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Register a new userPredicting Gene Expression Between Species with Neural Networks
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We train a neural network to predict human gene expression levels based on experimental data for rat cells. The network is trained with paired human/rat samples from the Open TG-GATES database, where paired samples were treated with the same compound at the same dose. When evaluated on a test set of held out compounds, the network successfully predicts human expression levels. On the majority of the test compounds, the list of differentially expressed genes determined from predicted expression levels agrees well with the list of differentially expressed genes determined from actual human experimental data.
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We train a neural network to predict chemical toxicity based on gene expression data. The input to the network is a full expression profile collected either in vitro from cultured cells or in vivo from live animals. The output is a set of fine grained predictions for the presence of a variety of pathological effects in treated animals. When trained on the Open TG-GATEs database it produces good results, outperforming classical models trained on the same data. This is a promising approach for efficiently screening chemicals for toxic effects, and for more accurately evaluating drug candidates based on preclinical data.
Linear discrimination, from the point of view of numerical linear algebra, can be treated as solving an ill-posed system of linear equations. In order to generate a solution that is robust in the presence of noise, these problems require regularization. Here, we examine the ill-posedness involved in the linear discrimination of cancer gene expression data with respect to outcome and tumor subclasses. We show that a filter factor representation, based upon Singular Value Decomposition, yields insight into the numerical ill-posedness of the hyperplane-based separation when applied to gene expression data. We also show that this representation yields useful diagnostic tools for guiding the selection of classifier parameters, thus leading to improved performance.
The Dissertation is focused on the studies of associations between functional elements in human genome and their nucleotide structure. The asymmetry in nucleotide content (skew, bias) was chosen as the main feature for nucleotide structure. A significant difference in nucleotide content asymmetry was found for human exons vs. introns. Specifically, exon sequences display bias for purines (i.e., excess of A and G over C and T), while introns exhibit keto-amino skew (i.e. excess of G and T over A and C). The extents of these biases depend upon gene expression patterns. The highest intronic keto-amino skew is found in the introns of housekeeping genes. In the case of introns, whose sequences are under weak repair system, the AT->GC and CG->TA substitutions are preferentially accumulated. A comparative analysis of gene sequences encoding cytochrome P450 2E1 of Homo sapiens and representative mammals was done. The cladistic tree on the basis of coding sequences similarity of the gene Cyp2e1 was constructed. A new programming tools of NCBI database sequence mining and analysis was developed, resulting in construction of a own database.
In unicellular organisms such as bacteria the same acquired mutations beneficial in one environment can be restrictive in another. However, evolving Escherichia coli populations demonstrate remarkable flexibility in adaptation. The mechanisms sustaining genetic flexibility remain unclear. In E. coli the transcriptional regulation of gene expression involves both dedicated regulators binding specific DNA sites with high affinity and also global regulators - abundant DNA architectural proteins of the bacterial chromoid binding multiple low affinity sites and thus modulating the superhelical density of DNA. The first form of transcriptional regulation is dominantly pairwise and specific, representing digitial control, while the second form is (in strength and distribution) continuous, representing analog control. Here we look at the properties of effective networks derived from significant gene expression changes under variation of the two forms of control and find that upon limitations of one type of control (caused e.g. by mutation of a global DNA architectural factor) the other type can compensate for compromised regulation. Mutations of global regulators significantly enhance the digital control; in the presence of global DNA architectural proteins regulation is mostly of the analog type, coupling spatially neighboring genomic loci; together our data suggest that two logically distinct types of control are balancing each other. By revealing two distinct logical types of control, our approach provides basic insights into both the organizational principles of transcriptional regulation and the mechanisms buffering genetic flexibility. We anticipate that the general concept of distinguishing logical types of control will apply to many complex biological networks.
Complex biological functions are carried out by the interaction of genes and proteins. Uncovering the gene regulation network behind a function is one of the central themes in biology. Typically, it involves extensive experiments of genetics, biochemistry and molecular biology. In this paper, we show that much of the inference task can be accomplished by a deep neural network (DNN), a form of machine learning or artificial intelligence. Specifically, the DNN learns from the dynamics of the gene expression. The learnt DNN behaves like an accurate simulator of the system, on which one can perform in-silico experiments to reveal the underlying gene network. We demonstrate the method with two examples: biochemical adaptation and the gap-gene patterning in fruit fly embryogenesis. In the first example, the DNN can successfully find the two basic network motifs for adaptation - the negative feedback and the incoherent feed-forward. In the second and much more complex example, the DNN can accurately predict behaviors of essentially all the mutants. Furthermore, the regulation network it uncovers is strikingly similar to the one inferred from experiments. In doing so, we develop methods for deciphering the gene regulation network hidden in the DNN black box. Our interpretable DNN approach should have broad applications in genotype-phenotype mapping.
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