DTox: A deep neural network-based in visio lens for large scale toxicogenomics data.

With the advancement of large-scale omics technologies, particularly transcriptomics data sets on drug and treatment response repositories available in public domain, toxicogenomics has emerged as a key field in safety pharmacology and chemical risk assessment. Traditional statistics-based bioinformatics analysis poses challenges in its application across multidimensional toxicogenomic data, including administration time, dosage, and gene expression levels. Motivated by the visual inspection workflow of field experts to augment their efficiency of screening significant genes to derive meaningful insights, together with the ability of deep neural architectures to learn the image signals, we developed DTox, a deep neural network-based in visio approach. Using the Percellome toxicogenomics database, instead of utilizing the numerical gene expression values of the transcripts (gene probes of the microarray) for dose-time combinations, DTox learned the image representation of 3D surface plots of distinct time and dosage data points to train the classifier on the experts' labels of gene probe significance. DTox outperformed statistical threshold-based bioinformatics and machine learning approaches based on numerical expression values. This result shows the ability of image-driven neural networks to overcome the limitations of classical numeric value-based approaches. Further, by augmenting the model with explainability modules, our study showed the potential to reveal the visual analysis process of human experts in toxicogenomics through the model weights. While the current work demonstrates the application of the DTox model in toxicogenomic studies, it can be further generalized as an in visio approach for multi-dimensional numeric data with applications in various fields in medical data sciences.

Enabling Personalization for Digital Cognitive Stimulation to Support Communication With People With Dementia: Pilot Intervention Study as a Prelude to AI Development.

BACKGROUND: Maintaining good communication and engagement between people with dementia and their caregivers is a major challenge in dementia care. Cognitive stimulation is a psychosocial intervention that supports communication and engagement, and several digital applications for cognitive stimulation have been developed. Personalization is an important factor for obtaining sustainable benefits, but the time and effort required to personalize and optimize applications often makes them difficult for routine use by nonspecialist caregivers and families. Although artificial intelligence (AI) has great potential to support automation of the personalization process, its use is largely unexplored because of the lack of suitable data from which to develop and train machine learning models. OBJECTIVE: This pilot study aims to evaluate a digital application called Aikomi in Japanese care homes for its potential to (1) create and deliver personalized cognitive stimulation programs to promote communication and engagement between people with dementia and usual care staff and (2) capture meaningful personalized data suitable for the development of AI systems. METHODS: A modular technology platform was developed and used to create personalized programs for 15 people with dementia living in 4 residential care facilities in Japan with the cooperation of a family member or care staff. A single intervention with the program was conducted with the person with dementia together with a care staff member, and for some participants, smell stimulation was provided using selected smell sticks in conjunction with the digital program. All sessions were recorded using a video camera, and the combined personalized data obtained by the platform were analyzed. RESULTS: Most people with dementia (10/15, 67%) showed high levels of engagement (>40 on Engagement of a Person with Dementia Scale), and there were no incidences of negative reactions toward the programs. Care staff reported that some participants showed extended concentration and spontaneous communication while using Aikomi, which was not their usual behavior. Smell stimulation promoted engagement for some participants even when they were unable to identify the smell. No changes in well-being were observed following the intervention according to the Mental Function Impairment Scale. The level of response to each type of content in the stimulation program varied greatly according to the person with dementia, and personalized data captured by the Aikomi platform enabled understanding of correlations between stimulation content and responses for each participant. CONCLUSIONS: This study suggests that the Aikomi digital application is acceptable for use by persons with dementia and care staff and may have the potential to promote communication and engagement. The platform captures personalized data, which can provide suitable input for machine learning. Further investigation of Aikomi will be conducted to develop AI systems and create personalized digital cognitive stimulation applications that can be easily used by nonspecialist caregivers.

A subnational reproductive, maternal, newborn, child, and adolescent health and development atlas of India.

Understanding the fine scale and subnational spatial distribution of reproductive, maternal, newborn, child, and adolescent health and development indicators is crucial for targeting and increasing the efficiency of resources for public health and development planning. National governments are committed to improve the lives of their people, lift the population out of poverty and to achieve the Sustainable Development Goals. We created an open access collection of high resolution gridded and district level health and development datasets of India using mainly the 2015-16 National Family Health Survey (NFHS-4) data, and provide estimates at higher granularity than what is available in NFHS-4, to support policies with spatially detailed data. Bayesian methods for the construction of 5 km × 5 km high resolution maps were applied for a set of indicators where the data allowed (36 datasets), while for some other indicators, only district level data were produced. All data were summarised using the India district administrative boundaries. In total, 138 high resolution and district level datasets for 28 indicators were produced and made openly available.

Impact of market-based home fortification with micronutrient powder on childhood anemia in Bangladesh: a modified stepped wedge design.

Background: Anemia poses a significant public health problem, affecting 1.6 billion people and contributing to the loss of 68.4 million disability-adjusted life years. We assessed the impact of a market-based home fortification program with micronutrient powder (MNP) called Pushtikona-5 implemented by Bangladesh Rural Advancement Committee (BRAC) on the prevalence of anemia among children aged 6-59 months in Bangladesh. Methods: We used a modified stepped wedged design and conducted three baseline, two midline, and three endline surveys to evaluate the Pushtikona-5 program implemented through three BRAC program platforms. We interviewed children's caregivers, and collected finger-prick blood samples from children to measure hemoglobin concentration. We also collected data on coverage of Pushtikona-5 and infant and young child feeding (IYCF) practices. We performed bivariate and multivariable analysis and calculated adjusted risk ratios (ARRs) to assess the effect of program outcomes. Results: A total of 16,936 households were surveyed. The prevalence of anemia was 46.6% at baseline, dropping to 32.1% at midline and 31.2% at endline. These represented adjusted relative reductions of 34% at midline (RR 0.66, 95%CI 0.62 to 0.71, value of p <0.001) and 32% at endline (RR 0.68, 95%CI 0.64 to 0.71, value of p <0.001) relative to baseline. Regarding MNP coverage, at baseline, 43.5% of caregivers surveyed had heard about MNP; 24.3% of children had ever consumed food with MNP, and only 1.8% had consumed three or more sachets in the 7 days preceding the survey. These increased to 63.0, 36.9, and 4.6%, respectively, at midline and 90.6, 68.9, and 11.5%, respectively, at endline. Conclusion: These results show evidence of a reduction in the prevalence of anemia and an improvement in coverage. This study provides important evidence of the feasibility and potential for impact of linking market-based MNP distribution with IYCF promotion through community level health workers.

Large scale text mining for deriving useful insights: A case study focused on microbiome.

Text mining has been shown to be an auxiliary but key driver for modeling, data harmonization, and interpretation in bio-medicine. Scientific literature holds a wealth of information and embodies cumulative knowledge and remains the core basis on which mechanistic pathways, molecular databases, and models are built and refined. Text mining provides the necessary tools to automatically harness the potential of text. In this study, we show the potential of large-scale text mining for deriving novel insights, with a focus on the growing field of microbiome. We first collected the complete set of abstracts relevant to the microbiome from PubMed and used our text mining and intelligence platform Taxila for analysis. We drive the usefulness of text mining using two case studies. First, we analyze the geographical distribution of research and study locations for the field of microbiome by extracting geo mentions from text. Using this analysis, we were able to draw useful insights on the state of research in microbiome w. r.t geographical distributions and economic drivers. Next, to understand the relationships between diseases, microbiome, and food which are central to the field, we construct semantic relationship networks between these different concepts central to the field of microbiome. We show how such networks can be useful to derive useful insight with no prior knowledge encoded.

Evaluating the impact of a countrywide, market-based roll-out of multiple micronutrient supplementation on low birth weight in Bangladesh: protocol for a two-arm, quasi-experimental and mixed-methods evaluation study.

INTRODUCTION: Multiple micronutrient supplementation (MMS) during pregnancy has a greater potential for reducing the risk of low birth weight (LBW) compared with the standard iron-folic acid supplementation. WHO recently included MMS on their Essential Medicines List. The Social Marketing Company (SMC) in Bangladesh is implementing a countrywide, market-based roll-out of MMS to pregnant women. We aimed to evaluate the implementation of the supplementation programme and its impact on reducing LBW. METHODS AND ANALYSIS: A two-arm, quasi-experimental and mixed-methods evaluation design will be used to evaluate the impact of this 36-month roll-out of MMS. In the intervention areas, pregnant women will purchase MMS products from the SMC's pharmacy networks. Pregnant women in comparison areas will not be exposed to this product until the end of the study. We will collect 4500 pregnant women's data on anthropometric, socioeconomic, nutrition-related and relevant programme indicators during recruitment and bimonthly follow-up until the end of their pregnancy. We will measure children's birth weight within 72 hours of birth and evaluate the changes in LBW prevalence. We will observe market-based MMS service delivery-related conditions of the pharmacies and the quality of the provider's service delivery. Concurrently, we will carry out a process evaluation to appraise the programme activities and recommend course correction. Cluster-adjusted multivariable logistic regression or log-binomial regression analysis of quantitative outcome data will be performed. For qualitative data, we will follow a thematic analysis approach. We will consolidate our study findings by triangulating the data derived from different methods. ETHICS AND DISSEMINATION: This study received ethical approval from the institutional review board of icddr,b (PR number 21001). We will recruit eligible participants after obtaining their informed written/verbal consent (and assent where needed) with full disclosure about the study. The results will be disseminated through peer-reviewed publications and conference presentations. TRIAL REGISTRATION NUMBER: NCT05108454.

Toxicity Analysis of Pentachlorophenol Data with a Bioinformatics Tool Set.

Rapid progress in technologies opened the new era of computer-leaded analytics, leaving humans more space for experimental design and decision making. Here we demonstrate the machine learning analysis workflow represented by spectral clustering, elucidation of evolutionary conserved transcription regulation, and network analysis using reverse engineering. Analysis of genes induced by the Pentachlorophenol toxic chemical revealed two subnetworks, one orchestrated by Interferon and another by Nuclear receptor factor 2 (NRF2) gene. Furthermore, network-inference based analysis identified a gene network module composed of genes associated with interferon signaling and their regulatory interaction with downstream genes, especially TRIM family proteins involved in responses of innate immune systems.

AI-driven laboratory workflows enable operation in the age of social distancing.

The COVID-19 (Coronavirus disease 2019) global pandemic has upended the normal pace of society at multiple levels-from daily activities in personal and professional lives to the way the sciences operate. Many laboratories have reported shortage in vital supplies, change in standard operating protocols, suspension of operations because of social distancing and stay-at-home guidelines during the pandemic. This global crisis has opened opportunities to leverage internet of things, connectivity, and artificial intelligence (AI) to build a connected laboratory automation platform. However, laboratory operations involve complex, multicomponent systems. It is unrealistic to completely automate the entire diversity of laboratories and processes. Recently, AI technology, particularly, game simulation has made significant strides in modeling and learning complex, multicomponent systems. Here, we present a cloud-based laboratory management and automation platform which combines multilayer information on a simulation-driven inference engine to plan and optimize laboratory operations under various constraints of COVID-19 and risk scenarios. The platform was used to assess the execution of two cell-based assays with distinct parameters in a real-life high-content screening laboratory scenario. The results show that the platform can provide a systematic framework for assessing laboratory operation scenarios under different conditions, quantifying tradeoffs, and determining the performance impact of specific resources or constraints, thereby enabling decision-making in a cost-effective manner. We envisage the laboratory management and automation platform to be further expanded by connecting it with sensors, robotic equipment, and other components of scientific operations to provide an integrated, end-to-end platform for scientific laboratory automation.

Effectiveness of community-based treatment programs for treatment of uncomplicated severe acute malnourished children aged 6-59 months using locally produced nutrient dense foods: protocol for a multicentric longitudinal quasi-experimental study.

BACKGROUND: Severe acute malnutrition (SAM) is a major underlying cause of mortality among children. Around one third of the world's acutely malnourished children live in India. The WHO recommends community-based management of acute malnutrition (CMAM) for managing children with SAM. In India, different states are implementing community-based SAM treatment programme, hereinafter called CSAM, using varieties of locally produced nutrient dense food items with different nutrient compositions. The study will assess the effectiveness of these state specific CSAM interventions. METHODS: The longitudinal quasi-experimental study will be undertaken in two purposively selected blocks of one district each in the four intervention states and one comparison state. From each state, 200 SAM children identified using weight-for-length/height z-score (WHZ) < - 3 criteria will be enrolled in the study. Their anthropometric data and skinfold thickness will be taken on admission, at sixth week and at discharge by trained field investigators. Other child details, incidence of morbidity and socio-economic details will be collected on admission. To assess food consumption pattern including consumption of locally produced nutrient dense food supplements, dietary assessment, using 24-h dietary recall will be conducted on admission, at sixth week and at discharge. In addition, body composition parameters will be assessed for a sub-set of children using bio-electrical impedance analysis on admission and at discharge to analyse changes in total body water, fat-free mass, and fat mass. Post discharge, all study participants will be followed up monthly until 6 months. Atleast 10% of the sample will be checked for quality assessment. The study's primary outcome is cure rate defined as children attaining WHZ ≥ -2. Secondary outcomes include mean weight gain, mean length of stay, body composition parameters, relapse and mortality rates. Additionally, process evaluation and cost effectiveness analysis will be conducted. DISCUSSION: There is a shortage of robust evidence regarding the effectiveness of locally produced nutrient dense food supplements provided as part of the CSAM intervention in India. This study will contribute to evidence on effective strategies to manage children with uncomplicated SAM in India. The study protocol has all necessary ethical approvals. Written informed consent will be obtained from caregivers of the children. TRIAL REGISTRATION: The study is registered with Clinical Trial Registration of India (Registration No.: CTRI/2020/09/028013 ) Date of registration 24/09/2020.

Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer's disease.

BACKGROUND: Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. METHODS: In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. RESULTS: We applied our computational framework to prioritize novel putative target genes for Alzheimer's disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). CONCLUSIONS: Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

A Geometric Clustering Tool (AGCT) to robustly unravel the inner cluster structures of time-series gene expressions.

Systems biology aims at holistically understanding the complexity of biological systems. In particular, nowadays with the broad availability of gene expression measurements, systems biology challenges the deciphering of the genetic cell machinery from them. In order to help researchers, reverse engineer the genetic cell machinery from these noisy datasets, interactive exploratory clustering methods, pipelines and gene clustering tools have to be specifically developed. Prior methods/tools for time series data, however, do not have the following four major ingredients in analytic and methodological view point: (i) principled time-series feature extraction methods, (ii) variety of manifold learning methods for capturing high-level view of the dataset, (iii) high-end automatic structure extraction, and (iv) friendliness to the biological user community. With a view to meet the requirements, we present AGCT (A Geometric Clustering Tool), a software package used to unravel the complex architecture of large-scale, non-necessarily synchronized time-series gene expression data. AGCT capture signals on exhaustive wavelet expansions of the data, which are then embedded on a low-dimensional non-linear map using manifold learning algorithms, where geometric proximity captures potential interactions. Post-processing techniques, including hard and soft information geometric clustering algorithms, facilitate the summarizing of the complete map as a smaller number of principal factors which can then be formally identified using embedded statistical inference techniques. Three-dimension interactive visualization and scenario recording over the processing helps to reproduce data analysis results without additional time. Analysis of the whole-cell Yeast Metabolic Cycle (YMC) moreover, Yeast Cell Cycle (YCC) datasets demonstrate AGCT's ability to accurately dissect all stages of metabolism and the cell cycle progression, independently of the time course and the number of patterns related to the signal. Analysis of Pentachlorophenol iduced dataset demonstrat how AGCT dissects data to identify two networks: Interferon signaling and NRF2-signaling networks.

Multi-dimensional computational pipeline for large-scale deep screening of compound effect assessment: an in silico case study on ageing-related compounds.

Designing alternative approaches to efficiently screen chemicals on the efficacy landscape is a challenging yet indispensable task in the current compound profiling methods. Particularly, increasing regulatory restrictions underscore the need to develop advanced computational pipelines for efficacy assessment of chemical compounds as alternative means to reduce and/or replace in vivo experiments. Here, we present an innovative computational pipeline for large-scale assessment of chemical compounds by analysing and clustering chemical compounds on the basis of multiple dimensions-structural similarity, binding profiles and their network effects across pathways and molecular interaction maps-to generate testable hypotheses on the pharmacological landscapes as well as identify potential mechanisms of efficacy on phenomenological processes. Further, we elucidate the application of the pipeline on a screen of anti-ageing-related compounds to cluster the candidates based on their structure, docking profile and network effects on fundamental metabolic/molecular pathways associated with the cell vitality, highlighting emergent insights on compounds activities based on the multi-dimensional deep screen pipeline.

Lenvatinib plus anti-PD-1 antibody combination treatment activates CD8+ T cells through reduction of tumor-associated macrophage and activation of the interferon pathway.

Lenvatinib is a multiple receptor tyrosine kinase inhibitor targeting mainly vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF) receptors. We investigated the immunomodulatory activities of lenvatinib in the tumor microenvironment and its mechanisms of enhanced antitumor activity when combined with a programmed cell death-1 (PD-1) blockade. Antitumor activity was examined in immunodeficient and immunocompetent mouse tumor models. Single-cell analysis, flow cytometric analysis, and immunohistochemistry were used to analyze immune cell populations and their activation. Gene co-expression network analysis and pathway analysis using RNA sequencing data were used to identify lenvatinib-driven combined activity with anti-PD-1 antibody (anti-PD-1). Lenvatinib showed potent antitumor activity in the immunocompetent tumor microenvironment compared with the immunodeficient tumor microenvironment. Antitumor activity of lenvatinib plus anti-PD-1 was greater than that of either single treatment. Flow cytometric analysis revealed that lenvatinib reduced tumor-associated macrophages (TAMs) and increased the percentage of activated CD8+ T cells secreting interferon (IFN)-γ+ and granzyme B (GzmB). Combination treatment further increased the percentage of T cells, especially CD8+ T cells, among CD45+ cells and increased IFN-γ+ and GzmB+ CD8+ T cells. Transcriptome analyses of tumors resected from treated mice showed that genes specifically regulated by the combination were significantly enriched for type-I IFN signaling. Pretreatment with lenvatinib followed by anti-PD-1 treatment induced significant antitumor activity compared with anti-PD-1 treatment alone. Our findings show that lenvatinib modulates cancer immunity in the tumor microenvironment by reducing TAMs and, when combined with PD-1 blockade, shows enhanced antitumor activity via the IFN signaling pathway. These findings provide a scientific rationale for combination therapy of lenvatinib with PD-1 blockade to improve cancer immunotherapy.

Sequence homology in eukaryotes (SHOE): interactive visual tool for promoter analysis.

BACKGROUND: Microarray and DNA-sequencing based technologies continue to produce enormous amounts of data on gene expression. This data has great potential to illuminate our understanding of biology and medicine, but the data alone is of limited value without computational tools to allow human investigators to visualize and interpret it in the context of their problem of interest. RESULTS: We created a web server called SHOE that provides an interactive, visual presentation of the available evidence of transcriptional regulation and gene co-expression to facilitate its exploration and interpretation. SHOE predicts the likely transcription factor binding sites in orthologous promoters of humans, mice, and rats using the combined information of 1) transcription factor binding preferences (position-specific scoring matrix (PSSM) libraries such as Transfac32, Jaspar, HOCOMOCO, ChIP-seq, SELEX, PBM, and iPS-reprogramming factor), 2) evolutionary conservation of putative binding sites in orthologous promoters, and 3) co-expression tendencies of gene pairs based on 1,714 normal human cells selected from the Gene Expression Omnibus Database. CONCLUSION: SHOE enables users to explore potential interactions between transcription factors and target genes via multiple data views, discover transcription factor binding motifs on top of gene co-expression, and visualize genes as a network of gene and transcription factors on its native gadget GeneViz, the CellDesigner pathway analyzer, and the Reactome database to search the pathways involved. As we demonstrate here when using the CREB1 and Nf-κB datasets, SHOE can reliably identify experimentally verified interactions and predict plausible novel ones, yielding new biological insights into the gene regulatory mechanisms involved. SHOE comes with a manual describing how to run it on a local PC or via the Garuda platform ( www.garuda-alliance.org ), where it joins other popular gadgets such as the CellDesigner pathway analyzer and the Reactome database, as part of analysis workflows to meet the growing needs of molecular biologists and medical researchers. SHOE is available from the following URL http://ec2-54-150-223-65.ap-northeast-1.compute.amazonaws.com A video demonstration of SHOE can be found here: https://www.youtube.com/watch?v=qARinNb9NtE.

Supporting evidence-based analysis for modified risk tobacco products through a toxicology data-sharing infrastructure.

The US FDA defines modified risk tobacco products (MRTPs) as products that aim to reduce harm or the risk of tobacco-related disease associated with commercially marketed tobacco products.  Establishing a product's potential as an MRTP requires scientific substantiation including toxicity studies and measures of disease risk relative to those of cigarette smoking.  Best practices encourage verification of the data from such studies through sharing and open standards. Building on the experience gained from the OpenTox project, a proof-of-concept database and website ( INTERVALS) has been developed to share results from both in vivo inhalation studies and in vitro studies conducted by Philip Morris International R&D to assess candidate MRTPs. As datasets are often generated by diverse methods and standards, they need to be traceable, curated, and the methods used well described so that knowledge can be gained using data science principles and tools. The data-management framework described here accounts for the latest standards of data sharing and research reproducibility. Curated data and methods descriptions have been prepared in ISA-Tab format and stored in a database accessible via a search portal on the INTERVALS website. The portal allows users to browse the data by study or mechanism (e.g., inflammation, oxidative stress) and obtain information relevant to study design, methods, and the most important results. Given the successful development of the initial infrastructure, the goal is to grow this initiative and establish a public repository for 21 st-century preclinical systems toxicology MRTP assessment data and results that supports open data principles.

Precise Temporal Profiling of Signaling Complexes in Primary Cells Using SWATH Mass Spectrometry.

Spatiotemporal organization of protein interactions in cell signaling is a fundamental process that drives cellular functions. Given differential protein expression across tissues and developmental stages, the architecture and dynamics of signaling interaction proteomes is, likely, highly context dependent. However, current interaction information has been almost exclusively obtained from transformed cells. In this study, we applied an advanced and robust workflow combining mouse genetics and affinity purification (AP)-SWATH mass spectrometry to profile the dynamics of 53 high-confidence protein interactions in primary T cells, using the scaffold protein GRB2 as a model. The workflow also provided a sufficient level of robustness to pinpoint differential interaction dynamics between two similar, but functionally distinct, primary T cell populations. Altogether, we demonstrated that precise and reproducible quantitative measurements of protein interaction dynamics can be achieved in primary cells isolated from mammalian tissues, allowing resolution of the tissue-specific context of cell-signaling events.

Digital health revolution: perfect storm or perfect opportunity for pharmaceutical R&D?

The convergence of technology and medicine has pushed healthcare to the brink of a major disruption that pharma has, until recently, been slow to recognize. Tech players have pioneered the emerging field of digital wellness and health, and pharma is ideally placed to use its expertise in drug development and embrace these technologies to create digital applications that address major medical needs. This review describes digital innovation from a pharma R&D perspective, outlining principal drivers, digital components, opportunities and challenges as well as a sustainable new business model predicated on empowered patients and achieving therapeutic outcomes.

A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.

BACKGROUND: The ability to sequence the transcriptomes of single cells using single-cell RNA-seq sequencing technologies presents a shift in the scientific paradigm where scientists, now, are able to concurrently investigate the complex biology of a heterogeneous population of cells, one at a time. However, till date, there has not been a suitable computational methodology for the analysis of such intricate deluge of data, in particular techniques which will aid the identification of the unique transcriptomic profiles difference between the different cellular subtypes. In this paper, we describe the novel methodology for the analysis of single-cell RNA-seq data, obtained from neocortical cells and neural progenitor cells, using machine learning algorithms (Support Vector machine (SVM) and Random Forest (RF)). RESULTS: Thirty-eight key transcripts were identified, using the SVM-based recursive feature elimination (SVM-RFE) method of feature selection, to best differentiate developing neocortical cells from neural progenitor cells in the SVM and RF classifiers built. Also, these genes possessed a higher discriminative power (enhanced prediction accuracy) as compared commonly used statistical techniques or geneset-based approaches. Further downstream network reconstruction analysis was carried out to unravel hidden general regulatory networks where novel interactions could be further validated in web-lab experimentation and be useful candidates to be targeted for the treatment of neuronal developmental diseases. CONCLUSION: This novel approach reported for is able to identify transcripts, with reported neuronal involvement, which optimally differentiate neocortical cells and neural progenitor cells. It is believed to be extensible and applicable to other single-cell RNA-seq expression profiles like that of the study of the cancer progression and treatment within a highly heterogeneous tumour.

Network analyses based on comprehensive molecular interaction maps reveal robust control structures in yeast stress response pathways.

Cellular stress responses require exquisite coordination between intracellular signaling molecules to integrate multiple stimuli and actuate specific cellular behaviors. Deciphering the web of complex interactions underlying stress responses is a key challenge in understanding robust biological systems and has the potential to lead to the discovery of targeted therapeutics for diseases triggered by dysregulation of stress response pathways. We constructed large-scale molecular interaction maps of six major stress response pathways in Saccharomyces cerevisiae (baker's or budding yeast). Biological findings from over 900 publications were converted into standardized graphical formats and integrated into a common framework. The maps are posted at http://www.yeast-maps.org/yeast-stress-response/ for browse and curation by the research community. On the basis of these maps, we undertook systematic analyses to unravel the underlying architecture of the networks. A series of network analyses revealed that yeast stress response pathways are organized in bow-tie structures, which have been proposed as universal sub-systems for robust biological regulation. Furthermore, we demonstrated a potential role for complexes in stabilizing the conserved core molecules of bow-tie structures. Specifically, complex-mediated reversible reactions, identified by network motif analyses, appeared to have an important role in buffering the concentration and activity of these core molecules. We propose complex-mediated reactions as a key mechanism mediating robust regulation of the yeast stress response. Thus, our comprehensive molecular interaction maps provide not only an integrated knowledge base, but also a platform for systematic network analyses to elucidate the underlying architecture in complex biological systems.

Oscillation of cAMP and Ca(2+) in cardiac myocytes: a systems biology approach.

Cyclic adenosine monophosphate (cAMP) and Ca(2+) levels may oscillate in harmony within excitable cells; a mathematical oscillation loop model, the Cooper model, of these oscillations was developed two decades ago. However, in that model all adenylyl cyclase (AC) isoforms were assumed to be inhibited by Ca(2+), and it is now known that the heart expresses multiple AC isoforms, among which the type 5/6 isoforms are Ca(2+)-inhibitable whereas the other five (AC2, 3, 4, 7, and 9) are not. We used a computational systems biology approach with CellDesigner simulation software to develop a comprehensive graphical map and oscillation loop model for cAMP and Ca(2+). This model indicated that Ca(2+)-mediated inhibition of AC is essential to create oscillations of Ca(2+) and cAMP, and the oscillations were not altered by incorporation of phosphodiesterase-mediated cAMP hydrolysis or PKA-mediated inhibition of AC into the model. More importantly, they were created but faded out immediately in the co-presence of Ca(2+)-noninhibitable AC isoforms. Because the subcellular locations of AC isoforms are different, spontaneous cAMP and Ca(2+) oscillations may occur within microdomains containing only Ca(2+)-inhibitable isoforms in cardiac myocytes, which might be necessary for fine tuning of excitation-contraction coupling.