Procrustes Analysis of Indonesian Mortality Table Iv and Indonesia's Death Rate During Covid-19 Pandemic

The level of accuracy to calculate the premium is one of the main points for an actuary to determine the criteria of product which is offered by an insurance company to customers. The main reference in this accuracy is the mortality table. The last mortality table made by AAJI (Asosiasi Asuransi Jiwa Indonesia) was Mortality Table Indonesia (MTI) IV which was published in 2019. However, unexpectedly, the Covid-19 pandemic occurred in early 2020 which caused the death rate to be higher than normal situation. This study aims to compare MTI IV which was made with assumptions before the Covid-19 pandemic according to the death rate in Indonesia during the Covid-19 pandemic. This study uses secondary data, by finding the probability of death in Indonesia by calculating the death rate in Indonesia based on population data according to age group classifications obtained from BPS (Badan Pusat Statistik) Indonesia. Furthermore, both data were compared using Procrustes analysis to calculated the level of conformity. The results showed that 75.97% of the data matched MTI IV with the death rate during the pandemic. If the insurance company wants more accurate results, they can be adjusted to the Indonesian Mortality Table using data during the pandemic. If it is quite satisfied with the accuracy of 75.97%, the company can continue to use MTI IV

Anticancer drug synergy prediction in understudied tissues using transfer learning

ocaa212Objective: Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues are more understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied tissues as a way of overcoming data scarcity problems. Materials and Methods: We collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines. We developed a drug synergy prediction model based on multitask deep neural networks to integrate multimodal input and multiple output. We also utilized transfer learning from data-rich tissues to data-poor tissues. Results: We showed improved accuracy in predicting synergy in both data-rich tissues and understudied tissues. In data-rich tissue, the prediction model accuracy was 0.9577 AUROC for binarized classification task and 174.3 mean squared error for regression task. We observed that an adequate transfer learning strategy significantly increases accuracy in the understudied tissues. Conclusions: Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help to prioritize future in-vitro experiments. Code is available at https://github.com/yejinjkim/synergy-transfer.Peer reviewe

Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review

Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging for cancer diagnosis and prognosis. Despite these advancements, machine learning models face challenges stemming from limited labeled sample sizes, the intricate interplay of high-dimensionality data types, the inherent heterogeneity observed among patients and within tumors, and concerns about interpretability and consistency with existing biomedical knowledge. One approach to surmount these challenges is to integrate biomedical knowledge into data-driven models, which has proven potential to improve the accuracy, robustness, and interpretability of model results. Here, we review the state-of-the-art machine learning studies that adopted the fusion of biomedical knowledge and data, termed knowledge-informed machine learning, for cancer diagnosis and prognosis. Emphasizing the properties inherent in four primary data types including clinical, imaging, molecular, and treatment data, we highlight modeling considerations relevant to these contexts. We provide an overview of diverse forms of knowledge representation and current strategies of knowledge integration into machine learning pipelines with concrete examples. We conclude the review article by discussing future directions to advance cancer research through knowledge-informed machine learning.Comment: 41 pages, 4 figures, 2 table

Machine learning and feature selection for drug response prediction in precision oncology applications

In-depth modeling of the complex interplay among multiple omics data measured from cancer cell lines or patient tumors is providing new opportunities toward identification of tailored therapies for individual cancer patients. Supervised machine learning algorithms are increasingly being applied to the omics profiles as they enable integrative analyses among the high-dimensional data sets, as well as personalized predictions of therapy responses using multi-omics panels of response-predictive biomarkers identified through feature selection and cross-validation. However, technical variability and frequent missingness in input "big data" require the application of dedicated data preprocessing pipelines that often lead to some loss of information and compressed view of the biological signal. We describe here the state-of-the-art machine learning methods for anti-cancer drug response modeling and prediction and give our perspective on further opportunities to make better use of high-dimensional multi-omics profiles along with knowledge about cancer pathways targeted by anti-cancer compounds when predicting their phenotypic responses

QSAR model development for early stage screening of monoclonal antibody therapeutics to facilitate rapid developability

PhD ThesisMonoclonal antibodies (mAbs) and related therapeutics are highly desirable from a biopharmaceutical perspective as they are highly target specific and well tolerated within the human system. Nevertheless, several mAbs have been discontinued or withdrawn based either on their inability to demonstrate efficacy and/or due to adverse effects. With nearly 80% of drugs failing in clinical development mainly due to lack of efficacy and safety there arises an urgent need for better understanding of biological activity, affinity, pharmacology, toxicity, immunogenicity etc. thus leading to early prediction of success/failure. In this study a hybrid modelling framework was developed that enabled early stage screening of mAbs. The applicability of the experimental methods was first tested on chemical compounds to assess the assay quality following which they were used to assess potential off target adverse effects of mAbs. Furthermore, hypersensitivity reactions were assessed using Skimune™, a non-artificial human skin explants based assay for safety and efficacy assessment of novel compounds and drugs, developed by Alcyomics Ltd. The suitability of Skimune™ for assessing the immune related adverse effects of aggregated mAbs was studied where aggregation was induced using a heat stress protocol. The aggregates were characterised by protein analysis techniques such as analytical ultra-centrifugation following which the immunogenicity tested using Skimune™ assay. Numerical features (descriptors) of mAbs were identified and generated using ProtDCal, EMBOSS Pepstat software as well as amino acid scales for different. Five independent and novel X block datasets consisting of these descriptors were generated based on the physicochemical, electronic, thermodynamic, electronic and topological properties of amino acids: Domain, Window, Substructure, Single Amino Acid, and Running Sum. This study describes the development of a hybrid QSAR based model with a structured workflow and clear evaluation metrics, with several optimisation steps, that could be beneficial for broader and more generic PLS modelling. Based on the results and observation from this study, it was demonstrated incremental improvement via selection of datasets and variables help in further optimisation of these hybrid models. Furthermore, using hypersensitivity and cross reactivity as responses and physicochemical characteristics of mAbs as descriptors, the QSAR models generated for different applicability domains allow for rapid early stage screening and developability. These models were validated with external test set comprising of proprietary compounds from industrial partners, thus paving way for enhanced developability that tackles manufacturing failures as well as attrition rates.European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie actions grant agreemen

Deep Learning for Embedding and Integrating Multimodal Biomedical Data

Biomedical data is being generated in extremely high throughput and high dimension by technologies in areas ranging from single-cell genomics, proteomics, and transcriptomics (cytometry, single-cell RNA and ATAC sequencing) to neuroscience and cognition (fMRI and PET) to pharmaceuticals (drug perturbations and interactions). These new and emerging technologies and the datasets they create give an unprecedented view into the workings of their respective biological entities. However, there is a large gap between the information contained in these datasets and the insights that current machine learning methods can extract from them. This is especially the case when multiple technologies can measure the same underlying biological entity or system. By separately analyzing the same system but from different views gathered by different data modalities, patterns are left unobserved if they only emerge from the multi-dimensional joint representation of all of the modalities together. Through an interdisciplinary approach that emphasizes active collaboration with data domain experts, my research has developed models for data integration, extracting important insights through the joint analysis of varied data sources. In this thesis, I discuss models that address this task of multi-modal data integration, especially generative adversarial networks (GANs) and autoencoders (AEs). My research has been focused on using both of these models in a generative way for concrete problems in cutting-edge scientific applications rather than the exclusive focus on the generation of high-resolution natural images. The research in this thesis is united around ideas of building models that can extract new knowledge from scientific data inaccessible to currently existing methods

Recent Advances in Forensic Anthropological Methods and Research

Forensic anthropology, while still relatively in its infancy compared to other forensic science disciplines, adopts a wide array of methods from many disciplines for human skeletal identification in medico-legal and humanitarian contexts. The human skeleton is a dynamic tissue that can withstand the ravages of time given the right environment and may be the only remaining evidence left in a forensic case whether a week or decades old. Improved understanding of the intrinsic and extrinsic factors that modulate skeletal tissues allows researchers and practitioners to improve the accuracy and precision of identification methods ranging from establishing a biological profile such as estimating age-at-death, and population affinity, estimating time-since-death, using isotopes for geolocation of unidentified decedents, radiology for personal identification, histology to assess a live birth, to assessing traumatic injuries and so much more

Chemometrics and statistical analysis in raman spectroscopy-based biological investigations

As mentioned in the chapter 1, chemometrics has become an essential tool in Raman spectroscopy-based biological investigations and significantly enhanced the sensitivity of Raman spectroscopy-based detection. However, there are some open issues on applying chemometrics in Raman spectroscopy-based biological investigations. An automatic proce- dure is needed to optimize the parameters of the mathematical baseline correction. Spectral reconstruction algorithm is required to recover a fluorescence-free Raman spectrum from the two Raman spectra measured with different excitation wavelengths for the shifted-excitation Raman difference spectroscopy (SERDS) technique. Guidelines are necessary for reliable model optimization and rigorous model evaluation to ensure high accuracy and robustness in Raman spectroscopy-based biological detection. Computational methods are required to enable a trained model to successfully predict new data that is significantly different from the training data due to inter-replicate variations. These tasks were tackled in this thesis. The related investigations were related to three main topics: baseline correction, statistical modeling, and model transfer.Wie im Kapitel 1 erwähnt, ist die Chemometrie zu einem essentiellen Werkzeug für biolo- gische Untersuchungen mittels der Raman-Spektroskopie geworden und hat die Sensitivität der Raman-spektroskopischen Detektion erheblich verbessert. Es gibt jedoch einige offene Fragen, welche die Anwendung der Chemometrie in Raman-spektroskopischen Untersuchun- gen biologischer Proben betreffen. Zum Beispiel wird eine automatische Prozedur benötigt, um die Parameter einer mathematischen Basislinienkorrektur zu optimieren. Ein SERDS- Rekonstruktionsalgorithmus ist erforderlich, um ein Fluoreszenz-freies Raman-Spektrum aus den zwei Raman-Spektren zu extrahieren, welche bei der Shifted-excitation-Raman-Differenz- Spektroskopie (SERDS) gemessen werden. Des Weiteren sind Richtlinien erforderlich, welche eine zuverlässige Modelloptimierung und eine rigorose Modellevaluation erlauben. Durch diese Richtlinien wird eine hohe Genauigkeit und Robustheit der Raman-spektroskopischen Detektion biologischer Proben gewährleistet. Computergestützte Methoden sind nötig, um mit einem trainierten Modell erfolgreich neue Daten, die sich aufgrund von Inter-Replikat- Variationen signifikant von den Trainingsdaten unterscheiden, vorherzusagen. Diese vier Probleme sind Beispiele für offene Fragen in der Chemometrie und diese vier Probleme wur- den in dieser Arbeit behandelt. Die damit verbundenen Untersuchungen bezogen sich auf drei Hauptthemen: die Basislinienkorrektur, die statistische Modellierung und der Modell- transfer

Gut microbiome westernization in Hmong and Karen refugees and immigrants in the United States

University of Minnesota Ph.D. dissertation. August 2018. Major: Biomedical Informatics and Computational Biology. Advisor: Dan Knights. 1 computer file (PDF); x, 132 pages.Many United States immigrant populations develop metabolic diseases post-immigration, but the causes are not well understood. Although the microbiome plays a role in metabolic disease, there have been no studies measuring the effects of U.S. immigration on the gut microbiome. We collected stool, dietary recalls, and anthropometrics from 514 Hmong and Karen individuals living in Thailand and the U.S., including first- and second-generation immigrants and 19 Karen individuals sampled before and after immigration, as well as from 36 U.S.-born Caucasian individuals. Using 16S and deep shotgun metagenomic DNA sequencing, we found that migration from a non-Western country to the U.S. is associated with immediate loss of gut microbiome diversity and function, with U.S.-associated strains and functions displacing native strains and functions. These effects increase with duration of U.S. residence, and are compounded by obesity and across generations