Using Genetic Algorithm, this paper presents a modelling method to generate novel logical-based features from DNA sequences enriched with H3K4mel histone signatures. Current histone signature is mostly represented using k-mers content features incapable of representing all the possible complex interactions of various DNA segments. The main contributions are, among others: (a) demonstrating that there are complex interactions among sequence segments in the histone regions; (b) developing a parse tree representation of the logical complex features. The proposed novel feature is compared to the k-mers content features using datasets from the mouse (mm9) genome. Evaluation results show that the new feature improves the prediction performance as shown by f-measure for all datasets tested. Also, it is discovered that tree-based features generated from a single chromosome can be generalized to predict histone marks in other chromosomes not used in the training. These findings have a great impact on feature design considerations for histone signatures as well as other classifier design features.
The race for the discovery of enhancers at a genome-wide scale has been on since the commencement of next generation sequencing decades after the discovery of the first enhancer, SV40. A few enhancer-predicting features such as chromatin feature, histone modifications and sequence feature had been implemented with varying success rates. However, to date, there is no consensus yet on the single enhancer marker that can be employed to ultimately distinguish and uncover enhancers from the enormous genomic regions. Many supervised, unsupervised and semi-supervised computational approaches had emerged to complement and facilitate experimental approaches in enhancer discovery. In this review, we placed our focus on the recently emerged enhancer predictor tools that work on general enhancer features such as sequences, chromatin states and histone modifications, eRNA and of multiple feature approach. Comparisons of their prediction methods and outcomes were done across their functionally similar counterparts. We provide some recommendations and insights for future development of more comprehensive and robust tools.
Despite significant advancements in screening, diagnosis, and treatment of non-small cell lung cancer (NSCLC), it remains the primary cause of cancer-related deaths globally. DNA damage is caused by the exposure to exogenous and endogenous factors and the correct functioning of DNA damage repair (DDR) is essential to maintain of normal cell circulation. The presence of genomic instability, which results from defective DDR, is a critical characteristic of cancer. The changes promote the accumulation of mutations, which are implicated in cancer cells, but these may be exploited for anti-cancer therapies. NSCLC has a distinct genomic profile compared to other tumors, making precision medicine essential for targeting actionable gene mutations. Although various treatment options for NSCLC exist including chemotherapy, targeted therapy, and immunotherapy, drug resistance inevitably arises. The identification of deleterious DDR mutations in 49.6% of NSCLC patients has led to the development of novel target therapies that have the potential to improve patient outcomes. Synthetic lethal treatment using poly (ADP-ribose) polymerase (PARP) inhibitors is a breakthrough in biomarker-driven therapy. Additionally, promising new compounds targeting DDR, such as ATR, CHK1, CHK2, DNA-PK, and WEE1, had demonstrated great potential for tumor selectivity. In this review, we provide an overview of DDR pathways and discuss the clinical translation of DDR inhibitors in NSCLC, including their application as single agents or in combination with chemotherapy, radiotherapy, and immunotherapy.
The Human Immunodeficiency Virus (HIV) infection is a global pandemic that has claimed 33 million lives to-date. One of the most efficacious treatments for naïve or pretreated HIV patients is the HIV integrase strand transfer inhibitors (INSTIs). However, given that HIV treatment is life-long, the emergence of HIV strains resistant to INSTIs is an imminent challenge. In this work, we showed two best regression QSAR models that were constructed using a boosted Random Forest algorithm (r2 = 0.998, q210CV = 0.721, q2external_test = 0.754) and a boosted K* algorithm (r2 = 0.987, q210CV = 0.721, q2external_test = 0.758) to predict the pIC50 values of INSTIs. Subsequently, the regression QSAR models were deployed against the Drugbank database for drug repositioning. The top-ranked compounds were further evaluated for their target engagement activity using molecular docking studies and accelerated Molecular Dynamics simulation. Lastly, their potential as INSTIs were also evaluated from our literature search. Our study offers the first example of a large-scale regression QSAR modelling effort for discovering highly active INSTIs to combat HIV infection.Communicated by Ramaswamy H. Sarma.