Rosetta algorithm and 3d modelling
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In this presentation, Shanthi describes what is called the rosetta model that uses cloud computing to show 3d models of chemical compounds. Shanthi says that such visualizations help in discovery of new proteins and drugs. Her interest area lies in the same domain.
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Memory (BiLSTM) networks, since the context-dependence of long amino acid sequences. Similarly,
DSAGLSTM-DTA also utilized a self-attention mechanism in the process of protein feature extraction to
obtain comprehensive representations of proteins, in which the final hidden states for element in the batch
were weighted with the each unit output of LSTM, and the results were represented as the final feature of
proteins. Eventually, representations of drug and protein were concatenated and fed into prediction block
for final prediction. The proposed model was evaluated on different regression datasets and binary
classification datasets, and the results demonstrated that DSAGLSTM-DTA was superior to some state-ofthe-art DTA models and exhibited good generalization ability.
Statistical modeling in pharmaceutical research and development
Statistical modeling in pharmaceutical research and development , Statistical Modeling , Descriptive Versus Mechanistic Modeling , Statistical Parameters Estimation , Confidence Regions , Non Linearity at the Optimum , Sensitivity Analysis , Optimal Design , Population Modeling
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
Graphical Model and Clustering-Regression based Methods for Causal Interactio...
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex
because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast
Cancer at the early stage was to apply artificial intelligence where machines are simulated with
intelligence and programmed to think and act like a human. This allows machines to passively learn and
find a pattern, which can be used later to detect any new changes that may occur. In general, machine
learning is quite useful particularly in the medical field, which depends on complex genomic
measurements such as microarray technique and would increase the accuracy and precision of results.
With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper
treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast
Cancer diagnostic system using complex genomic analysis via microarray technology. The system will
combine two machine learning methods, K-means cluster, and linear regression.
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
Statistical modeling in pharmaceutical research and development.
Statistical modeling in pharmaceutical research and development. This modelling is used in pharmaceutical industries to overcome the challenges related to pharmaceutical formulation, to reduce cost and increase quality and speed of pharmaceutical products.
Applicationsofbioinformaticsindrugdiscoveryandprocess
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NanoTechnology3DEuropeanChemicalBulletin.pdf
Three-dimensional (3-D) printing is elevating various growth in production viewpoint both at nanoscale and macro-scales. 3-D printing is being scouted for numerous bio-pharmaceutical administration and creation of nano-medicines employing supplementary production methods and shows assurance in capability in satisfying the demands for a patient-based customized approach. The previous outcome features the accessibility of novel natural bio-materials and finely designed polymeric substances, which can be created as unique 3-D printed nano-materials for numerous bio-pharmaceutical administrations as nano-medicines. Nano-medicine is described as the utilization of nanoscience in fabricating nano-materials for various pharmaceutical utilization, comprising identification, cure, scan, stopping, and management of diseases. Nano-medicine has also displayed a huge effect in the creation and evolve an accurate drug. In contrary the "one-size-fits-all" benchmark for the traditional drug is a personalized, structured, or accurate drug considering the variation in numerous characteristics, comprising genetics and pharmacokinetics of various victims, which have exhibited better outcomes over traditional cures. This article highlights the approaches advancements in the design and development of customized-made nano-medicine employing 3-D printing science.
Advanced Systems Biology Methods in Drug Discovery
This document discusses the role and methods of systems biology in drug discovery and development. It covers key topics such as:
- The challenges of interpreting large omics data sets and how systems biology aims to integrate multi-omics data.
- Examples of how systems biology approaches like computational modeling can be used in target discovery, understanding drug mechanisms of action, predicting drug combinations, and more.
- How systems biology methods that combine experimental data with modeling are being applied across various stages of the drug development process from preclinical research to determining side effects.
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PO WER - XX LO Gdańsk - Mathematics and treatment of cancer
Mathematics and statistical modeling can provide benefits for cancer treatment. Mathematical models of cancer processes can help researchers better understand the underlying biological mechanisms. Models can also be used to simulate disease progression and optimize therapeutic procedures. For example, models of cancer invasion describe processes like tumor cell migration, growth, and degradation of tissue. Additionally, 3D mathematical models present a three-dimensional image of the genome and allow simulations to be performed, providing insights into cancer development. Overall, the synergy between mathematics and biology can lead to more accurate applications and improved cancer therapies.
IRJET - Detection of Drug Abuse using Social Media Mining
This document discusses a study that aims to detect drug abuse patterns by mining social media data, specifically from Instagram. The study scrapes Instagram posts containing drug-related hashtags and analyzes the text and images. Text analysis identifies frequent drug slang and collocations between hashtags. Posts are filtered based on hashtags to collect information. A list of drug-related hashtags is generated. The model aims to dynamically detect illegal drug posts through hashtags and learn drug user lingo to study behavioral patterns and identify other potentially involved user accounts and locations.
Application of Machine Learning in Drug Discovery and Development Lifecycle
Machine learning and Artificial Intelligence have significantly advanced in recent years owing to their potential to considerably increase the quality of life while reducing human workload. The paper demonstrates how AI and ML are used in the drug development process to shorten and enhance the overall timeline. It contains pertinent information on a variety of Machine Learning approaches and algorithms that are used across the whole drug development process to speed up research, save expenses, and reduce risks related to clinical trials. A range of QSAR analysis, hit finding, and de novo drug design applications are used in the pharmaceutical industry to enhance decision-making. As technologies like high-throughput screening and computation analysis of databases used for lead and target identification and development create and integrate vast volumes of data, machine learning and deep learning have grown in importance. It has also been emphasized how these cognitive models and tools may be used in lead creation, optimization, and thorough virtual screening. In this paper, problem statements and the corresponding state-of-the-art models have been considered for target validation, prognostic biomarkers, and digital pathology. Machine Learning models play a vital role in the various operations related to clinical trials embracing protocol optimization, participant management, data analysis and storage, clinical trial data verification, and surveillance. Post-development drug monitoring and unique industrially prevalent ML applications of pharmacovigilance have also been discussed. As a result, the goal of this study is to investigate the machine learning and deep learning algorithms utilised across the drug development lifecycle as well as the supporting techniques that have the potential to be useful.
Application of Machine Learning in Drug Discovery and Development Lifecycle
Machine learning and Artificial Intelligence have significantly advanced in recent years owing to their potential to considerably increase the quality of life while reducing human workload. The paper demonstrates how AI and ML are used in the drug development process to shorten and enhance the overall timeline. It contains pertinent information on a variety of Machine Learning approaches and algorithms that are used across the whole drug development process to speed up research, save expenses, and reduce risks related to clinical trials. A range of QSAR analysis, hit finding, and de novo drug design applications are used in the pharmaceutical industry to enhance decision-making. As technologies like high-throughput screening and computation analysis of databases used for lead and target identification and development create and integrate vast volumes of data, machine learning and deep learning have grown in importance. It has also been emphasized how these cognitive models and tools may be used in lead creation, optimization, and thorough virtual screening. In this paper, problem statements and the corresponding state-of-the-art models have been considered for target validation, prognostic biomarkers, and digital pathology. Machine Learning models play a vital role in the various operations related to clinical trials embracing protocol optimization, participant management, data analysis and storage, clinical trial data verification, and surveillance. Post-development drug monitoring and unique industrially prevalent ML applications of pharmacovigilance have also been discussed. As a result, the goal of this study is to investigate the machine learning and deep learning algorithms utilised across the drug development lifecycle as well as the supporting techniques that have the potential to be useful.
Identifying Structures in Social Conversations in NSCLC Patients through the ...
The exploration of social conversations for addressing patient’s needs is an important analytical task in which
many scholarly publications are contributing to fill the knowledge gap in this area. The main difficulty remains
the inability to turn such contributions into pragmatic processes the pharmaceutical industry can leverage in
order to generate insight from social media data, which can be considered as one of the most challenging source
of information available today due to its sheer volume and noise. This study is based on the work by Scott
Spangler and Jeffrey Kreulen and applies it to identify structure in social media through the extraction of a
topical taxonomy able to capture the latent knowledge in social conversations in health-related sites. The
mechanism for automatically identifying and generating a taxonomy from social conversations is developed and
pressured tested using public data from media sites focused on the needs of cancer patients and their families.
Moreover, a novel method for generating the category’s label and the determination of an optimal number of
categories is presented which extends Scott and Jeffrey’s research in a meaningful way. We assume the reader is
familiar with taxonomies, what they are and how they are used.
Identifying Structures in Social Conversations in NSCLC Patients through the ...
The exploration of social conversations for addressing patient’s needs is an important analytical task in which
many scholarly publications are contributing to fill the knowledge gap in this area. The main difficulty remains
the inability to turn such contributions into pragmatic processes the pharmaceutical industry can leverage in
order to generate insight from social media data, which can be considered as one of the most challenging source
of information available today due to its sheer volume and noise. This study is based on the work by Scott
Spangler and Jeffrey Kreulen and applies it to identify structure in social media through the extraction of a
topical taxonomy able to capture the latent knowledge in social conversations in health-related sites. The
mechanism for automatically identifying and generating a taxonomy from social conversations is developed and
pressured tested using public data from media sites focused on the needs of cancer patients and their families.
Moreover, a novel method for generating the category’s label and the determination of an optimal number of
categories is presented which extends Scott and Jeffrey’s research in a meaningful way. We assume the reader is
familiar with taxonomies, what they are and how they are used.
Predicting active compounds for lung cancer based on quantitative structure-a...
This document describes a study that uses machine learning models to predict active compounds for lung cancer. Specifically:
1) A dataset of molecules was collected from the ChEMBL database and divided into active and non-active groups based on inhibition concentration values. Molecular descriptors were then calculated to encode the chemical structures.
2) Two machine learning models - a neural network and gradient boosting tree classifier - were trained on the molecular descriptors to predict compound activity. Feature selection was also performed to analyze important structural features.
3) The models accurately predicted active compounds for lung cancer based on quantitative structure-activity relationships. Comparative analysis identified important chemical structures contributing to compound effectiveness.
Predicting user behavior using data profiling and hidden Markov model
Mental health disorders affect many aspects of patient’s lives, including emotions, cognition, and especially behaviors. E-health technology helps to collect information wealth in a non-invasive manner, which represents a promising opportunity to construct health behavior markers. Combining such user behavior data can provide a more comprehensive and contextual view than questionnaire data. Due to behavioral data, we can train machine learning models to understand the data pattern and also use prediction algorithms to know the next state of a person’s behavior. The remaining challenges for this issue are how to apply mathematical formulations to textual datasets and find metadata that aids to identify the person’s life pattern and also predict the next state of his comportment. The main idea of this work is to use a hidden Markov model (HMM) to predict user behavior from social media applications by analyzing and detecting states and symbols from the user behavior dataset. To achieve this goal, we need to analyze and detect the states and symbols from the user behavior dataset, then convert the textual data to mathematical and numerical matrices. Finally, apply the HMM model to predict the hidden user behavior states. We tested our program and identified that the log-likelihood was higher and better when the model fits the data. In any case, the results of the study indicated that the program was suitable for the purpose and yielded valuable data.
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Machine Learning and the Value of Health Technologies
Machine Learning and the Value of Health Technologies
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Statistical modeling in pharmaceutical research and development
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GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...
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GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...
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Statistical modeling in pharmaceutical research and development.
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Applicationsofbioinformaticsindrugdiscoveryandprocess
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Advanced Systems Biology Methods in Drug Discovery
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Machine Learning Approaches for Diabetes Classification
Machine Learning Approaches for Diabetes Classification
PO WER - XX LO Gdańsk - Mathematics and treatment of cancer
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IRJET - Detection of Drug Abuse using Social Media Mining
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Application of Machine Learning in Drug Discovery and Development Lifecycle
Application of Machine Learning in Drug Discovery and Development Lifecycle
Application of Machine Learning in Drug Discovery and Development Lifecycle
Application of Machine Learning in Drug Discovery and Development Lifecycle
Identifying Structures in Social Conversations in NSCLC Patients through the ...
Identifying Structures in Social Conversations in NSCLC Patients through the ...
Identifying Structures in Social Conversations in NSCLC Patients through the ...
Identifying Structures in Social Conversations in NSCLC Patients through the ...
Predicting active compounds for lung cancer based on quantitative structure-a...
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Presentation of the OECD Artificial Intelligence Review of Germany
Rosetta algorithm and 3d modelling
- 1. Cloud Chemicals Name: Shanthi Mathi. R E-mail: shanthimathiraj@gmail.com Twitter Id: @shanthi_nila University: Pondicherry University Year/Semester: III/VI Branch: Information Technology
- 2. Introduction There are various ways to study about the medicine and chemicals in it. But here it is a excellent way to learn it in the form of 3Dimensional view. This can be achieved by combining the two relative domains namely, Cloud Computing and Chemistry. It applies the Rosetta algorithm, which gives the protein modeling using Cloud Computing.
- 3. Introduction(Contd..) The major importance of this modelling of chemicals helps us to identify the arrangement of molecules in the chemical compound. By this method, it has been predicted that more than 2.3 billion protein models can be found. But yet, only 255 models had only been given with the original pattern. This can be improved by giving some changes in the Rosetta model.
- 4. Trends The major trend used in this modeling is Cloud Computing technology. Since it is the emerging domain in Information Technology, it paves way for the medical practitioners. To get a wide knowledge in all the medicines and their structure, the Rosetta algorithm for modeling is applied. If the person needs to access the medicine it lists out the chemicals present in it, followed by the 3Dimensional model of how the molecules are arranged in it.
- 5. Trends(Contd..) The future enhancement in this method is to give the fast chemical reactivity prediction of any desired compound. By this how much amount of chemical is to be given in terms of age ranges in human beings can be specified. This helps us to find how much the chemical is reactive in case of children to adult etc., and it is given in terms of milligrams.
- 6. Interest Areas The interesting fact about this concept in my point of view is various patterns of medicine can be found using the 3Dimensional model . It also interests me to find a new drug pattern such that it can cure a incurable disease. If selected for an intern, I would like to work on this Cloud Computing and Chemistry domain to generate a new drug and serve the society.
- 7. Thank you