DLocalMotif: a discriminative approach for discovering local motifs in protein sequences

Motivation: Local motifs are patterns of DNA or protein sequences that occur within a sequence interval relative to a biologically defined anchor or landmark. Current protein motif discovery methods do not adequately consider such constraints to identify biologically significant motifs that are only weakly over-represented but spatially confined. Using negatives, i.e. sequences known to not contain a local motif, can further increase the specificity of their discovery

Safety and Efficacy of Axicabtagene Ciloleucel versus Standard of Care in Patients 65 Years of Age or Older with Relapsed/Refractory Large B-Cell Lymphoma

Purpose: Older patients with relapsed/refractory (R/R) large B-cell lymphoma (LBCL) may be considered ineligible for curative-intent therapy including high-dose chemotherapy with autologous stem-cell transplantation (HDT-ASCT). Here, we report outcomes of a preplanned subgroup analysis of patients >= 65 years in ZUMA-7. Patients and Methods: Patients with LBCL refractory to or relapsed = 65 years were random-ized to axi-cel and SOC, respectively. Median EFS was greater with axi-cel versus SOC (21.5 vs. 2.5 months; median follow-up: 24.3 months; HR, 0.276; descriptive P = 3 adverse events occurred in 94% of axi-cel and 82% of SOC patients. No grade 5 cytokine release syndrome or neurologic events occurred. In the quality-of-life analysis, the mean change in PRO scores from baseline at days 100 and 150 favored axi-cel for EORTC QLQ-C30 Global Health, Physical Functioning, and EQ-5D-5L visual analog scale (descriptive P = 65 and = 65 years with R/R LBCL

Author Correction: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

Correction to: npj Digital Medicine https://doi.org/10.1038/s41746-021-00431-6, published online 29 March 202

Candidate gene prioritization by network analysis of differential expression using machine learning approaches

<p>Abstract</p> <p>Background</p> <p>Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals.</p> <p>To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network.</p> <p>Results</p> <p>We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (<it>Simple Expression Ranking</it>). Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the <it>Heat Kernel Diffusion Ranking </it>leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%.</p> <p>Conclusion</p> <p>In this study we could identify promising candidate genes using network based machine learning approaches even if no knowledge is available about the disease or phenotype.</p

Genome-Wide Expression Analysis Identifies a Modulator of Ionizing Radiation-Induced p53-Independent Apoptosis in Drosophila melanogaster

Tumor suppressor p53 plays a key role in DNA damage responses in metazoa, yet more than half of human tumors show p53 deficiencies. Therefore, understanding how therapeutic genotoxins such as ionizing radiation (IR) can elicit DNA damage responses in a p53-independent manner is of clinical importance. Drosophila has been a good model to study the effects of IR because DNA damage responses as well as underlying genes are conserved in this model, and because streamlined gene families make loss-of-function analyses feasible. Indeed, Drosophila is the only genetically tractable model for IR-induced, p53-independent apoptosis and for tissue regeneration and homeostasis after radiation damage. While these phenomenon occur only in the larvae, all genome-wide gene expression analyses after irradiation to date have been in embryos. We report here the first analysis of IR-induced, genome-wide gene expression changes in wild type and p53 mutant Drosophila larvae. Key data from microarrays were confirmed by quantitative RT-PCR. The results solidify the central role of p53 in IR-induced transcriptome changes, but also show that nearly all changes are made of both p53-dependent and p53-independent components. p53 is found to be necessary not just for the induction of but also for the repression of transcript levels for many genes in response to IR. Furthermore, Functional analysis of one of the top-changing genes, EF1a-100E, implicates it in repression of IR-induced p53-independent apoptosis. These and other results support the emerging notion that there is not a single dominant mechanism but that both positive and negative inputs collaborate to induce p53-independent apoptosis in response to IR in Drosophila larvae

Meta-analysis of five genome-wide association studies identifies multiple new loci associated with testicular germ cell tumor

The international Testicular Cancer Consortium (TECAC) combined five published genome-wide association studies of testicular germ cell tumor (TGCT; 3,558 cases and 13,970 controls) to identify new susceptibility loci. We conducted a fixed-effects meta-analysis, including, to our knowledge, the first analysis of the X chromosome. Eight new loci mapping to 2q14.2, 3q26.2, 4q35.2, 7q36.3, 10q26.13, 15q21.3, 15q22.31, and Xq28 achieved genome-wide significance (P < 5 × 10−8). Most loci harbor biologically plausible candidate genes. We refined previously reported associations at 9p24.3 and 19p12 by identifying one and three additional independent SNPs, respectively. In aggregate, the 39 independent markers identified to date explain 37% of father-to-son familial risk, 8% of which can be attributed to the 12 new signals reported here. Our findings substantially increase the number of known TGCT susceptibility alleles, move the field closer to a comprehensive understanding of the underlying genetic architecture of TGCT, and provide further clues to the etiology of TGCT