data types can yield deeper insights into the biological mechanisms driving complex traits and diseases. Yet, extracting key shared biomarkers from multiple data layers remains a major challenge. We present a multivariate random forest (MRF)-based framework enhanced by a novel inverse minimal depth (IMD) metric for integrative variable selection. By assigning response variables to tree nodes and […]
- by Mbebi, A. J., Mercado, F., Hobby, D., Tong, H., Nikoloski, Z.Traits in any organism are not independent, but show considerable integration, observed in a form of couplings and trade-offs. Therefore, improvement in one trait may affect other traits, often in undesired direction. To account for this problem, crop breeding increasingly relies on multi-trait genomic prediction (MT-GP) approaches that leverage the availability of genetic markers from different populations along with advances in high-throughput precision phenotyping. While significant progress has been made to jointly model multiple traits using a variety of statistical […]
- by Ayala-Ruano, S., Webel, H., Santos, A.Motivation The analysis of omics data typically involves multiple bioinformatics tools and methods, each producing distinct output files. However, compiling these results into comprehensive reports often requires additional effort and technical skills. This creates a barrier for non-bioinformaticians, limiting their ability to produce reports from their findings. Moreover, the lack of streamlined reporting workflows impacts reproducibility and transparency, making it difficult to communicate results and track analytical processes. Results We present VueGen, a tool that automates the creation of reports […]
- by Zhu, H., Wang, K., Li, K., Fang, Z., Zhou, J., Ye, M.Knowledge of protein-ligand interactions is essential for comprehending various aspects of life science, including drug mechanisms of action, regulatory processes in cellular metabolism and signaling, etc. Recently, a robust ligand modification-free method, termed as peptide-centric local stability assay (PELSA), is developed to identify the protein targets and binding regions of diverse ligands at proteomics scale. This method has unprecedented sensitivity and can be broadly applied to ligands including drugs, metabolites, metal ions, antibodies tec. However, the extraction of key information […]
- by Shternshis, A., Tong, B., Wahlby, C., Zachariah, D., Hugerth, L. W., Singh, P.Time-series of compositional data are a common format for many high-throughput studies of biological molecules, analyzing e.g. response to a treatment or with the aim to predict an outcome. However, data from some time points may be missing, which reduces the size of the complete dataset. We propose a method for binary classification that includes imputation for missing values and logarithmic transformation of compositional data. Imputation approaches entail models that incorporate artificial data alongside true measurements, thereby supplementing the dataset. […]
- by Cheng, L., Wei, T., Cui, X., Chen, H., Yu, Z.Proteins typically interact with multiple partners to regulate biological processes, and peptide drugs targeting multiple receptors have shown strong therapeutic potential, emphasizing the need for multi-target strategies in protein design. However, most current protein sequence design methods focus on interactions with a single receptor, often neglecting the complexity of designing proteins that can bind to two distinct receptors. We introduced ProDualNet, a novel approach for designing dual-target protein sequences by integrating sequence-structure information from two distinct receptors. ProDualNet used a […]
- by Jimenez-Sanchez, A., Persad, S., Hayashi, A., Umeda, S., Sharma, R., Xie, Y., Mehta, A., Park, W., Masilionis, I., Chu, T., Zhu, F., Hong, J., Chaligne, R., O'Reilly, E. M., Mazutis, L., Nawy, T., Pe'er, I., Iacobuzio-Donahue, C. A., Pe'er, D.Metastasis is the leading cause of cancer deaths; nonetheless, how tumor cells adapt to vastly different organ contexts is largely unknown. To investigate this question, we generated a transcriptomic atlas of primary tumor and diverse metastatic samples from a patient with pancreatic ductal adenocarcinoma who underwent rapid autopsy. Unsupervised archetype analysis identified both shared and site-specific gene programs, including lipid metabolism and gastrointestinal programs prevalent in peritoneum and stomach wall lesions, respectively. We developed a probabilistic approach for inferring clonal […]
- by Zhang, J.Glioblastoma multiforme (GBM), a highly aggressive brain tumor characterized by molecular heterogeneity, necessitates integrative approaches to decipher driver pathways and therapeutic vulnerabilities. Here, we present gtePIDP, a computational framework that systematically integrates multi-omics data (genomic, transcriptomic, epigenetic) to identify Phenotypically Impactful Driver Pathways through dual evaluation of mutation patterns (coverage/exclusivity) and downstream regulatory cascades. Leveraging mutation profiles and expression data from The Cancer Genome Atlas (TCGA), combined with transcription factor (TF) and miRNA regulatory networks, we constructed a systems biology […]
- by Hard, J., Seidel, S., Ferreira, P., Berkes, P., Jahn, K., Eriksson, C.-J., Mold, J. E., Michaelsson, J., Kuipers, J., Beerenwinkel, N.Tissues and organs develop from single founder cells, which give rise to distinct cell lineages that contribute to regeneration and maintenance of homeostasis in the adult. Tracing the genealogical relationships between individual cells and their gene expression signatures is an important step towards understanding how these processes are regulated in human health and disease. Here, we present mt-SCITE, a computational method for inferring the evolutionary history of dividing cells based on mitochondrial mutations detected in single cells. We show that […]
- by Luo, Y., Parmeggiani, F.Carbohydrate-protein interactions are essential for biological processes such as cellular signaling and metabolism. However, current prediction methods cannot accurately evaluate the affinity and specificity of proteins for carbohydrates such as glucose and galactose. Here, we develop a machine learning classifier, named CLIMBS, and train it on crystal structures and synthetic data from unsuccessfully designed binders, to effectively assess if carbohydrate-protein complexes represent realistic, native like structures. Compared to other methods, CLIMBS has outstanding accuracy, excellent carbohydrate specificity, sub-second runtime per […]
- by Acencio, M., Lopata, O., Hemedan, A., Dand, N., Weidinger, S., Paternoster, L., Brown, S., Rastrick, J., Smith, C., Hubenthal, M., Ramessur, R., Ndlovu, M., Ghosh, S., Wang, X., Schneider, R., Satagopam, V., Ostaszewski, M.Background: Inflammatory skin diseases (ISD), including atopic dermatitis (AD) and psoriasis (PsO), emerge from a complex network of inter- and intracellular biochemical interactions under the influence of genetic and environmental factors. The complexity of ISD mechanisms hinders translation of research findings into effective treatments and may explain the low remission rates despite the availability of modern targeted therapies. Objective: To model AD- and PsO-associated mechanisms as networks of context-specific molecular interactions, the so-called ISD map, and to check how feasible […]
- by Malik, S., Tomer, R., Arora, A., Raghava, G. P. S.Most of the existing studies have identified a single profile of prognostic biomarkers for predicting high-risk cancer patients using transcriptomics data. In this study, we propose multiple distinct sets of prognostic biomarkers for predicting high-risk Skin Cutaneous Melanoma (SKCM) patients. Our primary analysis reveals that the expression of certain genes, such as CREG1, PCGF5, and VPS13C, strongly correlates with overall survival (OS) in SKCM patients. We developed machine learning-based prognostic models to predict 1-, 3-, and 5-year overall survival using […]
- by Behn, J., Deepak, R. N. V. K., Hu, J., Fan, H.Aberrant signaling due to mutations in the Epidermal Growth Factor Receptor (EGFR) kinase domain is implicated in various diseases, including cancer. However, the structural mechanisms underlying overactivation in many rare EGFR mutations remain poorly understood. Here, we benchmarked the CHARMM and AMBER force fields and compared simulations of asymmetric and symmetric dimers to establish a Molecular Dynamics (MD) protocol capable of revealing EGFR mutant actional modes using relatively short simulations. This protocol successfully reproduced the known mechanistic behavior of wild-type […]
- by Turkolmez, E., Khan, Y. A., Bund, C., Namer, I. J., Cicek, A. E., Aksoy, S.Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) are crucial in diagnosing various medical conditions related to brain. FDG-PET provides essential functional information by capturing synaptic transmission activity, while MRI offers anatomical details. The integration of these modalities has become increasingly valuable, especially with the advent of hybrid PET/MRI systems that enable acquisition of both images in a single session. However, a notable challenge arises due to the inherent differences in spatial resolution between PET and MRI. PET images […]
- by Ahmadi, A., Rafiei, F., Alizadeh, M., Chitsaz, A., Ghasemi, S., Bagheri, H., Ghazvini, A., Arabfard, M., Azimzadeh, S., Ghanei, M.The significance of sulfur mustard as a chemical hazard cannot be overstated. Its potential for causing severe morbidity and long-term health issues makes understanding its effects crucial. Mustard-DB is an essential database that consolidates extensive cellular, molecular, and clinical data on sulfur mustard exposure. It stands as a critical tool for the scientific community, shedding light on the pathophysiological mechanisms and potential therapeutic interventions for those affected by this noxious agent. The database provides a detailed account of the acute […]
- by Lazari, L. C., Azemi, G., Russo, C., Fernandes, L. R., Marie, S. K. N., Di Ieva, A. C., Palmisano, G.Data pre-processing is a critical step in the analysis of MALDI-TOF MS spectra for machine learning applications, typically involving steps such as spectra trimming, baseline correction, smoothing, transformation, and peak picking or spectral binning. While traditional approaches focus on protein/peptide peaks as features, this study explores a novel method of feature extraction by treating MALDI-TOF spectra as time-series data. This study investigates the use of computational fractal-based analysis to assess the complexity of MALDI-TOF spectra. Fractal analysis, previously successful in […]
- by Wiedemann, S., Fabian, Z., Soltanolkotabi, M., Heckel, R.Cryogenic electron tomography (cryo-ET) can produce detailed 3D images, called tomograms, of cellular environments. An essential step of cryo-ET data analysis is to find all instances of a particle in a set of tomograms. This particle picking task is a challenging 3D object detection problem due to strong noise and artifacts in the tomograms, as well as the diverse, crowded cellular environment. To enable a fast, flexible, and data-efficient workflow for particle picking, we propose ProPicker, a pretrained, promptable 3D […]
- by Grau, J., Keilwagen, J.Motivation Annotation of genes and transcripts is an important requirement for understanding the information that is encoded in newly sequenced genomes. One source of information suited for this purpose are RNA-seq data mapped to the respective genome sequence. RNA-seq-based approaches for transcript reconstruction generate transcript models from these data by combining regions of contiguous coverage (exons) and split read mappings (introns). Understanding phenotypes as a consequence of proteins encoded in a genome further requires the annotation of coding sequences within […]
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