Explicate molecular landscape of combined pulmonary fibrosis and emphysema through explainable artificial intelligence: a comprehensive analysis of ILD and COPD interactions using RNA from whole lung homogenates.

Combined pulmonary fibrosis and emphysema (CPFE) presents a unique challenge in respiratory disorders, merging features of interstitial lung disease (ILD) and chronic obstructive pulmonary disease (COPD). Using the random forest algorithm, our study thoroughly examines the molecular details of CPFE. Analyzing gene expression datasets from GSE47460 (ILD: 254, COPD: 220, control: 108), we identify key genes namely ADRB2, CDH3, IRS2, MATN3, CD38, PDIA4, VEGFC, and among twenty others, crucial in airway regulation, lung function, and apoptosis, shaping the complex pathogenesis of CPFE. Additionally, miRNAs (hsa-mir-101-3p, hsa-mir-1343-3p, hsa-mir-27a-3p, and miR-16-5p) showcase regulatory impacts on CPFE-related molecular pathways. Our machine learning model unveils these intricate interactions, offering a comprehensive insight into CPFE's molecular mechanisms. This research not only pinpoints potential therapeutic targets and biomarkers but also opens avenues for innovative approaches in managing CPFE, linking ILD and COPD within this complex respiratory condition.

Identifying the mitochondrial metabolism network by integration of machine learning and explainable artificial intelligence in skeletal muscle in type 2 diabetes.

Imbalance in glucose metabolism and insulin resistance are two primary features of type 2 diabetes/diabetes mellitus. Its etiology is linked to mitochondrial dysfunction in skeletal muscle tissue. The mitochondria are vital organelles involved in ATP synthesis and metabolism. The underlying biological pathways leading to mitochondrial dysfunction in type 2 diabetes can help us understand the pathophysiology of the disease. In this study, the mitochondrial gene expression dataset were retrieved from the GSE22309, GSE25462, and GSE18732 using Mitocarta 3.0, focusing specifically on genes that are associated with mitochondrial function in type 2 disease. Feature selection on the expression dataset of skeletal muscle tissue from 107 control patients and 70 type 2 diabetes patients using the XGBoost algorithm having the highest accuracy. For interpretation and analysis of results linked to the disease by examining the feature importance deduced from the model was done using SHAP (SHapley Additive exPlanations). Next, to comprehend the biological connections, study of protein-protien and mRNA-miRNA networks was conducted using String and Mienturnet respectively. The analysis revealed BDH1, YARS2, AKAP10, RARS2, MRPS31, were potential mitochondrial target genes among the other twenty genes. These genes are mainly involved in the transport and organization of mitochondria, regulation of its membrane potential, and intrinsic apoptotic signaling etc. mRNA-miRNA interaction network revealed a significant role of miR-375; miR-30a-5p; miR-16-5p; miR-129-5p; miR-1229-3p; and miR-1224-3p; in the regulation of mitochondrial function exhibited strong associations with type 2 diabetes. These results might aid in the creation of novel targets for therapy and type 2 diabetes biomarkers.

Acute and sub-acute metabolic change in different brain regions induced by moderate blunt traumatic brain injury.

The objective of the study was to observe the effect of moderate closed-head injury on hippocampal, thalamic, and striatal tissue metabolism with time. Closed head injury is responsible for metabolic changes. These changes can be permanent or temporary, depending on the injury's impact. For the experiment, 20 rats were randomly divided into four groups, each containing five animals. Animals were subjected to injury using a modified Marmarou's weight drop device; hippocampal, thalamic, and striatal tissue samples were collected after 1 day, 3 days, and 7 days of injury. NMR spectra were acquired following sample processing. Changes in myo-inositol, creatine, glutamate, succinate, lactate, and N-acetyl aspartic acid in hippocampal tissues were observed at day 3 PI. The tyrosine level in the hippocampus was altered at day 7 PI. While thalamic and striatal tissue samples showed altered levels of branched-chain amino acids and myo-inositol at day 1PI. Taurine, gamma amino butyric acid (GABA), choline, and alpha keto-glutarate levels were found to be significantly altered in striatal tissues at days 1 and 3PI. Acetate and GABA levels were altered in the thalamus on day 1 PI. The choline level in the thalamus was found to alter at all-time points after injury. The alteration in these metabolites may be due to the alteration in their respective pathways. Neurotransmitter and energy metabolism pathways were found to be altered in all three brain regions after TBI. This study may help better understand the effect of injury on the metabolic balance of a specific brain region and recovery.

Assessment of clinically actionable pharmacogenetic markers to stratify anti-seizure medications.

Epilepsy treatment is challenging due to heterogeneous syndromes, different seizure types and higher inter-individual variability. Identification of genetic variants predicting drug efficacy, tolerability and risk of adverse-effects for anti-seizure medications (ASMs) is essential. Here, we assessed the clinical actionability of known genetic variants, based on their functional and clinical significance and estimated their diagnostic predictability. We performed a systematic PubMed search to identify articles with pharmacogenomic (PGx) information for forty known ASMs. Functional annotation of the identified genetic variants was performed using different in silico tools, and their clinical significance was assessed using the American College of Medical Genetics (ACMG) guidelines for variant pathogenicity, level of evidence (LOE) from PharmGKB and the United States-Food and drug administration (US- FDA) drug labelling with PGx information. Diagnostic predictability of the replicated genetic variants was evaluated by calculating their accuracy. A total of 270 articles were retrieved with PGx evidence associated with 19 ASMs including 178 variants across 93 genes, classifying 26 genetic variants as benign/ likely benign, fourteen as drug response markers and three as risk factors for drug response. Only seventeen of these were replicated, with accuracy (up to 95%) in predicting PGx outcomes specific to six ASMs. Eight out of seventeen variants have FDA-approved PGx drug labelling for clinical implementation. Therefore, the remaining nine variants promise for potential clinical actionability and can be improvised with additional experimental evidence for clinical utility.

A cystatin C similar protein from Musa acuminata that inhibits cathepsin B involved in rheumatoid arthritis using in silico approach and in vitro cathepsin B inhibition by protein extract.

Rheumatoid arthritis (RA) is an auto-immune disease that affects the synovial lining of the joints, causes synovitis and culminates to joint destruction. Cathepsin B is responsible for digesting unwanted proteins in extracellular matrix but its hyper expression could implicate in pathological diseases like RA. Available treatments for RA are classified into non-steroidal anti-inflammatory drugs (NSAIDs), disease-modifying anti-rheumatic drugs (DMARDs), and steroids, but the severe side effects associated with these drugs is one of concerns and cannot be ignored. Thus, any alternative therapy with minimum or no side effects would be a cornerstone. In our in silico studies a cystatin C similar protein (CCSP) has been identified from Musa acuminata that could effectively inhibit the cathepsin B activity. In silico and molecular dynamics studies showed that the identified CCSP and cathepsin B complex has binding energy -66.89 kcal/mol as compared to cystatin C - cathepsin B complex with binding energy of -23.38 kcal/mol. These results indicate that CCSP from Musa acuminata has better affinity towards cathepsin B as compared to its natural inhibitor cystatin C. Hence, CCSP may be suggested as an alternative therapeutic in combating RA by inhibiting its one of the key proteases cathepsin B. Further, in vitro experiments with fractionated protein extracts from Musa sp. peel inhibited cathepsin B to 98.30% at 300 µg protein concentration and its IC50 was found to be 45.92 µg indicating the presence of cathepsin B inhibitor(s) in protein extract of peel which was further confirmed by reverse zymography.Communicated by Ramaswamy H. Sarma.

Rare deleterious mutations in Bruton's tyrosine kinase as biomarkers for ibrutinib-based therapy: an in silico insight.

CONTEXT: Squamous cell carcinoma (SCC) is the second most common type of skin cancer caused by malignant keratinocytes. Multiple studies have shown that protein mutations have a significant impact on the development and progression of cancer, including SCC. We attempted to decode the effect of single amino acid mutations in the Bruton's tyrosine kinase (BTK) protein in this study. Molecular dynamic (MD) simulations were performed on selected deleterious mutations of the BTK protein, revealing that the variants adversely affect the protein, indicating that they may contribute to the prognosis of SCC by making the protein unstable. Then, we investigated the interaction between the protein and its mutants with ibrutinib, a drug designed to treat SCC. Even though the mutations have deleterious effects on protein structure, they bind to ibrutinib similarly to their wild type counterpart. This study demonstrates that the effect of detected missense mutations is unfavorable and can result in function loss, which is severe for SCC, but that ibrutinib-based therapy can still be effective on them, and the mutations can be used as biomarkers for Ibrutinib-based treatment. METHODS: Seven different computational techniques were used to compute the effect of SAVs in accordance with the experimental requirements of this study. To understand the differences in protein and mutant dynamics, MD simulation and trajectory analysis, including RMSD, RMSF, PCA, and contact analysis, were performed. The free binding energy and its decomposition for each protein-drug complex were determined using docking, MM-GBSA, MM-PBSA, and interaction analysis (wild and mutants).

Acute metabolic alterations in the hippocampus are associated with decreased acetylation after blast induced TBI.

INTRODUCTION: Blast induced Traumatic brain injury (BI-TBI) is common among military personnels as well as war affected civilians. In the war zone, people can also encounter repeated exposure of blast wave, which may affect their cognition and metabolic alterations. OBJECTIVE: In this study we assess the metabolic and histological changes in the hippocampus of rats at 24 h post injury. METHOD: Rats were divided into four groups: (i) Sham; (ii) Mild TBI (mi); (iii) Moderate TBI (mo); and (iv) Repetitive mild TBI (rm TBI) and then subjected to different intensities of blast exposure. Hippocampal tissues were collected after 24 h of injury for proton nuclear magnetic resonance spectroscopy (1H NMR spectroscopy) and immunohistochemical (IHC) analysis. RESULTS: The metabolic alterations were found in the hippocampal tissue samples and these alterations showed significant change in glutamate, N-Acetylaspartic acid (NAA), acetate, creatine, phosphoethanolamine (PE), ethanolamine and PC/choline concentrations in rmTBI rats only. IHC studies revealed that AH3 (Acetyl histone) positive cells were decreased in rm TBI tissue samples in comparison to other TBI groups and sham rats. This might reflect an epigenetic alteration due to repeated blast exposure at 24 h post injury. Additionally, astrogliosis was observed in miTBI and moTBI hippocampal tissue while no change was observed in rmTBI tissues. CONCLUSION: The present study reports altered acetylation in the presence of altered metabolism in hippocampal tissue of blast induced rmTBI at 24 h post injury. Mechanistic understanding of these intertwined processes may help in the development of better therapeutic pathways and agents for blast induced TBI in near future.

Epicatechin analogues may hinder human parainfluenza virus infection by inhibition of hemagglutinin neuraminidase protein and prevention of cellular entry.

Human parainfluenza viruses (HPIVs) are ( -)ssRNA viruses belonging to Paramyoviridaie family. They are one of the leading causes of mortality in infants and young children and can cause ailments like croup, bronchitis, and pneumonia. Currently, no antiviral medications or vaccines are available to effectively treat parainfluenza. This necessitates the search for a novel and effective treatment. Computer-aided drug design (CADD) methodology can be utilized to discover target-based inhibitors with high accuracy in less time. A library of 45 phytocompounds with immunomodulatory properties was prepared. Thereafter, molecular docking studies were conducted to characterize the binding behavior of ligand in the binding pocket of HPIV3 HN protein. The physicochemical properties for screened compounds were computed, and the top hits from docking studies were further analyzed and validated using molecular dynamics simulation studies using the Desmond module of Schrodinger Suite 2021-1, followed by MM/GBSA analysis. The compounds CID:72276 (1) and CID:107905 (2) emerged as lead compounds of our in silico investigation. Further in vitro studies will be required to prove the efficacy of lead compounds as inhibitors and to determine the exact mechanism of their inhibition. Computational studies predict three natural flavonoids to inhibit the HN protein of HPIV3.

In-silico screening and in-vitro assay show the antiviral effect of Indomethacin against SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worldwide spread of coronavirus disease 19 (COVID-19), and till now, it has caused death to more than 6.2 million people. Although various vaccines and drug candidates are being tested globally with limited to moderate success, a comprehensive therapeutic cure is yet to be achieved. In this study, we applied computational drug repurposing methods complemented with the analyses of the already existing gene expression data to find better therapeutics in treatment and recovery. Primarily, we identified the most crucial proteins of SARS-CoV-2 and host human cells responsible for viral infection and host response. An in-silico screening of the existing drugs was performed against the crucial proteins for SARS-CoV-2 infection, and a few existing drugs were shortlisted. Further, we analyzed the gene expression data of SARS-CoV-2 in human lung epithelial cells and investigated the molecules that can reverse the cellular mRNA expression profiles in the diseased state. LINCS L1000 and Comparative Toxicogenomics Database (CTD) were utilized to obtain two sets of compounds that can be used to counter SARS-CoV-2 infection from the gene expression perspective. Indomethacin, a nonsteroidal anti-inflammatory drug (NSAID), and Vitamin-A were found in two sets of compounds, and in the in-silico screening of existing drugs to treat SARS-CoV-2. Our in-silico findings on Indomethacin were further successfully validated by in-vitro testing in Vero CCL-81 cells with an IC50 of 12 µM. Along with these findings, we briefly discuss the possible roles of Indomethacin and Vitamin-A to counter the SARS-CoV-2 infection in humans.

Bidirectional Microbiome-Gut-Brain-Axis Communication Influences Metabolic Switch-Associated Responses in the Mosquito Anopheles culicifacies.

The periodic ingestion of a protein-rich blood meal by adult female mosquitoes causes a drastic metabolic change in their innate physiological status, which is referred to as a 'metabolic switch'. While understanding the neural circuits for host-seeking is modestly attended, how the gut 'metabolic switch' modulates brain functions, and resilience to physiological homeostasis, remains unexplored. Here, through a comparative brain RNA-Seq study, we demonstrate that the protein-rich diet induces the expression of brain transcripts related to mitochondrial function and energy metabolism, possibly causing a shift in the brain's engagement to manage organismal homeostasis. A dynamic mRNA expression pattern of neuro-signaling and neuro-modulatory genes in both the gut and brain likely establishes an active gut-brain communication. The disruption of this communication through decapitation does not affect the modulation of the neuro-modulator receptor genes in the gut. In parallel, an unusual and paramount shift in the level of neurotransmitters (NTs), from the brain to the gut after blood feeding, further supports the idea of the gut's ability to serve as a 'second brain'. After blood-feeding, a moderate enrichment of the gut microbial population, and altered immunity in the gut of histamine receptor-silenced mosquitoes, provide initial evidence that the gut-microbiome plays a crucial role in gut-brain-axis communication. Finally, a comparative metagenomics evaluation of the gut microbiome highlighted that blood-feeding enriches the family members of the Morganellaceae and Pseudomonadaceae bacterial communities. The notable observation of a rapid proliferation of Pseudomonas bacterial sp. and tryptophan enrichment in the gut correlates with the suppression of appetite after blood-feeding. Additionally, altered NTs dynamics of naïve and aseptic mosquitoes provide further evidence that gut-endosymbionts are key modulators for the synthesis of major neuroactive molecules. Our data establish a new conceptual understanding of microbiome-gut-brain-axis communication in mosquitoes.

Human genetic factors associated with pneumonia risk, a cue for COVID-19 susceptibility.

Pneumonia, an acute respiratory tract infection, is one of the major causes of mortality worldwide. Depending on the site of acquisition, pneumonia can be community acquired pneumonia (CAP) or nosocomial pneumonia (NP). The risk of pneumonia, is partially driven by host genetics. CYP1A1 is a widely studied pulmonary CYP family gene primarily expressed in peripheral airway epithelium. The CYP1A1 genetic variants, included in this study, alter the gene activity and are known to contribute in lung inflammation, which may cause pneumonia pathogenesis. In this study, we performed a meta-analysis to establish the possible contribution of CYP1A1 gene, and its three variants (rs2606345, rs1048943 and rs4646903) towards the genetic etiology of pneumonia risk. Using PRISMA guidelines, we systematically reviewed and meta-analysed case-control studies, evaluating risk of pneumonia in patients carrying the risk alleles of CYP1A1 variants. Heterogeneity across the studies was evaluated using I2 statistics. Based on heterogeneity, a random-effect (using maximum likelihood) or fixed-effect (using inverse variance) model was applied to estimate the effect size. Pooled odds ratio (OR) was calculated to estimate the overall effect of the risk allele association with pneumonia susceptibility. Egger's regression test and funnel plot were used to assess publication bias. Subgroup analysis was performed based on pneumonia type (CAP and NP), population, as well as age group. A total of ten articles were identified as eligible studies, which included 3049 cases and 2249 healthy controls. The meta-analysis findings revealed CYP1A1 variants, rs2606345 [T vs G; OR = 1.12 (0.75-1.50); p = 0.02; I2 = 84.89%], and rs1048943 [G vs T; OR = 1.19 (0.76-1.61); p = 0.02; I2 = 0.00%] as risk markers whereas rs4646903 showed no statistical significance for susceptibility to pneumonia. On subgroup analysis, both the genetic variants showed significant association with CAP but not with NP. We additionally performed a spatial analysis to identify the key factors possibly explaining the variability across countries in the prevalence of the coronavirus disease 2019 (COVID-19), a viral pneumonia. We observed a significant association between the risk allele of rs2606345 and rs1048943, with a higher COVID-19 prevalence worldwide, providing us important links in understanding the variability in COVID-19 prevalence.

Application of explainable artificial intelligence in the identification of Squamous Cell Carcinoma biomarkers.

Non-melanoma skin cancers (NMSCs) are the fifth most common type of cancer worldwide, affecting both men and women. Each year, more than a million new occurrences of NMSC are estimated, with Squamous Cell Carcinoma (SCC) representing approximately 20% of all skin malignancies. The purpose of this study was to find potential diagnostic biomarkers for SCC by application of eXplainable Artificial Intelligence (XAI) on XGBoost machine learning (ML) models trained on binary classification datasets comprising the expression data of 40 SCC, 38 AK, and 46 normal healthy skin samples. After successfully incorporating SHAP values into the ML models, 23 significant genes were identified and were found to be associated with the progression of SCC. These identified genes may serve as diagnostic and prognostic biomarkers in patients with SCC.

Polymorphisms at site 469 of B-RAF protein associated with skin melanoma may be correlated with dabrafenib resistance: An in silico study.

Melanoma is a type of skin cancer. Numerous genes and their proteins are strongly associated with melanoma susceptibility. This study aims to use an in silico method to identify genetic variants in the melanoma susceptibility gene. The COSMIC database was queried for genes and cross-referenced with three environment-gene interaction databases (EGP, SeattleSNPs and CTD) to identify shared genes. The majority of approved skin melanoma drugs were found to act on the protein serine/threonine-protein kinase (B-RAF) encoded by the BRAF gene, which was also present in all three referenced databases. Comprehensive computational analysis was performed to predict deleterious genetic variants associated with skin melanoma, and the nsSNPs G469V and G469E were prioritized based on their predicted deleterious effects. Molecular dynamic simulation analysis of the B-RAF protein mutants G469V and G469E reveals that variations in the amino acid conformation at the drug binding site result in inconsistency in drug interaction. Additionally, this analysis showed that the G469V and G469E mutants have lower binding energy for dabrafenib than the wild type. The population with the highest frequency of each deleterious and pathogenic variant has been determined. The study's findings would support the development of more effective treatment strategies for skin melanoma. Communicated by Ramaswamy H. Sarma.

MicroRNA-128 inhibits mitochondrial biogenesis and function via targeting PGC1α and NDUFS4.

The size and morphology of mitochondria are very heterogeneous and correlates well with their healthy functioning. In many pathological conditions, mitochondrial morphology is altered due to impaired mitochondrial dynamics (a collective term for mitochondrial fusion and fission) and dysfunction. The current study aimed at identifying the role of microRNA-128 (miR-128) in regulating mitochondrial biogenesis. Previously, peroxisome proliferator activator receptor γ coactivator 1α (PGC1α) has been shown to co-activate key intermediates of mitochondrial biogenesis, function, and dynamics; however, the upstream regulatory network remains largely unknown. We, herein using in silico analysis followed by in vitro experiments in C2C12 myoblasts, showed that miR-128 reduces mitochondrial biogenesis by directly targeting PGC1α. The expression of downstream genes, nuclear respiratory factors 1 and 2 (NRF1 and NRF2, respectively), and mitochondrial transcription factor A (TFAM) were decreased in C2C12 myoblasts upon overexpression of miR-128. Also, miR-128 is shown to promote mitochondrial dysfunction by directly targeting NADH Dehydrogenase (Ubiquinone) Fe-S Protein 4 (NDUFS4). The mitochondrial dynamics and morphology were impaired post miR-128 overexpression, as revealed by downregulation of fusion proteins (mitofusin1 and 2, i.e., MFN1 and MFN2, respectively) and upregulation of fission protein (dynamin-related protein 1, i.e., DRP1). Conversely, inhibition of miR-128 expression improved mitochondrial biogenesis, function, and dynamics, as evidenced by increased mitochondrial mass and ATP production after antimiR-128 treatment. Our findings reveal that inhibition of miR-128 can be a new potential target for reversing the effects of metabolic disorders of skeletal muscle as observed during many pathophysiological conditions such as obesity and type II diabetes.

A hitchhiker's guide to single-cell transcriptomics and data analysis pipelines.

Single-cell transcriptomics (SCT) is a tour de force in the era of big omics data that has led to the accumulation of massive cellular transcription data at an astounding resolution of single cells. It provides valuable insights into cells previously unachieved by bulk cell analysis and is proving crucial in uncovering cellular heterogeneity, identifying rare cell populations, distinct cell-lineage trajectories, and mechanisms involved in complex cellular processes. SCT data is highly complex and necessitates advanced statistical and computational methods for analysis. This review provides a comprehensive overview of the steps in a typical SCT workflow, starting from experimental protocol to data analysis, deliberating various pipelines used. We discuss recent trends, challenges, machine learning methods for data analysis, and future prospects. We conclude by listing the multitude of scRNA-seq data applications and how it shall revolutionize our understanding of cellular biology and diseases.

Insights into Structural Modifications of Valproic Acid and Their Pharmacological Profile.

Valproic acid (VPA) is a well-established anticonvulsant drug discovered serendipitously and marketed for the treatment of epilepsy, migraine, bipolar disorder and neuropathic pain. Apart from this, VPA has potential therapeutic applications in other central nervous system (CNS) disorders and in various cancer types. Since the discovery of its anticonvulsant activity, substantial efforts have been made to develop structural analogues and derivatives in an attempt to increase potency and decrease adverse side effects, the most significant being teratogenicity and hepatotoxicity. Most of these compounds have shown reduced toxicity with improved potency. The simple structure of VPA offers a great advantage to its modification. This review briefly discusses the pharmacology and molecular targets of VPA. The article then elaborates on the structural modifications in VPA including amide-derivatives, acid and cyclic analogues, urea derivatives and pro-drugs, and compares their pharmacological profile with that of the parent molecule. The current challenges for the clinical use of these derivatives are also discussed. The review is expected to provide necessary knowledgebase for the further development of VPA-derived compounds.

Corrigendum: Global Text Mining and Development of Pharmacogenomic Knowledge Resource for Precision Medicine.

[This corrects the article DOI: 10.3389/fphar.2019.00839.].

Genetic Landscape of Common Epilepsies: Advancing towards Precision in Treatment.

Epilepsy, a neurological disease characterized by recurrent seizures, is highly heterogeneous in nature. Based on the prevalence, epilepsy is classified into two types: common and rare epilepsies. Common epilepsies affecting nearly 95% people with epilepsy, comprise generalized epilepsy which encompass idiopathic generalized epilepsy like childhood absence epilepsy, juvenile myoclonic epilepsy, juvenile absence epilepsy and epilepsy with generalized tonic-clonic seizure on awakening and focal epilepsy like temporal lobe epilepsy and cryptogenic focal epilepsy. In 70% of the epilepsy cases, genetic factors are responsible either as single genetic variant in rare epilepsies or multiple genetic variants acting along with different environmental factors as in common epilepsies. Genetic testing and precision treatment have been developed for a few rare epilepsies and is lacking for common epilepsies due to their complex nature of inheritance. Precision medicine for common epilepsies require a panoramic approach that incorporates polygenic background and other non-genetic factors like microbiome, diet, age at disease onset, optimal time for treatment and other lifestyle factors which influence seizure threshold. This review aims to comprehensively present a state-of-art review of all the genes and their genetic variants that are associated with all common epilepsy subtypes. It also encompasses the basis of these genes in the epileptogenesis. Here, we discussed the current status of the common epilepsy genetics and address the clinical application so far on evidence-based markers in prognosis, diagnosis, and treatment management. In addition, we assessed the diagnostic predictability of a few genetic markers used for disease risk prediction in individuals. A combination of deeper endo-phenotyping including pharmaco-response data, electro-clinical imaging, and other clinical measurements along with genetics may be used to diagnose common epilepsies and this marks a step ahead in precision medicine in common epilepsies management.

Predicting MicroRNA Sequence Using CNN and LSTM Stacked in Seq2Seq Architecture.

CNN and LSTM have proven their ability in feature extraction and natural language processing, respectively. So, we tried to use their ability to process the language of RNAs, i.e., predicting sequence of microRNAs using the sequence of mRNA. The idea is to extract the features from sequence of mRNA using CNN and use LSTM network for prediction of miRNA. The model has learned the basic features such as seed match at first 2-8 nucleotides starting at the 5' end and counting toward the 3' end. Also, it was able to predict G-U wobble base pair in seed region. While validating on experimentally validated data, the model was able to predict on average 72 percent of miRNAs for specific mRNA and shows highest positive expression fold change of predicted targets on a microarray data generated using anti 25 miRNAs compare to other predicted tools. Codes are available at https://github.com/rajkumar1501/sequence-prediction-using-CNN-and-LSTMs.