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Article ; Online: Artificial intelligence in clinical and genomic diagnostics

Raquel Dias / Ali Torkamani

Genome Medicine, Vol 11, Iss 1, Pp 1-

2019 Volume 12

Abstract: Abstract Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing ... ...

Abstract	Abstract Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical AI applications. In clinical diagnostics, AI-based computer vision approaches are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. In some areas, such as clinical genomics, a specific type of AI algorithm known as deep learning is used to process large and complex genomic datasets. In this review, we first summarize the main classes of problems that AI systems are well suited to solve and describe the clinical diagnostic tasks that benefit from these solutions. Next, we focus on emerging methods for specific tasks in clinical genomics, including variant calling, genome annotation and variant classification, and phenotype-to-genotype correspondence. Finally, we end with a discussion on the future potential of AI in individualized medicine applications, especially for risk prediction in common complex diseases, and the challenges, limitations, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those utilizing human genetics and genomics data.
Keywords	Medicine ; R ; Genetics ; QH426-470
Subject code	004
Language	English
Publishing date	2019-11-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Dose response of running on blood biomarkers of wellness in generally healthy individuals.

Bartek Nogal / Svetlana Vinogradova / Milena Jorge / Ali Torkamani / Paul Fabian / Gil Blander

PLoS ONE, Vol 18, Iss 11, p e

2023 Volume 0293631

Abstract: Exercise is effective toward delaying or preventing chronic disease, with a large body of evidence supporting its effectiveness. However, less is known about the specific healthspan-promoting effects of exercise on blood biomarkers in the disease-free ... ...

Abstract	Exercise is effective toward delaying or preventing chronic disease, with a large body of evidence supporting its effectiveness. However, less is known about the specific healthspan-promoting effects of exercise on blood biomarkers in the disease-free population. In this work, we examine 23,237 generally healthy individuals who self-report varying weekly running volumes and compare them to 4,428 generally healthy sedentary individuals, as well as 82 professional endurance runners. We estimate the significance of differences among blood biomarkers for groups of increasing running levels using analysis of variance (ANOVA), adjusting for age, gender, and BMI. We attempt and add insight to our observational dataset analysis via two-sample Mendelian randomization (2S-MR) using large independent datasets. We find that self-reported running volume associates with biomarker signatures of improved wellness, with some serum markers apparently being principally modified by BMI, whereas others show a dose-effect with respect to running volume. We further detect hints of sexually dimorphic serum responses in oxygen transport and hormonal traits, and we also observe a tendency toward pronounced modifications in magnesium status in professional endurance athletes. Thus, our results further characterize blood biomarkers of exercise and metabolic health, particularly regarding dose-effect relationships, and better inform personalized advice for training and performance.
Keywords	Medicine ; R ; Science ; Q
Subject code	796
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Co-Inference of Data Mislabelings Reveals Improved Models in Genomics and Breast Cancer Diagnostics

Susanne Gerber / Lukas Pospisil / Stanislav Sys / Charlotte Hewel / Ali Torkamani / Illia Horenko

Frontiers in Artificial Intelligence, Vol

2022 Volume 4

Abstract: Mislabeling of cases as well as controls in case–control studies is a frequent source of strong bias in prognostic and diagnostic tests and algorithms. Common data processing methods available to the researchers in the biomedical community do not allow ... ...

Abstract	Mislabeling of cases as well as controls in case–control studies is a frequent source of strong bias in prognostic and diagnostic tests and algorithms. Common data processing methods available to the researchers in the biomedical community do not allow for consistent and robust treatment of labeled data in the situations where both, the case and the control groups, contain a non-negligible proportion of mislabeled data instances. This is an especially prominent issue in studies regarding late-onset conditions, where individuals who may convert to cases may populate the control group, and for screening studies that often have high false-positive/-negative rates. To address this problem, we propose a method for a simultaneous robust inference of Lasso reduced discriminative models and of latent group-specific mislabeling risks, not requiring any exactly labeled data. We apply it to a standard breast cancer imaging dataset and infer the mislabeling probabilities (being rates of false-negative and false-positive core-needle biopsies) together with a small set of simple diagnostic rules, outperforming the state-of-the-art BI-RADS diagnostics on these data. The inferred mislabeling rates for breast cancer biopsies agree with the published purely empirical studies. Applying the method to human genomic data from a healthy-ageing cohort reveals a previously unreported compact combination of single-nucleotide polymorphisms that are strongly associated with a healthy-ageing phenotype for Caucasians. It determines that 7.5% of Caucasians in the 1000 Genomes dataset (selected as a control group) carry a pattern characteristic of healthy ageing.
Keywords	mislabeling ; label noise ; latent variable estimation ; bioinformatics ; bias ; regression ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	519
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: SG-ADVISER mtDNA

Manuel Rueda / Ali Torkamani

BMC Bioinformatics, Vol 18, Iss 1, Pp 1-

a web server for mitochondrial DNA annotation with data from 200 samples of a healthy aging cohort

2017 Volume 10

Abstract: Abstract Background Whole genome and exome sequencing usually include reads containing mitochondrial DNA (mtDNA). Yet, state-of-the-art pipelines and services for human nuclear genome variant calling and annotation do not handle mitochondrial genome data ...

Abstract	Abstract Background Whole genome and exome sequencing usually include reads containing mitochondrial DNA (mtDNA). Yet, state-of-the-art pipelines and services for human nuclear genome variant calling and annotation do not handle mitochondrial genome data appropriately. As a consequence, any researcher desiring to add mtDNA variant analysis to their investigations is forced to explore the literature for mtDNA pipelines, evaluate them, and implement their own instance of the desired tool. This task is far from trivial, and can be prohibitive for non-bioinformaticians. Results We have developed SG-ADVISER mtDNA, a web server to facilitate the analysis and interpretation of mtDNA genomic data coming from next generation sequencing (NGS) experiments. The server was built in the context of our SG-ADVISER framework and on top of the MtoolBox platform (Calabrese et al., Bioinformatics 30(21):3115–3117, 2014), and includes most of its functionalities (i.e., assembly of mitochondrial genomes, heteroplasmic fractions, haplogroup assignment, functional and prioritization analysis of mitochondrial variants) as well as a back-end and a front-end interface. The server has been tested with unpublished data from 200 individuals of a healthy aging cohort (Erikson et al., Cell 165(4):1002–1011, 2016) and their data is made publicly available here along with a preliminary analysis of the variants. We observed that individuals over ~90 years old carried low levels of heteroplasmic variants in their genomes. Conclusions SG-ADVISER mtDNA is a fast and functional tool that allows for variant calling and annotation of human mtDNA data coming from NGS experiments. The server was built with simplicity in mind, and builds on our own experience in interpreting mtDNA variants in the context of sudden death and rare diseases. Our objective is to provide an interface for non-bioinformaticians aiming to acquire (or contrast) mtDNA annotations via MToolBox. SG-ADVISER web server is freely available to all users at ...
Keywords	Mitochondrial DNA ; Annotation ; Healthy aging ; Heteroplasmy ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	005
Language	English
Publishing date	2017-08-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Liquid Biopsies for Cancer

Nithya Krishnamurthy / Emily Spencer / Ali Torkamani / Laura Nicholson

Journal of Clinical Medicine, Vol 6, Iss 1, p

Coming to a Patient near You

2017 Volume 3

Abstract: The use of circulating tumor DNA (ctDNA) as a novel and non-invasive test for the diagnosis and surveillance of cancer is a rapidly growing area of interest, with sequencing of ctDNA acting as a potential surrogate for tissue biopsy. Circulating tumor ... ...

Abstract	The use of circulating tumor DNA (ctDNA) as a novel and non-invasive test for the diagnosis and surveillance of cancer is a rapidly growing area of interest, with sequencing of ctDNA acting as a potential surrogate for tissue biopsy. Circulating tumor DNA has been detected incidentally during noninvasive prenatal testing and additionally in more than 75% of known cancer patients participating in ctDNA studies evaluating its sensitivity. In the setting of mutation-based targeted tumor therapy, it shows a concordance rate >80% when compared with gold-standard tissue biopsies. Through ctDNA detection and sequencing, a simple blood test becomes a liquid biopsy for cancer, surveying a patient’s entire circulation with the goal of early detection, prognostic information, personalized therapy options, and tracking for recurrence or resistance, all with fewer or no tissue biopsies. Given the recent first-ever FDA approval of a liquid biopsy, it is important for clinicians to be aware of the rapid advancements likely to bring these tests into our practices soon. Here we review the biology, clinical implications, and recent advances in circulating tumor DNA analysis.
Keywords	cancer surveillance ; genomics ; personalized medicine ; liquid biopsy ; Medicine ; R
Subject code	610
Language	English
Publishing date	2017-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Assessment of circulating copy number variant detection for cancer screening.

Bhuvan Molparia / Eshaan Nichani / Ali Torkamani

PLoS ONE, Vol 12, Iss 7, p e

2017 Volume 0180647

Abstract: Current high-sensitivity cancer screening methods, largely utilizing correlative biomarkers, suffer from false positive rates that lead to unnecessary medical procedures and debatable public health benefit overall. Detection of circulating tumor DNA ( ... ...

Abstract	Current high-sensitivity cancer screening methods, largely utilizing correlative biomarkers, suffer from false positive rates that lead to unnecessary medical procedures and debatable public health benefit overall. Detection of circulating tumor DNA (ctDNA), a causal biomarker, has the potential to revolutionize cancer screening. Thus far, the majority of ctDNA studies have focused on detection of tumor-specific point mutations after cancer diagnosis for the purpose of post-treatment surveillance. However, ctDNA point mutation detection methods developed to date likely lack either the scope or analytical sensitivity necessary to be useful for cancer screening, due to the low (<1%) ctDNA fraction derived from early stage tumors. On the other hand, tumor-derived copy number variant (CNV) detection is hypothetically a superior means of ctDNA-based cancer screening for many tumor types, given that, relative to point mutations, each individual tumor CNV contributes a much larger number of ctDNA fragments to the overall pool of circulating free DNA (cfDNA). A small number of studies have demonstrated the potential of ctDNA CNV-based screening in select cancer types. Here we perform an in silico assessment of the potential for ctDNA CNV-based cancer screening across many common cancers, and suggest ctDNA CNV detection shows promise as a broad cancer screening methodology.
Keywords	Medicine ; R ; Science ; Q
Subject code	610
Language	English
Publishing date	2017-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Identification of an N-acetylneuraminic acid-presenting bacteria isolated from a human microbiome

Zhen Han / Peter S. Thuy-Boun / Wayne Pfeiffer / Vincent F. Vartabedian / Ali Torkamani / John R. Teijaro / Dennis W. Wolan

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 12

Abstract: Abstract N-Acetylneuraminic acid is the most abundant sialic acid (SA) in humans and is expressed as the terminal sugar on intestinal mucus glycans. Several pathogenic bacteria harvest and display host SA on their own surfaces to evade Siglec-mediated ... ...

Abstract	Abstract N-Acetylneuraminic acid is the most abundant sialic acid (SA) in humans and is expressed as the terminal sugar on intestinal mucus glycans. Several pathogenic bacteria harvest and display host SA on their own surfaces to evade Siglec-mediated host immunity. While previous studies have identified bacterial enzymes associated with SA catabolism, no reported methods permit the selective labeling, tracking, and quantitation of SA-presenting microbes within complex multi-microbial systems. We combined metabolic labeling, click chemistry, 16S rRNA gene, and whole-genome sequencing to track and identify SA-presenting microbes from a cultured human fecal microbiome. We isolated a new strain of Escherichia coli that incorporates SA onto its own surface and encodes for the nanT, neuA, and neuS genes necessary for harvesting and presenting SA. Our method is applicable to the identification of SA-presenting bacteria from human, animal, and environmental microbiomes, as well as providing an entry point for the investigation of surface-expressed SA-associated structures.
Keywords	Medicine ; R ; Science ; Q
Subject code	540
Language	English
Publishing date	2021-02-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A feasibility study of colorectal cancer diagnosis via circulating tumor DNA derived CNV detection.

Bhuvan Molparia / Glenn Oliveira / Jennifer L Wagner / Emily G Spencer / Ali Torkamani

PLoS ONE, Vol 13, Iss 5, p e

2018 Volume 0196826

Abstract: Circulating tumor DNA (ctDNA) has shown great promise as a biomarker for early detection of cancer. However, due to the low abundance of ctDNA, especially at early stages, it is hard to detect at high accuracies while keeping sequencing costs low. Here ... ...

Abstract	Circulating tumor DNA (ctDNA) has shown great promise as a biomarker for early detection of cancer. However, due to the low abundance of ctDNA, especially at early stages, it is hard to detect at high accuracies while keeping sequencing costs low. Here we present a pilot stage study to detect large scale somatic copy numbers variations (CNVs), which contribute more molecules to ctDNA signal compared to point mutations, via cell free DNA sequencing. We show that it is possible to detect somatic CNVs in early stage colorectal cancer (CRC) patients and subsequently discriminate them from normal patients. With 25 normal and 24 CRC samples, we achieve 100% specificity (lower bound confidence interval: 86%) and ~79% sensitivity (95% confidence interval: 63% - 95%,), though the performance should be considered with caution given the limited sample size. We report a lack of concordance between the CNVs detected via cfDNA sequencing and CNVs identified in parent tissue samples. However, recent findings suggest that a lack of concordance is expected for CNVs in CRC because of their sub-clonal nature. Finally, the CNVs we detect very likely contribute to cancer progression as they lie in functionally important regions, and have been shown to be associated with CRC specifically. This study paves the path for a larger scale exploration of the potential of CNV detection for both diagnoses and prognoses of cancer.
Keywords	Medicine ; R ; Science ; Q
Subject code	616
Language	English
Publishing date	2018-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Ranking of non-coding pathogenic variants and putative essential regions of the human genome

Alex Wells / David Heckerman / Ali Torkamani / Li Yin / Jonathan Sebat / Bing Ren / Amalio Telenti / Julia di Iulio

Nature Communications, Vol 10, Iss 1, Pp 1-

2019 Volume 9

Abstract: Whole genome sequencing (WGS) holds promise to solve a subset of Mendelian disease cases for which exome sequencing did not provide a genetic diagnosis. Here, Wells et al. report a supervised machine learning model trained on functional, mutational and ... ...

Abstract	Whole genome sequencing (WGS) holds promise to solve a subset of Mendelian disease cases for which exome sequencing did not provide a genetic diagnosis. Here, Wells et al. report a supervised machine learning model trained on functional, mutational and structural features for rank-scoring and interpreting variants in non-coding regions from WGS.
Keywords	Science ; Q
Language	English
Publishing date	2019-11-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Ranking of non-coding pathogenic variants and putative essential regions of the human genome

Alex Wells / David Heckerman / Ali Torkamani / Li Yin / Jonathan Sebat / Bing Ren / Amalio Telenti / Julia di Iulio

Nature Communications, Vol 10, Iss 1, Pp 1-

2019 Volume 9

Abstract	Whole genome sequencing (WGS) holds promise to solve a subset of Mendelian disease cases for which exome sequencing did not provide a genetic diagnosis. Here, Wells et al. report a supervised machine learning model trained on functional, mutational and structural features for rank-scoring and interpreting variants in non-coding regions from WGS.
Keywords	Science ; Q
Language	English
Publishing date	2019-11-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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