What is the Machine Learning?

Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables -- aided by physical intuition -- that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks are then trained on this modified data. Observed decreases in sensitivity diagnose the variable's discriminating power. Planing also allows the investigation of the linear versus non-linear nature of the boundaries between signal and background. We demonstrate the efficacy of this approach using a toy example, followed by an application to an idealized heavy resonance scenario at the Large Hadron Collider. By unpacking the information being utilized by these algorithms, this method puts in context what it means for a machine to learn.Comment: 6 pages, 3 figures. Version published in PRD, discussion adde

SMG6 localizes to the chromatoid body and shapes the male germ cell transcriptome to drive spermatogenesis

Nonsense-mediated RNA decay (NMD) is a highly conserved and selective RNA turnover pathway that depends on the endonuclease SMG6. Here, we show that SMG6 is essential for male germ cell differentiation in mice. Germ-cell conditional knockout (cKO) of Smg6 induces extensive transcriptome misregulation, including a failure to eliminate meiotically expressed transcripts in early haploid cells, and accumulation of NMD target mRNAs with long 3 ' untranslated regions (UTRs). Loss of SMG6 in the male germline results in complete arrest of spermatogenesis at the early haploid cell stage. We find that SMG6 is strikingly enriched in the chromatoid body (CB), a specialized cytoplasmic granule in male germ cells also harboring PIWI-interacting RNAs (piRNAs) and the piRNA-binding protein PIWIL1. This raises the possibility that SMG6 and the piRNA pathway function together, which is supported by several findings, including that Piwil1-KO mice phenocopy Smg6-cKO mice and that SMG6 and PIWIL1 co-regulate many genes in round spermatids. Together, our results demonstrate that SMG6 is an essential regulator of the male germline transcriptome, and highlight the CB as a molecular platform coordinating RNA regulatory pathways to control sperm production and fertility.Peer reviewe

Toward understanding the core meiotic transcriptome in mammals and its implications for somatic cancer.

International audienceProgression through meiotic development is in part controlled by an expression program that coordinates the timing of induction and time of function of numerous loci essential for the process. Whole-genome profiling of male germline expression in mouse, rat, and human provides important clues about the transcriptional regulatory machinery that drives the expression of its target genes. Among several thousand genes differentially expressed between testicular Sertoli and germ cells, a subset of conserved loci display highly similar meiotic and postmeiotic profiles across rodents and Homo sapiens. Mouse genes transcribed in the germline, but not in somatic control tissues, are frequently found to be important for sexual reproduction, thus correlating potentially specific expression and essential function in the male germline. In silico promoter analysis yields insight into DNA sequence conservation and the distribution of known regulatory elements within potential promoter regions of meiotic and postmeiotic genes. Some genes strongly expressed in male gonads are implicated in cancer, thus supporting the idea that gametogenesis and tumorigenesis may share molecular functions. Transcriptome, proteome, and protein network data reveal the kinetics of mRNA synthesis and translation in the germline, and help identify novel potentially important genes previously unassociated with the mammalian male germline

Additional file 5: Figure S3. of Glyceollins trigger anti-proliferative effects through estradiol-dependent and independent pathways in breast cancer cells

GO enrichment analysis of different treatment-related expression patterns. Eight expression patterns are matched with a selection of GO terms from the ontology “phenotypes,” “biological process,” “cellular component” and “pathways.” The numbers of genes associated with each GO term are indicated in the first column. Enrichment is indicated by bolded rectangles, where the first number indicates the number of genes found in our analysis and the second the number expected with a random list of genes. Overrepresented genes in a specific GO term are shown in red, and underrepresented genes are shown in blue. (TIFF 2724 kb