Noninvasive, label-free image approaches to predict multimodal molecular markers in pluripotency assessment.

Manufacturing regenerative medicine requires continuous monitoring of pluripotent cell culture and quality assessment while eliminating cell destruction and contaminants. In this study, we employed a novel method to monitor the pluripotency of stem cells through image analysis, avoiding the traditionally used invasive procedures. This approach employs machine learning algorithms to analyze stem cell images to predict the expression of pluripotency markers, such as OCT4 and NANOG, without physically interacting with or harming cells.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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)).

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.

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 (baker's or budding yeast).

Elucidation of the molecular mechanisms underlying adverse reactions associated with a kinase inhibitor using systems toxicology.

Background/objectives: Targeted kinase inhibitors are an important class of agents in anticancer therapeutics, but their limited tolerability hampers their clinical performance. Identification of the molecular mechanisms underlying the development of adverse reactions will be helpful in establishing a rational method for the management of clinically adverse reactions. Here, we selected sunitinib as a model and demonstrated that the molecular mechanisms underlying the adverse reactions associated with kinase inhibitors can efficiently be identified using a systems toxicological approach.

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+).

Modeling and simulation using CellDesigner.

In silico modeling and simulation are effective means to understand how the regulatory systems function in life. In this chapter, we explain how to build a model and run the simulation using CellDesigner, adopting the standards such as SBML and SBGN.

Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.

Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools.

Harnessing diversity towards the reconstructing of large scale gene regulatory networks.

Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs.