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“Machine Learning” “Precision Medicine”

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https://read.qxmd.com/read/30758596/-model-based-treatment-in-surgery
#1
REVIEW
T Vogel, N Kohn, D Ostler, N Marahrens, N Samm, A Jell, M Kranzfelder, D Wilhelm, H Friess, H Feußner
BACKGROUND: The "magic triangle" in surgery and other disciplines consists of the demand for increasingly gentler forms of treatment, simultaneous cost reduction and the fundamental primacy of improving the quality of results. The digitalization of medicine offers a promising opportunity to do justice to this, also in the sense of "Surgery 4.0". The aim is to create a cognitive, collaborative diagnostics and treatment environment to support the surgeon. METHODS: In the sense of a "theory building" for analysis and planning, process modeling is the cornerstone for modern treatment planning...
February 13, 2019: Der Chirurg; Zeitschrift Für Alle Gebiete der Operativen Medizen
https://read.qxmd.com/read/30705340/multi-channel-3d-deep-feature-learning-for-survival-time-prediction-of-brain-tumor-patients-using-multi-modal-neuroimages
#2
Dong Nie, Junfeng Lu, Han Zhang, Ehsan Adeli, Jun Wang, Zhengda Yu, LuYan Liu, Qian Wang, Jinsong Wu, Dinggang Shen
High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative prognosis for this cohort can lead to better treatment planning. Conventional survival prediction based on clinical information is subjective and could be inaccurate. Recent radiomics studies have shown better prognosis by using carefully-engineered image features from magnetic resonance images (MRI). However, feature engineering is usually time consuming, laborious and subjective. Most importantly, the engineered features cannot effectively encode other predictive but implicit information provided by multi-modal neuroimages...
January 31, 2019: Scientific Reports
https://read.qxmd.com/read/30696086/machine-learning-and-integrative-analysis-of-biomedical-big-data
#3
REVIEW
Bilal Mirza, Wei Wang, Jie Wang, Howard Choi, Neo Christopher Chung, Peipei Ping
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies...
January 28, 2019: Genes
https://read.qxmd.com/read/30689846/overview-of-the-biocreative-vi-precision-medicine-track-mining-protein-interactions-and-mutations-for-precision-medicine
#4
Rezarta Islamaj Dogan, Sun Kim, Andrew Chatr-Aryamontri, Chih-Hsuan Wei, Donald C Comeau, Rui Antunes, Sérgio Matos, Qingyu Chen, Aparna Elangovan, Nagesh C Panyam, Karin Verspoor, Hongfang Liu, Yanshan Wang, Zhuang Liu, Berna Altinel, Zehra Melce Hüsünbeyi, Arzucan Özgür, Aris Fergadis, Chen-Kai Wang, Hong-Jie Dai, Tung Tran, Ramakanth Kavuluru, Ling Luo, Albert Steppi, Jinfeng Zhang, Jinchan Qu, Zhiyong Lu
The Precision Medicine Initiative is a multicenter effort aiming at formulating personalized treatments leveraging on individual patient data (clinical, genome sequence and functional genomic data) together with the information in large knowledge bases (KBs) that integrate genome annotation, disease association studies, electronic health records and other data types. The biomedical literature provides a rich foundation for populating these KBs, reporting genetic and molecular interactions that provide the scaffold for the cellular regulatory systems and detailing the influence of genetic variants in these interactions...
January 1, 2019: Database: the Journal of Biological Databases and Curation
https://read.qxmd.com/read/30676950/lung-and-pancreatic-tumor-characterization-in-the-deep-learning-era-novel-supervised-and-unsupervised-learning-approaches
#5
Sarfaraz Hussein, Pujan Kandel, Candice W Bolan, Michael B Wallace, Ulas Bagci
Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computeraided diagnosis (CAD) tools. Tumor characterization through such tools can also enable non-invasive cancer staging, prognosis, and foster personalized treatment planning as a part of precision medicine. In this study, we propose both supervised and unsupervised machine learning strategies to improve tumor characterization. Our first approach is based on supervised learning for which we demonstrate significant gains with deep learning algorithms, particularly by utilizing a 3D Convolutional Neural Network and Transfer Learning...
January 23, 2019: IEEE Transactions on Medical Imaging
https://read.qxmd.com/read/30671672/translating-cancer-genomics-into-precision-medicine-with-artificial-intelligence-applications-challenges-and-future-perspectives
#6
REVIEW
Jia Xu, Pengwei Yang, Shang Xue, Bhuvan Sharma, Marta Sanchez-Martin, Fang Wang, Kirk A Beaty, Elinor Dehan, Baiju Parikh
In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine...
January 22, 2019: Human Genetics
https://read.qxmd.com/read/30652605/panoply-omics-guided-drug-prioritization-method-tailored-to-an-individual-patient
#7
Krishna R Kalari, Jason P Sinnwell, Kevin J Thompson, Xiaojia Tang, Erin E Carlson, Jia Yu, Peter T Vedell, James N Ingle, Richard M Weinshilboum, Judy C Boughey, Liewei Wang, Matthew P Goetz, Vera Suman
PURPOSE: The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory), to identify and prioritize drug targets and cancer therapy regimens. MATERIALS AND METHODS: The PANOPLY approach integrates clinical data with germline and somatic features obtained from multiomics platforms and applies machine learning and network analysis approaches in the context of the individual patient and matched controls...
December 2018: JCO Clinical Cancer Informatics
https://read.qxmd.com/read/30643192/circulating-cell-free-dna-in-breast-cancer-size-profiling-levels-and-methylation-patterns-lead-to-prognostic-and-predictive-classifiers
#8
Maria Panagopoulou, Makrina Karaglani, Ioanna Balgkouranidou, Eirini Biziota, Triantafillia Koukaki, Evaggelos Karamitrousis, Evangelia Nena, Ioannis Tsamardinos, George Kolios, Evi Lianidou, Stylianos Kakolyris, Ekaterini Chatzaki
Blood circulating cell-free DNA (ccfDNA) is a suggested biosource of valuable clinical information for cancer, meeting the need for a minimally-invasive advancement in the route of precision medicine. In this paper, we evaluated the prognostic and predictive potential of ccfDNA parameters in early and advanced breast cancer. Groups consisted of 150 and 16 breast cancer patients under adjuvant and neoadjuvant therapy respectively, 34 patients with metastatic disease and 35 healthy volunteers. Direct quantification of ccfDNA in plasma revealed elevated concentrations correlated to the incidence of death, shorter PFS, and non-response to pharmacotherapy in the metastatic but not in the other groups...
January 14, 2019: Oncogene
https://read.qxmd.com/read/30628494/deep-learning-for-image-analysis-personalizing-medicine-closer-to-the-point-of-care
#9
Quin Xie, Kevin Faust, Randy Van Ommeren, Adeel Sheikh, Ugljesa Djuric, Phedias Diamandis
The precision-based revolution in medicine continues to demand stratification of patients into smaller and more personalized subgroups. While genomic technologies have largely led this movement, diagnostic results can take days to weeks to generate. Management at, or closer to, the point of care still heavily relies on the subjective qualitative interpretation of clinical and diagnostic imaging findings. New and emerging technological advances in artificial intelligence (AI) now appear poised to help bring objectivity and precision to these traditionally qualitative analytic tools...
January 10, 2019: Critical Reviews in Clinical Laboratory Sciences
https://read.qxmd.com/read/30576487/potent-pairing-ensemble-of-long-short-term-memory-networks-and-support-vector-machine-for-chemical-protein-relation-extraction
#10
Farrokh Mehryary, Jari Björne, Tapio Salakoski, Filip Ginter
Biomedical researchers regularly discover new interactions between chemical compounds/drugs and genes/proteins, and report them in research literature. Having knowledge about these interactions is crucially important in many research areas such as precision medicine and drug discovery. The BioCreative VI Task 5 (CHEMPROT) challenge promotes the development and evaluation of computer systems that can automatically recognize and extract statements of such interactions from biomedical literature. We participated in this challenge with a Support Vector Machine (SVM) system and a deep learning-based system (ST-ANN), and achieved an F-score of 60...
January 1, 2018: Database: the Journal of Biological Databases and Curation
https://read.qxmd.com/read/30567402/blood-based-biomarkers-for-predicting-the-risk-for-five-year-incident-coronary-heart-disease-in-the-framingham-heart-study-via-machine-learning
#11
Meeshanthini V Dogan, Steven R H Beach, Ronald L Simons, Amaury Lendasse, Brandan Penaluna, Robert A Philibert
An improved approach for predicting the risk for incident coronary heart disease (CHD) could lead to substantial improvements in cardiovascular health. Previously, we have shown that genetic and epigenetic loci could predict CHD status more sensitively than conventional risk factors. Herein, we examine whether similar machine learning approaches could be used to develop a similar panel for predicting incident CHD. Training and test sets consisted of 1180 and 524 individuals, respectively. Data mining techniques were employed to mine for predictive biosignatures in the training set...
December 18, 2018: Genes
https://read.qxmd.com/read/30548534/integrating-molecular-networks-with-genetic-variant-interpretation-for-precision-medicine
#12
REVIEW
Emidio Capriotti, Kivilcim Ozturk, Hannah Carter
More reliable and cheaper sequencing technologies have revealed the vast mutational landscapes characteristic of many phenotypes. The analysis of such genetic variants has led to successful identification of altered proteins underlying many Mendelian disorders. Nevertheless the simple one-variant one-phenotype model valid for many monogenic diseases does not capture the complexity of polygenic traits and disorders. Although experimental and computational approaches have improved detection of functionally deleterious variants and important interactions between gene products, the development of comprehensive models relating genotype and phenotypes remains a challenge in the field of genomic medicine...
December 12, 2018: Wiley Interdisciplinary Reviews. Systems Biology and Medicine
https://read.qxmd.com/read/30531069/opportunities-and-challenges-for-developing-closed-loop-bioelectronic-medicines
#13
REVIEW
Patrick D Ganzer, Gaurav Sharma
The peripheral nervous system plays a major role in the maintenance of our physiology. Several peripheral nerves intimately regulate the state of the brain, spinal cord, and visceral systems. A new class of therapeutics, called bioelectronic medicines, are being developed to precisely regulate physiology and treat dysfunction using peripheral nerve stimulation. In this review, we first discuss new work using closed-loop bioelectronic medicine to treat upper limb paralysis. In contrast to open-loop bioelectronic medicines, closed-loop approaches trigger 'on demand' peripheral nerve stimulation due to a change in function (e...
January 2019: Neural Regeneration Research
https://read.qxmd.com/read/30523334/machine-learning-based-patient-specific-prediction-models-for-knee-osteoarthritis
#14
REVIEW
Afshin Jamshidi, Jean-Pierre Pelletier, Johanne Martel-Pelletier
Osteoarthritis (OA) is an extremely common musculoskeletal disease. However, current guidelines are not well suited for diagnosing patients in the early stages of disease and do not discriminate patients for whom the disease might progress rapidly. The most important hurdle in OA management is identifying and classifying patients who will benefit most from treatment. Further efforts are needed in patient subgrouping and developing prediction models. Conventional statistical modelling approaches exist; however, these models are limited in the amount of information they can adequately process...
January 2019: Nature Reviews. Rheumatology
https://read.qxmd.com/read/30511518/-precision-screening-and-treatment-of-human-papilloma-virus-related-cervical-cancer
#15
Zheng Hu, Ding Ma
Cervical cancer is a complex disease caused by both genetic susceptibility and environmental factors. Inherited genomic variance, high-risk human papilloma virus (HPV) infection/integration, genome methylation and somatic mutation could all constitute one machine learning model, laying the ground for molecular classification and the precision medicine of cervical cancer. Therefore, for cervical screening, next generation sequencing (NGS)-based HPV DNA and other molecular tests as well as dynamic machine learning models would accurately predict patients with potential to develop the cancer, thereby reducing the burden of repeated screening...
February 25, 2018: Zhejiang da Xue Xue Bao. Yi Xue Ban, Journal of Zhejiang University. Medical Sciences
https://read.qxmd.com/read/30510440/precision-pharmacotherapy-psychiatry-s-future-direction-in-preventing-diagnosing-and-treating-mental-disorders
#16
REVIEW
Andreas Menke
Mental disorders account for around one-third of disability worldwide and cause enormous personal and societal burden. Current pharmacotherapies and nonpharmacotherapies do help many patients, but there are still high rates of partial or no response, delayed effect, and unfavorable adverse effects. The current diagnostic taxonomy of mental disorders by the Diagnostic and Statistical Manual of Mental Disorders and the International Classification of Diseases relies on presenting signs and symptoms, but does not reflect evidence from neurobiological and behavioral systems...
2018: Pharmacogenomics and Personalized Medicine
https://read.qxmd.com/read/30472716/machine-learning-and-imaging-informatics-in-oncology
#17
REVIEW
Huan-Hsin Tseng, Lise Wei, Sunan Cui, Yi Luo, Randall K Ten Haken, Issam El Naqa
In the era of personalized and precision medicine, informatics technologies utilizing machine learning (ML) and quantitative imaging are witnessing a rapidly increasing role in medicine in general and in oncology in particular. This expanding role ranges from computer-aided diagnosis to decision support of treatments with the potential to transform the current landscape of cancer management. In this review, we aim to provide an overview of ML methodologies and imaging informatics techniques and their recent application in modern oncology...
November 23, 2018: Oncology
https://read.qxmd.com/read/30472499/human-skeletal-muscle-cell-atlas-unraveling-cellular-secrets-utilizing-muscle-on-a-chip-differential-expansion-microscopy-mass-spectrometry-nanothermometry-and-machine-learning
#18
Bhanu P Jena, Domenico L Gatti, Suzan Arslanturk, Sebastian Pernal, Douglas J Taatjes
The 'Human Cell Atlas' project has been launched to obtain a comprehensive understanding of all cell types, the fundamental living units that constitute the human body. This is a global partnership and effort involving experts from many disciplines, from computer science, engineering to medicine, and is supported by several private and public organizations, among them, the Chan Zuckerberg Foundation, the National Institutes of Health, and Google, that will greatly benefit humanity. Nearly 37 trillion cells of various shapes, sizes, and composition, are precisely organized to constitute the human body...
November 16, 2018: Micron: the International Research and Review Journal for Microscopy
https://read.qxmd.com/read/30470933/precision-immunoprofiling-by-image-analysis-and-artificial-intelligence
#19
REVIEW
Viktor H Koelzer, Korsuk Sirinukunwattana, Jens Rittscher, Kirsten D Mertz
Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This requirement can be met by a combination of computational pathology and artificial intelligence. Here, we critically assess computational approaches supporting the development of a standardized methodology in the assessment of immune-oncology biomarkers, such as PD-L1 and immune cell infiltrates. We examine immunoprofiling through spatial analysis of tumor-immune cell interactions and multiplexing technologies as a predictor of patient response to cancer treatment...
November 23, 2018: Virchows Archiv: An International Journal of Pathology
https://read.qxmd.com/read/30417117/multi-faceted-computational-assessment-of-risk-and-progression-in-oligodendroglioma-implicates-notch-and-pi3k-pathways
#20
Sameer H Halani, Safoora Yousefi, Jose Velazquez Vega, Michael R Rossi, Zheng Zhao, Fatemeh Amrollahi, Chad A Holder, Amelia Baxter-Stoltzfus, Jennifer Eschbacher, Brent Griffith, Jeffrey J Olson, Tao Jiang, Joseph R Yates, Charles G Eberhart, Laila M Poisson, Lee A D Cooper, Daniel J Brat
Oligodendrogliomas are diffusely infiltrative gliomas defined by IDH -mutation and co-deletion of 1p/19q. They have highly variable clinical courses, with survivals ranging from 6 months to over 20 years, but little is known regarding the pathways involved with their progression or optimal markers for stratifying risk. We utilized machine-learning approaches with genomic data from The Cancer Genome Atlas to objectively identify molecular factors associated with clinical outcomes of oligodendroglioma and extended these findings to study signaling pathways implicated in oncogenesis and clinical endpoints associated with glioma progression...
2018: NPJ Precision Oncology
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