Biomedical Data Classification with Improvised Deep Learning Architectures

With the rise of very powerful hardware and evolution of deep learning architectures, healthcare data analysis and its applications have been drastically transformed. These transformations mainly aim to aid a healthcare personnel with diagnosis and prognosis of a disease or abnormality at any given point of healthcare routine workflow. For instance, many of the cancer metastases detection depends on pathological tissue procedures and pathologist reviews. The reports of severity classification vary amongst different pathologist, which then leads to different treatment options for a patient. This labor-intensive work can lead to errors or mistreatments resulting in high cost of healthcare. With the help of machine learning and deep learning modules, some of these traditional diagnosis techniques can be improved and aid a doctor in decision making with an unbiased view. Some of such modules can help reduce the cost, shortage of an expertise, and time in identifying the disease. However, there are many other datapoints that are available with medical images, such as omics data, biomarker calculations, patient demographics and history. All these datapoints can enhance disease classification or prediction of progression with the help of machine learning/deep learning modules. However, it is very difficult to find a comprehensive dataset with all different modalities and features in healthcare setting due to privacy regulations. Hence in this thesis, we explore both medical imaging data with clinical datapoints as well as genomics datasets separately for classification tasks using combinational deep learning architectures. We use deep neural networks with 3D volumetric structural magnetic resonance images of Alzheimer Disease dataset for classification of disease. A separate study is implemented to understand classification based on clinical datapoints achieved by machine learning algorithms. For bioinformatics applications, sequence classification task is a crucial step for many metagenomics applications, however, requires a lot of preprocessing that requires sequence assembly or sequence alignment before making use of raw whole genome sequencing data, hence time consuming especially in bacterial taxonomy classification. There are only a few approaches for sequence classification tasks that mainly involve some convolutions and deep neural network. A novel method is developed using an intrinsic nature of recurrent neural networks for 16s rRNA sequence classification which can be adapted to utilize read sequences directly. For this classification task, the accuracy is improved using optimization techniques with a hybrid neural network

An exploratory study of consumer demeanor towards financial investment

Investment is the employment of fund on assets with the aim of earning income or capital appreciation. Investments have become a basic necessity for everyone. In India there is a rapid growth in investment. This is why an understanding of consumer demeanor for financial investment is vital to the success of the business. The review paper covers the various financial avenues like equity/stocks, bank fixed deposits, kisan vikas patra, national savings certificate, life insurance, mutual fund and discusses the factors influencing investment decision process. The prime factors affecting the financial investment behavior are demographic factors and socio-economic factors. They can further be segregated as age, income, qualification, gender, social class, family income, tax benefits, safety of fund, brand perception, risk appetite, past performance, return on investment

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Multi-omic stratification of the missense variant and redox-sensitive cysteinome

Cysteine-directed chemoproteomic profiling methods yield high-throughput inventories of redox-sensitive and ligandable cysteine residues. They are enabling techniques for functional biology. Due to their nucleophilicity and sensitivity to alkylation, cysteines have emerged as attractive sites to target with chemical probes. Cysteine-reactive covalent compounds can access small and poorly defined binding sites and efficiently block high-affinity interactions or compete with high concentrations of endogenous biomolecules. Furthermore, cysteine is the most frequently acquired amino acid due to missense variants in cancer databases. Acquired cysteines are both driver mutations and sites targeted by precision therapies; however, despite their ubiquity, nearly all acquired cysteines remain uncharacterized. Regardless of improvements in sample preparation workflows, cysteine chemoproteomic experiments still only sample a small fraction of the human cysteinome due to biological factors such as protein abundance, restricted protein expression profiles, and technical factors such as unoptimized data analysis workflows not tailored to chemoproteomics, including database searches that do not sample the mutation-induced variant proteome. The cumbersome nature of these sample preparation workflows along with reagent costs hinder most chemoproteomics studies. In this work, we develop two new chemoproteomics platforms to enable high-throughput identification of redox sensitive and ligandable cysteines, including gain-of-cysteines. First, we tailor our single-pot, solid-phase-enhanced sample preparation (SP3) method to specifically probe the redox proteome, which showcases the utility of the SP3 platform in multistep sample-preparation workflows. Application of the SP3-Rox method to cellular proteomes identified cysteines sensitive to the oxidative stressor GSNO and cysteine oxidation state changes that occur during T cell activation. By implementing a customized workflow in the FragPipe computational pipeline, we achieve accurate MS1-based quantification, including for peptides containing multiple cysteine residues. We also present “chemoproteogenomics”, combining proteogenomics with established chemoproteomics methods to study human missense variation resulting in neo cysteine residues or mutations nearby cysteine residues. For both cancer and healthy genomes, we find that cysteine acquisition is a ubiquitous consequence of genetic variation that is further elevated in the context of decreased DNA repair. Our chemoproteogenomics platform integrates chemoproteomic, whole exome, and RNA-seq data, with a customized 2-stage false discovery rate (FDR) error controlled proteomic search enhanced with a user-friendly FragPipe interface to improve coverage of acquired cysteine variants and proximal variants using a panel of 11 cancer cell lines. These two established pipelines allow us to extend activity-based profiling methods, including small molecule screening and redox-profiling, to gain-of-cysteine variants and cysteines proximal to variants. We expect widespread utility in guiding proteoform-specific biology and therapeutic discovery

From chemoproteomic‐detected amino acids to genomic coordinates: insights into precise multi‐omic data integration

The integration of proteomic, transcriptomic, and genetic variant annotation data will improve our understanding of genotype-phenotype associations. Due, in part, to challenges associated with accurate inter-database mapping, such multi-omic studies have not extended to chemoproteomics, a method that measures the intrinsic reactivity and potential "druggability" of nucleophilic amino acid side chains. Here, we evaluated mapping approaches to match chemoproteomic-detected cysteine and lysine residues with their genetic coordinates. Our analysis revealed that database update cycles and reliance on stable identifiers can lead to pervasive misidentification of labeled residues. Enabled by this examination of mapping strategies, we then integrated our chemoproteomics data with computational methods for predicting genetic variant pathogenicity, which revealed that codons of highly reactive cysteines are enriched for genetic variants that are predicted to be more deleterious and allowed us to identify and functionally characterize a new damaging residue in the cysteine protease caspase-8. Our study provides a roadmap for more precise inter-database mapping and points to untapped opportunities to improve the predictive power of pathogenicity scores and to advance prioritization of putative druggable sites

From chemoproteomic‐detected amino acids to genomic coordinates: insights into precise multi‐omic data integration

Abstract The integration of proteomic, transcriptomic, and genetic variant annotation data will improve our understanding of genotype–phenotype associations. Due, in part, to challenges associated with accurate inter‐database mapping, such multi‐omic studies have not extended to chemoproteomics, a method that measures the intrinsic reactivity and potential “druggability” of nucleophilic amino acid side chains. Here, we evaluated mapping approaches to match chemoproteomic‐detected cysteine and lysine residues with their genetic coordinates. Our analysis revealed that database update cycles and reliance on stable identifiers can lead to pervasive misidentification of labeled residues. Enabled by this examination of mapping strategies, we then integrated our chemoproteomics data with computational methods for predicting genetic variant pathogenicity, which revealed that codons of highly reactive cysteines are enriched for genetic variants that are predicted to be more deleterious and allowed us to identify and functionally characterize a new damaging residue in the cysteine protease caspase‐8. Our study provides a roadmap for more precise inter‐database mapping and points to untapped opportunities to improve the predictive power of pathogenicity scores and to advance prioritization of putative druggable sites

Suzuki–Miyaura cross-coupling for chemoproteomic applications

Bioorthogonal chemistry is a mainstay of chemoproteomic sample preparation workflows. While numerous transformations are now available, chemoproteomic studies still rely overwhelmingly on copper-catalyzed azide –alkyne cycloaddition (CuAAC) or \u27click\u27 chemistry. Here we demonstrate that gel-based activity-based protein profiling (ABPP) and mass-spectrometry-based chemoproteomic profiling can be conducted using Suzuki–Miyaura cross-coupling. We identify reaction conditions that proceed in complex cell lysates and find that Suzuki –Miyaura cross-coupling and CuAAC yield comparable chemoproteomic coverage. Importantly, Suzuki–Miyaura is also compatible with chemoproteomic target deconvolution, as demonstrated using structurally matched probes tailored to react with the cysteine protease caspase-8. Uniquely enabled by the observed orthogonality of palladium-catalyzed cross-coupling and CuAAC, we combine both reactions to achieve dual protein labeling