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.
In the literature, neuronal networks are often represented as graphs where each node symbolizes a neuron and each arc stands for a synaptic connection. Some specific neuronal graphs have biologically relevant structures and behaviors and we call them archetypes. Six of them have already been characterized and validated using formal methods. In this work, we tackle the next logical step and proceed to the study of the properties of their couplings. For this purpose, we rely on Leaky Integrate and Fire neuron modeling and we use the synchronous programming language Lustre to implement the neuronal archetypes and to formalize their expected properties. Then, we exploit an associated model checker called kind2 to automatically validate these behaviors. We show that, when the archetypes are coupled, either these behaviors are slightly modulated or they give way to a brand new behavior. We can also observe that different archetype couplings can give rise to strictly identical behaviors. Our results show that time coding modeling is more suited than rate coding modeling for this kind of studies.
With the technological advances that allow for high throughput profiling of biological systems at a low cost. The low cost of data generation is leading us to the "big data" era. The availability of big data provides unprecedented opportunities but also raises new challenges for data mining and analysis. In this talk, I will start will the concepts in the analysis of big data, specifically the AI algorithms. My group has in The Biomedical Informatics Lab (BIL) is a research Centre is the focus of the education, research and development, and human-resource training in heath informatics and bioinformatics at NTU. The mission of BIL is to provide the interdisciplinary environment and training for students and researchers to engage in leading and cutting edge research in bioinformatics, and thereby become a part of the life sciences workforce in Singapore and elsewhere. This talk, by presenting selected research activities, will provide an overview of some of the innovative and creative approaches with the application of AI in big data analytics to address the challenges and solutions in both health and bioinformatics.
This paper describing a method of specifying common terms of genes from microarray data in 3 steps. First, we use random forest for extracting disease-related genes and it give each gene variable importance. The higher the variable importance, the more effective feature for classification. We extract genes whose variable importance more than 0 and set them positive samples and the rest set negative samples for ILP. Next, we annotate extracted genes by using Gene Ontology (GO) and use the term as predicate for ILP. Annotation is the process of assigning GO terms to gene products. Finally, we obtain rules about common terms in positive samples by using ILP. ILP is a subfield of machine learning which uses logic programming as a uniform representation technique for examples, background knowledge and hypotheses. ILP learns based on background knowledge. Background knowledge is represented in first-order logic. In the result, we extracted 1051 mRNA as positive samples for ILP from random forest and its F-measure score was 65.1%. We obtained about 4000 terms at each dataset and use them as predicates for ILP. We got eventually some rules about positive samples.
Vietnam has an abundant of herbal traditional medicine with accumulated experience for thousands of years. They play an important role in the drug development. However, several therapeutic effects remain unknown among these plants. To explore active ingredients in the effective Vietnamese herbal medicine formulations for individual diseases and to understand therapeutic effects under scientific viewpoint, this project predicts therapeutic effects based on metabolite profiles. The herbal medicine database has been processed to get the useful information by the supporting of computational approach, particularly Random forest algorithm, Generalized Boosted Model and Support Vector Machine. Three specific therapeutic effects which are ''Edema treatment", "Astrictive treatment" and "Cure sore eyes" - metabolites binary classification model to deal with multi-class classification and Unbalanced class data problem. Since this project can reveal the main predictors of specific therapeutic effect, they are valuable information for further research of drug development.
Translating the growing volumes of proteomics mass spectrometry data into reusable evidence of the occurrence and provenance of proteomics events requires the development of novel algorithms and community-scale computational workflows. MassIVE (http://massive.ucsd.edu) proposes to address this challenge in three stages. First, systematic annotation of human proteomics big data requires automated reanalysis of all public data using open source workflows with detailed records of search parameters and of individual Peptide Spectrum Matches (PSMs). As such, our large-scale reanalysis of tens of terabytes of human data has now increased the total number of proper public PSMs by over 10-fold to over 320 million PSMs whose coverage includes over 95 Second, proper synthesis of community-scale search results into a reusable knowledge base (KB) requires scalable workflows imposing strict statistical controls. Our MassIVE-KB spectral library has thus properly assembled 2+ million precursors from over 1.5 million peptides covering over 6.2 million amino acids in the human proteome, all of which at least double the numbers covered by the popular NIST spectral libraries. Moreover, MassIVE-KB detects 723 novel proteins (PE 2-5) for a total of 16,852 proteins observed in non-synthetic LCMS runs and 19,610 total proteins when including the recent ProteomeTools data. Third, we show how advanced identification algorithms combine with public data to reveal dozens of unexpected putative modifications supported by multiple highly-correlated spectra. These show that protein regions can be observed in over 100 different variants with various combinations of post-translational modifications and cleavage events, thus suggesting that current coverage of proteome diversity (at 1.3 variants per protein region) is far below what is observable in experimental data.
Cancer has several pathways by which it is developed in our body. Among them folic acid biosynthetic pathway is one where dihydrofolate reductase (DHFR) enzyme converts dihydrofolate into tetrahydrofolate which leads to unwanted and uncontrollable growth of tissues. Our aim of this study is to design DHFR antagonistic potential small molecules that inhibits Folic Acid Biosynthetic Pathways. In this study, Human DHFR obtained from Protein Data Bank (PDB) docked with several established anticancer drugs including Afatinib, Doxorubicin, Trimetrexate, Curcumin & Trimethoprim and several potential small molecules including Acarbose, Adenosine monophosphate, Abacavir, Aceprometazine & Isoxyl; obtained from PubChem and Drug Bank respectively. PyMOL and PyRx were used to visualize, curate and dock. For validation purpose Discovery Studio and Ramachandran Plot were run. Results after docking showed best binding affinities of established anticancer drugs with Human DHFR throughout the generations for example Methotrexate to Trimethoprim. Potential small molecules which belong from different therapeutic classes.
In the search of safer and more effective drugs while reducing costs and increasing productivity of novel drug discovery, scientists are changing their focus to an approach known as drug repurposing. This involves finding a new therapeutic effect of an already existing drug. It is a method that can effectively be addressed in the drug discovery and development challenges of targeting different disorders. Many drugs which have failed clinical trials for not being effective in their intended therapeutic indication have also been repurposed which has been of great benefit for pharmaceutical industries. For instance, sildenafil failed its clinical trials and was repurposed and currently in use as a repurposed drug. Many methods are available for drug repurposing but in silico method is a cost effective and convenient method for drug repurposing which uses computer software to find a possible binding site of a drug within a protein. For its advantages, computational docking approach was used for the present drug repurposing study of mTOR protein, where the drugs chosen were metformin, aspirin and rosuvastatin. Autodock Vina and PyMOL was used to complete the study and it was found that aspirin and metformin have poor affinity (-5.8 kcal/mol) for this protein which is upregulated in various types of cancer such as breast cancer and ovarian cancer. On the other hand, rosuvastatin was found to have a high affinity (-7.8 kcal/mol in case of flexible docking and -10.2 kcal/mol in case of rigid docking) for mTOR and binds to the same binding pocket where the immunosuppressant and anticancer drug rapamycin binds. The study therefore indicates that rosuvastatin might have significant immunosuppressive and anticancer activity by downregulating the activity of mTOR and needs further studies to prove it.
Breathing activity can be independently measured electronically, e.g., using a thoracic belt or a nasal thermistor or be reconstructed from noninvasive measurements such as an ECG. In this paper, the use of an accelerometer sensor to measure respiratory activity is presented. Movement of the chest was recorded by an accelerometer sensor attached to a belt around the chest. The acquisition is realized in different status: normal, apnea, deep breathing or after exhaustion and also in different postures: vertical (sitting, standing) or horizontal (lying down). The results of the experimental evaluation indicate that using a chest -accelerometer can correctly detect the waveform and the respiration rate. This method could, therefore, be suitable for automatic identification of some respiratory malfunction, for example during the obstructive apnea.
The rapid improvement of the next-generation sequencing (NGS) technologies has enabled unprecedented production of huge DNA sequence data at low cost. However, the NGS technologies are still limited to generate short DNA sequences, which has led to the development of many assembly algorithms to recover whole genome sequences from those short sequences. Unfortunately, the assembly algorithms alone can only construct scaffold sequences, which are generally much shorter than chromosome sequences. To generate chromosome sequences, additional expensive experimental data is required. To overcome this problem, there have been many studies to develop new computational algorithms to further merge the scaffold sequences, and produce chromosome-level sequences by utilizing an existing genome assembly of a related species called a reference. However, even though the quality of the chosen reference assembly is critical for generating a good final assembly, its effect is not well uncovered yet. In this study, we measured the effect of the reference genome assembly on the quality of the final assembly generated by reference-guided assembly algorithms. By using the genome assemblies of total eleven reference species (eight primates and three rodents), the human genome sequences were assembled from scaffold sequences by one of the reference-guided assembly algorithms, called RACA, and they were compared with known genome sequences to measure their quality in terms of the number of misassemblies. The effect of the quality of the reference assemblies was investigated in terms of divergence time against human, alignment coverage between the reference and human, and the amount of inclusion of core eukaryotic genes. We found that the divergence time is a good indicator of the quality of the final assembly when reference assemblies with high quality are used. We believe this study will contribute to broaden our understanding of the effect and importance of a reference assembly on the reference-guided assembly task.
The alternative splicing (AS) is one of the most important contributions for increasing the gene's expression biodiversity. However, whether individual isoforms can exhibit substantial differences in gene expression is unclear. Here, we profiled the AS characteristics in the whole transcriptome of 20 HeLa cells at single-cell level. For the most of the AS, they show the pattern of stochasticity among different single cells, but the pattern of dominant isoform usages in a specific cell. The pathway analysis of the differential AS indicates that the cell cycle state might also have a major influence on the isoform usages. We also identify several cancer-related pathways, including WNT signaling and NOTCH signaling. Furthermore, by investigating the potential regulatory network under the AS, several disease-related transcription factors were identified, including FOS, YWHAZ, and STAT3, which might play important roles in cervical cancer. Together, we perform the comprehensive analysis of AS at single-cell level and reveal the AS patterns and potential roles in both normal cellular process and tumor development.
A non-coding RNA (ncRNA) is an RNA molecule that is not coded into a protein. ncRNAs are important regulators of gene expression, and have a wide range of functions in cellular and developmental processes. However, to date, only a few well-studied ncRNAs have provided important clues about their relevance to particular diseases. This study aims to find disease-related mRNAs and long intergenic non-coding RNAs (lincRNAs) from microarray data. More specifically, this study focuses on acute lung injury (ALI) disease. It seeks to distinguish between ALI and normal one. We utilize microarray data including mRNAs and lincRNAs that contain transcriptomic characterization of mice's brain inflammation during ALI.These samples are obtained at six time points (1h, 1.5h, 3h, 4h, 18h, and 24h) after injection of oleic acid or physiological serum. Because microarray data contains a large number of genes with a small number of samples, we must extract genes that are relevant for classification. First, we divide the dataset into three groups according to elapsed time after injection: group1 with early elapsed time (1h, 1.5h, 3h, and 4h), group2 with late elapsed time (18h and 24h), and group3 with full-length elapsed time (1h to 24h). We then extract some genes using a random forest algorithm for each group. A random forest algorithm can identify which features (genes) are important for classification. Finally, we obtain some important genes (mRNAs and lincRNAs) for classification as output. From these result, we distinguish between mRNA and lincRNA, and extract some mRNAs and lincRNAs using a random forest algorithm from microarray data. The final results indicate that some extracted lincRNAs may function like some mRNAs in ALI disease. This study provides new knowledge for biologists and medical scientists by investigating lincRNAs that may be related to ALI disease.
Phenotypic differences among individuals of the same species are the result of a set of genetic variations which can be observed in the DNA sequence. To conduct a population genetic study, a high throughput genotyping platform such as Single Nucleotide Polymorphism (SNP) array is popularly used to obtain a large set of SNPs for each individual. However, analyzing today's genotypic data can be computationally expensive due to its large size and complexity. Faulty substructure may also be detected if the data is noisy from redundant or non-informative SNPs. Considerable efforts have been done to extract a smaller informative SNP subset that still represents the same intrinsic structure of populations within a data set as the full panel of SNPs. This work describes a foundation of a PCA-based informative marker selection technique. The proposed technique is simple and efficient. It improves upon another spectral analysis technique called PCA-correlated SNPs. A new informativeness score based on a basis function expansion of the SNP variation patterns across individuals is introduced. Such score is computed for each SNP to select a subset of SNPs with the best scores. Using a bovine data set, we demonstrate that our technique is superior to the PCA-correlated SNPs method, which requires accurate rank estimation to perform well. In contrast, our method is robust to the assumed rank of the data. High data representation accuracy is also achieved after a significant reduction of the number of SNPs while retaining information about the underlying population structure from the original data.
Finding a disease-related gene is important in drug discovery. However, Alzheimer's disease involves many genes, and conducting experiments to find a disease-related gene is costly. Therefore, machine learning on gene expression microarray data is a suitable method to address this problem. However, sometimes the results are not fully correct. The present study aims to find a disease-related gene using random forest and to compare the results with previous medical research. In doing so, genes are determined to be disease-related using two methods. With random forest, we use the microarray data from GEO data sets. We feature a common gene in each data and combine them into a data set. A gene that has high importance is chosen. Random forest found 51 genes. In contrast, from previous medical science research, the study found 6167 genes. The study used the square of the impact factor to rank the genes. The impact factor is gained from the average number of citations received per paper published in that journal during the two preceding years. Based on the results from random forest and comparison with previous studies, we found 31 genes that are the same. Four of these genes are related to the GENE database, and the remaining 27 genes are related potential genes or unknown genes found in this study. The high ranking among them are considered to be very likely related. A low-ranking gene should also be considered. Genes in which the first method has been extracted. However, in the second method, the genes have not extracted thought new related potential gene. In conclusion, comparing random forest results to another related study in medical science is a powerful method to find unknown genes related to Alzheimer's disease.
Mucor circinelloides is an attractive oleaginous filamentous fungus because it can produce high content of triacylglycerol (TAG) rich in γ-linolenic acid (GLA). With the increasing attention in microbial oils, many experiments have been carried out in order to enhance storage lipid production in M. circinelloides. Availabilities of genome sequence, biotechnology and computational tools, and pathway databases have facilitated strain improvement through systems metabolic engineering. Genome-scale metabolic model (GEM) has become a useful tool for guiding the design of metabolic engineering strategies. In this study, the GEM of M. circinelloides, iWV1213, was used to investigate candidate genes and metabolites, which could drive metabolic fluxes for lipid production. To evaluate the metabolic capabilities related to growth and lipid formation of iWV1213, the model was firstly simulated and validated with experimental data. Using FBA and MOMA, a single gene deletion together with FVA were performed to identify candidate essential genes for growth and lipid production. Moreover, a single reaction deletion and shadow price analysis were also applied to determine the candidate essential metabolites. The results showed that the top 20 essential genes were involved in amino acid metabolism, TCA cycle, oxidative phosphorylation, and fatty acid oxidation, while the candidate metabolites were involved in lipid biosynthesis, such as malonyl-CoA and acetyl-CoA. These candidates could potentially be used as targets to guide future metabolic engineering strategies for enhancing TAG and GLA production in M. circinelloides.
This paper proposed a novel pseudo relevance feedback strategy to facilitate the retrieval of more relevant biomedical documents by improving the quality of both feedback documents and expansion terms. Firstly, an ontology-graph based query expansion technique is applied to retrieve more relevant feedback documents. Secondly, useful expansion terms are extracted from the feedback documents based on a semantic graph based ranking approach. We add the expansion terms to the user query to retrieve more relevant documents. We use 10-fold cross validation technique to evaluate the performance of the proposed pseudo relevance feedback strategy over OHSUMED test collection. The experimental results demonstrate that the proposed strategy improves the retrieval performance by 33.8% over free-text based query in 11-point average precision. The proposed strategy also achieves better retrieval performance than two representative pseudo relevance feedback approaches. We have integrated this new strategy into G-Bean, a graph-based biomedical search engine. G-Bean is available at: http://bioinformatics.clemson.edu:8080/G-Bean/index.jsp
