Autism spectrum disorder (ASD) is a common heterogeneous neurodevelopmental disorder with typical symptoms such as impaired social interaction, language and communication abnormalities and stereotypical behavior. Since the genetics of ASDs is so diverse, information on genome function as provided by transcriptomic data is essential to further our understanding. This is transcriptome is a key link between measuring protein levels and genetic information. Transcriptome-based studies have been often performed by comparing ASD and control groups to identify which genes are dysregulated in the ASD group using statistical techniques. However, these statistical techniques can only find genes solely related to ASD, but cannot reflect relationship among genes which could be the etiology of ASD. In this study, we propose a novel method to find the ASD-associated genes, which are predictive for ASD. For this purpose, we metaanalyze whole-transcriptomic data of previous studies for ASD, which were performed using some expression profiling platforms on different issues of interest. These predictive genes, which can differentiate a sample into either ASD or non-ASD, are selected by an optimization process. Comparing subsets selected from different tissues/platforms, we conclude that tissues contain different gene sets associated with ASD. In addition, a platform can supply other ASD-associated genes of which other platforms cannot. Identified genes are then compared to those which have been well documented in SFARI, which is the most comprehensive and up-to-date data of ASD. Interestingly, we can find two novel genes with evidences from literature, which have not yet been recorded in this database. In summary, meta-analysis on whole-transcriptome data of ASD could shed light on the etiology of ASD.
microRNAs (miRNAs) are important gene regulators, controlling a wide range of biological processes and being involved in several types of cancers. Currently, several computational approaches have been developed to elucidate the miRNA-mRNA regulatory relationships. However, these approaches have their own limitations and we are still far from understanding the miRNA-mRNA relationships, especially in specific biological processes. In this paper, we adapt a causal inference method to infer miRNA targets from the Epithelial Mesenchymal Transition (EMT) dataset. Our method utilises a causality based method that estimates the causal effect of each miRNA on a mRNA while controlling the effects of other miRNAs on the mRNA. The inferred causal effect is similar to the effect of a miRNA on a mRNA when we knockout all the other miRNAs. The experimental results show that our method is better than existing benchmark methods in finding experimentally confirmed miRNA targets. Moreover, we have found that the miR-200 family members (miR-141, miR-200a/b/c, and miR-429) synergistically regulate a number of target genes in EMT, suggesting their roles in controlling cancer metastasis. In addition, functional and pathway enrichment analyses show that the discovered miRNA-mRNA regulatory relationships are highly enriched in EMT, implying the validity of the proposed method. Novel miRNA-mRNA regulatory relationships discovered by our method provide a rich resource for follow up wet-lab experiments and EMT related studies.
Multiple Sequence Alignment (MSA) is a basic tool for biological sequence analysis and also a crucial step utilized by biologists to analyze phylogentic, gene regulations, homology marker, drug discovery, and predicting the protein structure and its functions. Effective Alignment of multiple sequences having biologic relevance is still an open problem. Accuracy of MSA is highly dependent on the scoring function, which aligns a given residue to its appropriate position during alignment. Scoring function has three possible cases to score a pair of residues: i) a residue with same residue, ii) a residue with different residue and iii) a residue with gap. A number of biological meaningful approaches are developed for the first two cases. However, for the third case, most of the approaches follow the default score for gap penalty, which is provided as an input by an expert. In this study, we propose a new, biologically relevant, and position-residue specific dynamic scoring approach for gap penalty. Position-Residue Specific Dynamic Gap Penalty (PRSDGP) scoring function is tested on the BAliBASE benchmark dataset. The proposed PRSDGP scoring approach is compared with the CLUSTAL O program and Quality metric improvement ranges from 46.2% to 81.5%.
We propose a formalisation of spiking neural networks based on timed automata networks. Neurons are modelled as timed automata waiting for inputs on a number of different channels (synapses), for a given amount of time (the accumulation period). When this period is over, the current potential value is computed taking into account the current inputs and the previous decayed potential value. If the current potential overcomes a given threshold, the automaton emits a broadcast signal over its output channel, otherwise it restarts another accumulation period. After each emission, the automaton is constrained to remain inactive for a fixed refractory period. Spiking neural networks are formalised as sets of automata, one for each neuron, running in parallel and sharing channels according to the structure of the network. The model is then validated against some crucial properties defined via proper temporal logic formulae.
Finding disease-related genes is important in drug discovery. Many genes are involved in the disease, and many studies have been conducted and reported for each disease. However, it is very costly to check these one by one. Therefore, machine learning is a suitable method to address this problem. By extracting study results from research papers by text mining, it is possible to make use of that knowledge. In this research, we aim to extract disease-related genes from PubMed papers using word2vec, which is a text mining method. The method extracts the top 10 genes whose known disease genes and vectors are close to those obtained by word2vec. Based on these, genes other than known disease-related genes are extracted and used as disease-related genes. We conducted experiments using schizophrenia, and confirmed the likelihood of this disease-related gene using xgboost. Pattern 1: Only known genes. Pattern 2: Pattern 1 plus disease-related genes extracted in this study. Pattern 3: Pattern 1 plus the same number of random genes. Using these three patterns, we performed a xgboost with microarray data and compared the classification accuracy. The result was that Pattern 2 had the highest accuracy. Therefore, we could extract genes with using genes related to disease by our method.
