Poster presentations at ISMB/ECCB 2021 will be presented virtually. Authors will pre-record their poster talk (5-7 minutes) and will upload it to the virtual conference platform site along with a PDF of their poster beginning July 19 and no later than July 23. All registered conference participants will have access to the poster and presentation through the conference and content until October 31, 2021. There are Q&A opportunities through a chat function and poster presenters can schedule small group discussions with up to 15 delegates during the conference.

Information on preparing your poster and poster talk are available at: https://www.iscb.org/ismbeccb2021-general/presenterinfo#posters

Ideally authors should be available for interactive chat during the times noted below:

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Session A: Sunday, July 25 between 15:20 - 16:20 UTC	Session B: Monday, July 26 between 15:20 - 16:20 UTC
3DSIG Bio-Ontologies BioVis HitSeq Special Session 01 TransMed	3DSIG Bio-Ontologies BioVis CompMS Education EvolCompGen HitSeq NetBio RegSys TransMed

Session C: Tuesday, July 27 between 15:20 - 16:20 UTC	Session D: Wednesday, July 28 between 15:20 - 16:20 UTC
3DSIG BioVis CompMS Education EvolCompGen HitSeq NetBio RegSys TransMed	CAMDA Education EvolCompGen Function iRNA MICROBIOME MLCSB RegSys Text Mining

Session E: Thursday, July 29 between 15:20 - 16:20 UTC
BIOINFO-CORE BOSC CAMDA COVID-19 EvolCompGen Function iRNA MICROBIOME MLCSB RegSys Special Session 05 SysMod Text Mining VarI General Comp Bio

Short Abstract: While humans developed amazing thinking abilities, other species evolved to have special powers to survive in nature. It will be amazing if we learnt from the billions of years of evolutionary success in different species. The genomic revolution characterized by Technological advances and shrinking cost of all DNA sequencing and analysis made it possible to predict the alleles and genes that gave some species their special powers. We are particularly utilizing comparative genomics approaches to predict alleles that made some species resistant to cancer. Interestingly that while we still barley trying to understand cancer, there are many species already “solved” it. They independently evolved to be almost cancer resistant. To find the specific alleles that enhanced cancer resistance we are systematically comparing the proteomes of cancer resistant species and cancer prone species. Alleles that are conserved in resistant species and not conserved in the prone species are hypothesized to be cancer protecting alleles. Our predicted anti-cancer map consists of thousands of alleles distributed in thousands of genes that are enriched in genes associated with cancer. The analysis of our predicted variants frequencies in healthy people and cancer patients support that these variants are cancer protecting alleles.

Short Abstract: Drug-induced liver injury (DILI) is one of the most common reasons for the withdrawal of drug candidates. Among the cases of DILI, detecting unexpected (idiosyncratic) liver injury poses an interesting challenge, since this is not directly tied to the (dose-dependent) toxicity of a drug or its metabolites. As such, literature search remains a major tool for sourcing DILI-related information, which often comes directly from clinical practice.

Here, we present dialogí, a text-mining tool that combines different Natural Language Processing (NLP) approaches, together with a linear classifier, to differentiate between DILI-positive and -negative PubMed abstracts. Often, within the same DILI-positive paper, multiple drugs-- most of which unrelated to DILI-- are mentioned. We, thus, expand our tool with a framework that tries to identify and extract key (DILI-positive) drugs on a paper-by-paper basis.

The aforementioned classifier was trained on 11,200 equally-split DILI-positive and -negative PubMed abstracts, including titles, and was validated (internally) on the remaining 2,800 abstracts, resulting in a precision of 94.8% and recall of 93.5%. On external validation, the model displayed precision and recall of 93.3% and 94.9%, respectively, with an accuracy of 94.1%.

Short Abstract: Drug-Induced Liver Injury (DILI) is the most frequent cause of acute liver failure in the majority of western countries[4] and is a major cause of attrition of novel drug candidates[2]. Manual trawling of literature for DILI papers is the main route of obtaining data from DILI studies. This makes it an inefficient process prone to human error. Therefore, an automatized AI model capable of retrieving DILI-related papers from the huge ocean of literature could be invaluable for the drug discovery community. In this project, we built an artificial intelligence (AI) model combining the power of Natural Language Processing (NLP) and Machine Learning (ML) to address the above problem. The keywords from NLP are processed by apriori pattern mining ML algorithm to extract relevant patterns which are used to estimate initial weightings for an ML classifier. Along with pattern importance and frequency, an FDA-approved drug list mentioning DILI adds extra confidence in classification. The combined power of these methods builds a DILI classifier with 94.91% cross-validation and94.14% external validation accuracy. An R Shiny App capable to classify single or multiple entries will be developed to enhance user experience.

Short Abstract: Drug-induced liver injury (DILI) is an adverse effect of drugs characterized by abnormalities in liver tests, and it may lead to acute liver failure. As a key assessment for new drug candidates, DILI events are reported in the publications of clinical practices and preliminary in vitro and in vivo experiments. Conventionally, screening the large corpus of publications to label DILI-related reports is carried out manually, which substantially limits the processing speed. The development of natural language processing (NLP) techniques enables the automatic processing of texts. Here, we report a model for filtering DILI literature with four NLP text vectorization techniques and ensemble learning. The model with TF-IDF and logistic regression outperformed others with an AUROC of 0.990, an accuracy of 0.957, and an AUPRC of 0.990. An ensemble model with similar performance but the fewest false-negative cases was built based on 12 models. Both models showed good performance on the hold-out validation data, and the ensemble model reached a higher accuracy of 0.954 and an F1 score of 0.955. Additionally, important words in positive/negative predictions were identified by interpreting the models. Generally, the ensemble model reached satisfactory classification results, which can be used by researchers to quickly filter DILI-related literature.

Short Abstract: Throughout course of SARS-CoV-2 pandemic, diagnostic laboratories and researchers all around the world had observed that different clades/lineages may impact COVID-19 diagnosis, leading to false results, which allows for further, unnoticed spread of virus. We had placed hypothesis that SNPs can drastically decrease diagnostic power and value of primer sets. With obtained in-vitro results of amplification of SARS-CoV-2 hypothesis has been strengthened and we evaluated in-silico how variability in genomes of SARS-CoV-2 in primer/probe binding sites may potentially affect their interactions, and suggest the best combinations for further consideration. We downloaded nearly 1.5 millions of SARS-CoV-2 genomes from GISAID, applied quality filters, and performed an analysis with our Python library pyprimer for the 15 publicly available primers/probe sets. We found that the five sets are most susceptible to the currently most abundant clades/lineages. Mismatches encompassing the binding sites for them are present in current Variants of Concern. Best performing five sets of primers can still detect almost all of VOC with high overall accuracy. Nonetheless, secondary structure of some of best performing primers raises concerns regarding similar structure properties from retracted sets, which by dimerization were producing false-positives.

Short Abstract: Medical text classification is important in the drug and biomedical discovery process. Traditional text mining techniques can identify patterns from a text based on a curated list of domain-specific words. However, these approaches are dependent on the subject matter and input of additional keywords is often required as new terminologies are introduced. We developed a hybrid model that does not require any input of curated words from the subject matter (drug-induced liver toxicity) and which could potentially be abstracted to a different domain. Our model exploits three features from a passage of text - its sequence, structure and higher dimensionality. We developed a hybrid CNN-bidirectional LSTM architecture using word embeddings as input. We simultaneously trained SVM and Naive Bayes classifiers using term tf–idf data as input. We obtained a majority vote for the classifiers. If there was a consensus, we considered this a stable prediction. However, if there was discordance between the classifiers, we implemented dynamic time warping (DTW) techniques to act as the final arbiter. DTW was used to align the tf–idf representations for every abstract and the prevalence of DILI or Non-DILI labelled abstracts in the 0.5% most aligned abstracts was taken as the prediction.

Short Abstract: In this study, we are applying TextNetTopics on textual data as a response to the CAMDA challenge. TextNetTopics is a novel approach that applies feature selection by considering topics of words rather than that traditional approach, Bag-of-words. Thus the approach performs topic selections rather than word selection. TextNetTopics is based on the generic approach of grouping and scoring/ranking.
The approach suggests ranked significant topics as its output along with the performance of building a model from top topics. The performance of TextNetTopics outperforms other feature selection approaches while getting a high performance when applying the model on the validation data provided by the CAMDA.

Short Abstract: Due to the COVID-19 pandemic, the global need for vaccines to prevent the disease is imperative. To date, several manufacturers have made efforts to develop vaccines against SARS-CoV-2. It will be helpful for future vaccine designs, resulting in long-term disease protection, if we know more details of the mechanism of T cell responses to SARS-CoV-2. In this study, we first detected DE genes between healthy donors and COVID-19 patients, and then built a healthy network and a COVID-19 network among those genes, separately. For each network, we identified modules and obtained hub genes for each module. Furthermore, we evaluated the differential connectivity for each gene between two networks. The results might improve the insight of gene expression associated with CD4+ T cells and expand our understanding of COVID-19.