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Small non-coding RNAs (ncRNAs) are attracting increasing attention as they are now considered potentially valuable resources in the development of new drugs intended to cure several human diseases. A prerequisite for the development of drugs targeting ncRNAs or the related pathways is the identification and correct classification of such ncRNAs. State-of-the-art small ncRNA classification methodologies use secondary structural features as input. However, such feature extraction approaches only take global characteristics into account and completely ignore co-relative effects of local structures. Furthermore, secondary structure based approaches incorporate high dimensional feature space which is computationally expensive. The present paper proposes a novel Robust and Precise ConvNet (RPC-snRC) methodology which classifies small ncRNAs into relevant families by utilizing their primary sequence. RPC-snRC methodology learns hierarchical representation of features by utilizing positioning and information on the occurrence of nucleotides. To avoid exploding and vanishing gradient problems, we use an approach similar to DenseNet in which gradient can flow straight from subsequent layers to previous layers. In order to assess the effectiveness of deeper architectures for small ncRNA classification, we also adapted two ResNet architectures having a different number of layers. Experimental results on a benchmark small ncRNA dataset show that the proposed methodology does not only outperform existing small ncRNA classification approaches with a significant performance margin of 10% but it also gives better results than adapted ResNet architectures. To reproduce the results Source code and data set is available at https://github.com/muas16/small-non-coding-RNA-classification.

Small non-coding RNAs (ncRNAs) are attracting increasing attention as they are now considered potentially valuable resources in the development of new drugs intended to cure several human diseases. A prerequisite for the development of drugs targeting ncRNAs or the related pathways is the identification and correct classification of such ncRNAs. State-of-the-art small ncRNA classification methodologies use secondary structural features as input. However, such feature extraction approaches only take global characteristics into account and completely ignore co-relative effects of local structures. Furthermore, secondary structure based approaches incorporate high dimensional feature space which is computationally expensive. The present paper proposes a novel Robust and Precise ConvNet (RPC-snRC) methodology which classifies small ncRNAs into relevant families by utilizing their primary sequence. RPC-snRC methodology learns hierarchical representation of features by utilizing positioning and information on the occurrence of nucleotides. To avoid exploding and vanishing gradient problems, we use an approach similar to DenseNet in which gradient can flow straight from subsequent layers to previous layers. In order to assess the effectiveness of deeper architectures for small ncRNA classification, we also adapted two ResNet architectures having a different number of layers. Experimental results on a benchmark small ncRNA dataset show that the proposed methodology does not only outperform existing small ncRNA classification approaches with a significant performance margin of 10% but it also gives better results than adapted ResNet architectures. To reproduce the results Source code and data set is available at https://github.com/muas16/small-non-coding-RNA-classification.

Small non-coding RNAs (ncRNAs) are attracting increasing attention as they are now considered potentially valuable resources in the development of new drugs intended to cure several human diseases. A prerequisite for the development of drugs targeting ncRNAs or the related pathways is the identification and correct classification of such ncRNAs. State-of-the-art small ncRNA classification methodologies use secondary structural features as input. However, such feature extraction approaches only take global characteristics into account and completely ignore co-relative effects of local structures. Furthermore, secondary structure based approaches incorporate high dimensional feature space which is computationally expensive. The present paper proposes a novel Robust and Precise ConvNet (RPC-snRC) methodology which classifies small ncRNAs into relevant families by utilizing their primary sequence. RPC-snRC methodology learns hierarchical representation of features by utilizing positioning and information on the occurrence of nucleotides. To avoid exploding and vanishing gradient problems, we use an approach similar to DenseNet in which gradient can flow straight from subsequent layers to previous layers. In order to assess the effectiveness of deeper architectures for small ncRNA classification, we also adapted two ResNet architectures having a different number of layers. Experimental results on a benchmark small ncRNA dataset show that the proposed methodology does not only outperform existing small ncRNA classification approaches with a significant performance margin of 10% but it also gives better results than adapted ResNet architectures. To reproduce the results Source code and data set is available at https://github.com/muas16/small-non-coding-RNA-classification.

Small non-coding RNAs (ncRNAs) are attracting increasing attention as they are now considered potentially valuable resources in the development of new drugs intended to cure several human diseases. A prerequisite for the development of drugs targeting ncRNAs or the related pathways is the identification and correct classification of such ncRNAs. State-of-the-art small ncRNA classification methodologies use secondary structural features as input. However, such feature extraction approaches only take global characteristics into account and completely ignore co-relative effects of local structures. Furthermore, secondary structure based approaches incorporate high dimensional feature space which is computationally expensive. The present paper proposes a novel Robust and Precise ConvNet (RPC-snRC) methodology which classifies small ncRNAs into relevant families by utilizing their primary sequence. RPC-snRC methodology learns hierarchical representation of features by utilizing positioning and information on the occurrence of nucleotides. To avoid exploding and vanishing gradient problems, we use an approach similar to DenseNet in which gradient can flow straight from subsequent layers to previous layers. In order to assess the effectiveness of deeper architectures for small ncRNA classification, we also adapted two ResNet architectures having a different number of layers. Experimental results on a benchmark small ncRNA dataset show that the proposed methodology does not only outperform existing small ncRNA classification approaches with a significant performance margin of 10% but it also gives better results than adapted ResNet architectures. To reproduce the results Source code and data set is available at https://github.com/muas16/small-non-coding-RNA-classification.

Small non-coding RNAs (ncRNAs) are attracting increasing attention as they are now considered potentially valuable resources in the development of new drugs intended to cure several human diseases. A prerequisite for the development of drugs targeting ncRNAs or the related pathways is the identification and correct classification of such ncRNAs. State-of-the-art small ncRNA classification methodologies use secondary structural features as input. However, such feature extraction approaches only take global characteristics into account and completely ignore co-relative effects of local structures. Furthermore, secondary structure based approaches incorporate high dimensional feature space which is computationally expensive. The present paper proposes a novel Robust and Precise ConvNet (RPC-snRC) methodology which classifies small ncRNAs into relevant families by utilizing their primary sequence. RPC-snRC methodology learns hierarchical representation of features by utilizing positioning and information on the occurrence of nucleotides. To avoid exploding and vanishing gradient problems, we use an approach similar to DenseNet in which gradient can flow straight from subsequent layers to previous layers. In order to assess the effectiveness of deeper architectures for small ncRNA classification, we also adapted two ResNet architectures having a different number of layers. Experimental results on a benchmark small ncRNA dataset show that the proposed methodology does not only outperform existing small ncRNA classification approaches with a significant performance margin of 10% but it also gives better results than adapted ResNet architectures. To reproduce the results Source code and data set is available at https://github.com/muas16/small-non-coding-RNA-classification.

This thesis illustrates intrinsic properties and sex or stress steroid-induced alterations of adipose tissue function. In addition, this thesis reveals sex differences in thermal perception, a contributing factor for the control of brown adipose tissue activity.

8 p., Background: Colorectal cancer (CRC) is the second most common cause of death from cancer in both men and women in the majority of developed countries. Molecular tests of blood could potentially provide this ideal screening tool. -- Aim: Our objective was to assess the usefulness of serum markers and mRNA expression levels in the diagnosis of CRC. -- Methods: In a prospective study, we measured mRNA expression levels of 13 markers (carbonic anhydrase, guanylyl cyclase C, plasminogen activator inhibitor, matrix metalloproteinase 7 (MMP7), urokinase-type plasminogen activator receptor (uPAR), urokinase-type plasminogen activator, survivin, tetranectin, vascular endothelial growth factor (VEGF), cytokeratin 20, thymidylate synthase, cyclooxygenase 2 (COX-2), and CD44) and three proteins in serum (alpha 1 antitrypsin, carcinoembryonic antigen (CEA) and activated C3 in 42 patients with CRC and 33 with normal colonoscopy results. -- Results: Alpha 1-antitrypsin was the serum marker that was most useful for CRC diagnosis (1.79 +/- 0.25 in the CRC group vs 1.27 +/- 0.25 in the control group, P < 0.0005). The area under the ROC curve for alpha 1-antitrypsin was 0.88 (0.79-0.96). The mRNA expression levels of five markers were statistically different between CRC cases and controls: those for which the ROC area was over 75% were MMP7 (0.81) and tetranectin (0.80), COX-2 (0.78), uPAR (0.78) and carbonic anhydrase (0.77). The markers which identified early stage CRC (Stages I and II) were alpha 1-antitrypsin, uPAR, COX-2 and MMP7. -- Conclusions: Serum alpha 1-antitrypsin and the levels of mRNA expression of MMP7, COX-2 and uPAR have good diagnostic accuracy for CRC, even in the early stages.

8 p., Background: Colorectal cancer (CRC) is the second most common cause of death from cancer in both men and women in the majority of developed countries. Molecular tests of blood could potentially provide this ideal screening tool. -- Aim: Our objective was to assess the usefulness of serum markers and mRNA expression levels in the diagnosis of CRC. -- Methods: In a prospective study, we measured mRNA expression levels of 13 markers (carbonic anhydrase, guanylyl cyclase C, plasminogen activator inhibitor, matrix metalloproteinase 7 (MMP7), urokinase-type plasminogen activator receptor (uPAR), urokinase-type plasminogen activator, survivin, tetranectin, vascular endothelial growth factor (VEGF), cytokeratin 20, thymidylate synthase, cyclooxygenase 2 (COX-2), and CD44) and three proteins in serum (alpha 1 antitrypsin, carcinoembryonic antigen (CEA) and activated C3 in 42 patients with CRC and 33 with normal colonoscopy results. -- Results: Alpha 1-antitrypsin was the serum marker that was most useful for CRC diagnosis (1.79 +/- 0.25 in the CRC group vs 1.27 +/- 0.25 in the control group, P < 0.0005). The area under the ROC curve for alpha 1-antitrypsin was 0.88 (0.79-0.96). The mRNA expression levels of five markers were statistically different between CRC cases and controls: those for which the ROC area was over 75% were MMP7 (0.81) and tetranectin (0.80), COX-2 (0.78), uPAR (0.78) and carbonic anhydrase (0.77). The markers which identified early stage CRC (Stages I and II) were alpha 1-antitrypsin, uPAR, COX-2 and MMP7. -- Conclusions: Serum alpha 1-antitrypsin and the levels of mRNA expression of MMP7, COX-2 and uPAR have good diagnostic accuracy for CRC, even in the early stages.

8 p., Background: Colorectal cancer (CRC) is the second most common cause of death from cancer in both men and women in the majority of developed countries. Molecular tests of blood could potentially provide this ideal screening tool. -- Aim: Our objective was to assess the usefulness of serum markers and mRNA expression levels in the diagnosis of CRC. -- Methods: In a prospective study, we measured mRNA expression levels of 13 markers (carbonic anhydrase, guanylyl cyclase C, plasminogen activator inhibitor, matrix metalloproteinase 7 (MMP7), urokinase-type plasminogen activator receptor (uPAR), urokinase-type plasminogen activator, survivin, tetranectin, vascular endothelial growth factor (VEGF), cytokeratin 20, thymidylate synthase, cyclooxygenase 2 (COX-2), and CD44) and three proteins in serum (alpha 1 antitrypsin, carcinoembryonic antigen (CEA) and activated C3 in 42 patients with CRC and 33 with normal colonoscopy results. -- Results: Alpha 1-antitrypsin was the serum marker that was most useful for CRC diagnosis (1.79 +/- 0.25 in the CRC group vs 1.27 +/- 0.25 in the control group, P < 0.0005). The area under the ROC curve for alpha 1-antitrypsin was 0.88 (0.79-0.96). The mRNA expression levels of five markers were statistically different between CRC cases and controls: those for which the ROC area was over 75% were MMP7 (0.81) and tetranectin (0.80), COX-2 (0.78), uPAR (0.78) and carbonic anhydrase (0.77). The markers which identified early stage CRC (Stages I and II) were alpha 1-antitrypsin, uPAR, COX-2 and MMP7. -- Conclusions: Serum alpha 1-antitrypsin and the levels of mRNA expression of MMP7, COX-2 and uPAR have good diagnostic accuracy for CRC, even in the early stages.