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Identifying cancer risk groups by multi-omics has attracted researchers in their quest to find biomarkers from diverse risk-related omics. Stratifying the patients into cancer risk groups using genomics is essential for clinicians for pre-prevention treatment to improve the survival time for patients and identify the appropriate therapy strategies. This study proposes a multi-omics framework that can extract the features from various omics simultaneously. The framework employs autoencoders to learn the non-linear representation of the data and applies tensor analysis for feature learning. Further, the clustering method is used to stratify the patients into multiple cancer risk groups. Several omics were included in the experiments, namely methylation, somatic copy-number variation (SCNV), micro RNA (miRNA) and RNA sequencing (RNAseq) from two cancer types, including Glioma and Breast Invasive Carcinoma from the TCGA dataset. The results of this study are promising, as evidenced by the survival analysis and classification models, which outperformed the state-of-the-art. The patients can be significantly (p-value<0.05) divided into risk groups using extracted latent variables from the fused multi-omics data. The pipeline is open source to help researchers and clinicians identify the patients' risk groups using genomics.

Influence Maximization (IM) is the task of determining k optimal influential nodes in a social network to maximize the influence spread using a propagation model. IM is a prominent problem for viral marketing and helps significantly in social media advertising. Previous Greedy and Reverse Influence Sampling-based IM approaches are ineffective in real-world social networks due to their significant computational cost and execution time. Further, even heuristic approaches applied to IM generally yield minimal performance gain relative to the decreased time complexity. This presents a challenge in developing cost-effective algorithms with low execution time that can handle diverse social networks. In this paper, we propose the discretization of the nature-inspired Harris’ Hawks Optimization meta-heuristic algorithm using community structures for optimal selection of seed nodes for influence spread. In addition to Harris’ Hawks’ intelligence, we employ a neighbor scout strategy algorithm to avoid blindness and enhance the searching ability of the hawks. Further, we use a candidate nodes-based random population initialization approach, and these candidate nodes aid in accelerating the convergence process for the entire populace, reducing the total computational cost. We evaluate the efficacy of our proposed DHHO approach on eight social networks using the Independent Cascade model for information diffusion. We observe that DHHO is comparable or better than competing meta-heuristic approaches for Influence Maximization across five metrics, and performs noticeably better than competing heuristic approaches.

To mount an adequate immune response against pathogens, stepwise mutation and selection processes are crucial functions of the adaptive immune system. To better characterize a successful vaccination response, we performed longitudinal (days 0, 5, 7, 10, and 14 after Boostrix vaccination) analysis of the single-cell transcriptome as well as the B-cell receptor (BCR) repertoire (scBCR-rep) in plasma cells of an immunized donor and compared it with baseline B-cell characteristics as well as flow cytometry findings. Based on the flow cytometry knowledge and literature findings, we discriminated individual B-cell subsets in the transcriptomics data and traced over-time maturation of plasmablasts/plasma cells (PB/PCs) and identified the pathways associated with the plasma cell maturation. We observed that the repertoire in PB/PCs differed from the baseline B-cell repertoire e.g., regarding expansion of unique clones in post-vaccination visits, high usage of IGHG1 in expanded clones, increased class-switching events post-vaccination represented by clonotypes spanning multiple IGHC classes and positive selection of CDR3 sequences over time. Importantly, the Variable gene family-based clustering of BCRs represented a similar measure as the gene-based clustering, but certainly improved the clustering of BCRs, as BCRs from duplicated Variable gene families could be clustered together. Finally, we developed a query tool to dissect the immune response to the components of the Boostrix vaccine. Using this tool, we could identify the BCRs related to anti-tetanus and anti-pertussis toxoid BCRs. Collectively, we developed a bioinformatic workflow which allows description of the key features of an ongoing (longitudinal) immune response, such as activation of PB/PCs, Ig class switching, somatic hypermutation, and clonal expansion, all of which are hallmarks of antigen exposure, followed by mutation & selection processes., Pattern Recognition and Bioinformatics

Immunoglobulin (IG) loci harbor inter-individual allelic variants in many different germline IG variable, diversity and joining genes of the IG heavy (IGH), kappa (IGK) and lambda (IGL) loci, which together form the genetic basis of the highly diverse antigen-specific B-cell receptors. These allelic variants can be shared between or be specific to human populations. The current immunogenetics resources gather the germline alleles, however, lack the population specificity of the alleles which poses limitations for disease-association studies related to immune responses in different human populations. Therefore, we systematically identified germline alleles from 26 different human populations around the world, profiled by “1000 Genomes” data. We identified 409 IGHV, 179 IGKV, and 199 IGLV germline alleles supported by at least seven haplotypes. The diversity of germline alleles is the highest in Africans. Remarkably, the variants in the identified novel alleles show strikingly conserved patterns, the same as found in other IG databases, suggesting over-time evolutionary selection processes. We could relate the genetic variants to population-specific immune responses, e.g. IGHV1-69 for flu in Africans. The population matched IG (pmIG) resource will enhance our understanding of the SHM-related B-cell receptor selection processes in (infectious) diseases and vaccination within and between different human populations., Pattern Recognition and Bioinformatics

Contains fulltext : 218166.pdf (publisher's version ) (Open Access), The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.