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Article ; Online: Reconstruction algorithms for DNA-storage systems.

Sabary, Omer / Yucovich, Alexander / Shapira, Guy / Yaakobi, Eitan

2024 Volume 14, Issue 1, Page(s) 1951

Abstract: Motivated by DNA storage systems, this work presents the DNA reconstruction problem, in which a length-n string, is passing through the DNA-storage channel, which introduces deletion, insertion and substitution errors. This channel generates multiple ... ...

Abstract	Motivated by DNA storage systems, this work presents the DNA reconstruction problem, in which a length-n string, is passing through the DNA-storage channel, which introduces deletion, insertion and substitution errors. This channel generates multiple noisy copies of the transmitted string which are called traces. A DNA reconstruction algorithm is a mapping which receives t traces as an input and produces an estimation of the original string. The goal in the DNA reconstruction problem is to minimize the edit distance between the original string and the algorithm's estimation. In this work, we present several new algorithms for this problem. Our algorithms look globally on the entire sequence of the traces and use dynamic programming algorithms, which are used for the shortest common supersequence and the longest common subsequence problems, in order to decode the original string. Our algorithms do not require any limitations on the input and the number of traces, and more than that, they perform well even for error probabilities as high as 0.27. The algorithms have been tested on simulated data, on data from previous DNA storage experiments, and on a new synthesized dataset, and are shown to outperform previous algorithms in reconstruction accuracy.
MeSH term(s)	Algorithms ; DNA ; Motivation ; Probability ; Records
Chemical Substances	DNA (9007-49-2)
Language	English
Publishing date	2024-01-23
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-51730-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Unexpected gender differences in progressive supranuclear palsy reveal efficacy for davunetide in women.

Gozes, Illana / Shapira, Guy / Lobyntseva, Alexandra / Shomron, Noam

Translational psychiatry

2023 Volume 13, Issue 1, Page(s) 319

Abstract: Progressive supranuclear palsy (PSP) is a pure tauopathy, implicating davunetide, enhancing Tau-microtubule interaction, as an ideal drug candidate. However, pooling patient data irrespective of sex concluded no efficacy. Here, analyzing sex-dependency ... ...

Abstract	Progressive supranuclear palsy (PSP) is a pure tauopathy, implicating davunetide, enhancing Tau-microtubule interaction, as an ideal drug candidate. However, pooling patient data irrespective of sex concluded no efficacy. Here, analyzing sex-dependency in a 52 week-long- PSP clinical trial (involving over 200 patients) demonstrated clear baseline differences in brain ventricular volumes, a secondary endpoint. Dramatic baseline ventricular volume-dependent/volume increase correlations were observed in 52-week-placebo-treated females (r = 0.74, P = 2.36
MeSH term(s)	Male ; Humans ; Female ; Aged ; Supranuclear Palsy, Progressive/drug therapy ; Activities of Daily Living ; Sex Factors ; Disease Progression
Chemical Substances	davunetide (GF00K3IIWE)
Language	English
Publishing date	2023-10-16
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2609311-X
ISSN	2158-3188 ; 2158-3188
ISSN (online)	2158-3188
ISSN	2158-3188
DOI	10.1038/s41398-023-02618-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Molecule generation using transformers and policy gradient reinforcement learning.

Mazuz, Eyal / Shtar, Guy / Shapira, Bracha / Rokach, Lior

Scientific reports

2023 Volume 13, Issue 1, Page(s) 8799

Abstract: Generating novel valid molecules is often a difficult task, because the vast chemical space relies on the intuition of experienced chemists. In recent years, deep learning models have helped accelerate this process. These advanced models can also help ... ...

Abstract	Generating novel valid molecules is often a difficult task, because the vast chemical space relies on the intuition of experienced chemists. In recent years, deep learning models have helped accelerate this process. These advanced models can also help identify suitable molecules for disease treatment. In this paper, we propose Taiga, a transformer-based architecture for the generation of molecules with desired properties. Using a two-stage approach, we first treat the problem as a language modeling task of predicting the next token, using SMILES strings. Then, we use reinforcement learning to optimize molecular properties such as QED. This approach allows our model to learn the underlying rules of chemistry and more easily optimize for molecules with desired properties. Our evaluation of Taiga, which was performed with multiple datasets and tasks, shows that Taiga is comparable to, or even outperforms, state-of-the-art baselines for molecule optimization, with improvements in the QED ranging from 2 to over 20 percent. The improvement was demonstrated both on datasets containing lead molecules and random molecules. We also show that with its two stages, Taiga is capable of generating molecules with higher biological property scores than the same model without reinforcement learning.
Language	English
Publishing date	2023-05-31
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-35648-w
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Immunosuppression as a Hub for SARS-CoV-2 Mutational Drift.

Shapira, Guy / Patalon, Tal / Gazit, Sivan / Shomron, Noam

Viruses

2023 Volume 15, Issue 4

Abstract: The clinical course of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is largely determined by host factors, with a wide range of outcomes. Despite an extensive vaccination campaign and high ... ...

Abstract	The clinical course of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is largely determined by host factors, with a wide range of outcomes. Despite an extensive vaccination campaign and high rates of infection worldwide, the pandemic persists, adapting to overcome antiviral immunity acquired through prior exposure. The source of many such major adaptations is variants of concern (VOCs), novel SARS-CoV-2 variants produced by extraordinary evolutionary leaps whose origins remain mostly unknown. In this study, we tested the influence of factors on the evolutionary course of SARS-CoV-2. Electronic health records of individuals infected with SARS-CoV-2 were paired to viral whole-genome sequences to assess the effects of host clinical parameters and immunity on the intra-host evolution of SARS-CoV-2. We found slight, albeit significant, differences in SARS-CoV-2 intra-host diversity, which depended on host parameters such as vaccination status and smoking. Only one viral genome had significant alterations as a result of host parameters; it was found in an immunocompromised, chronically infected woman in her 70s. We highlight the unusual viral genome obtained from this woman, which had an accelerated mutational rate and an excess of rare mutations, including near-complete truncating of the accessory protein ORF3a. Our findings suggest that the evolutionary capacity of SARS-CoV-2 during acute infection is limited and mostly unaffected by host characteristics. Significant viral evolution is seemingly exclusive to a small subset of COVID-19 cases, which typically prolong infections in immunocompromised patients. In these rare cases, SARS-CoV-2 genomes accumulate many impactful and potentially adaptive mutations; however, the transmissibility of such viruses remains unclear.
MeSH term(s)	Humans ; Female ; SARS-CoV-2/genetics ; COVID-19 ; Mutation ; Immunosuppression Therapy ; Spike Glycoprotein, Coronavirus
Chemical Substances	Spike Glycoprotein, Coronavirus ; spike protein, SARS-CoV-2
Language	English
Publishing date	2023-03-27
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2516098-9
ISSN	1999-4915 ; 1999-4915
ISSN (online)	1999-4915
ISSN	1999-4915
DOI	10.3390/v15040855
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Single-Cell Transcriptome Profiling.

Shapira, Guy / Shomron, Noam

Methods in molecular biology (Clifton, N.J.)

2021 Volume 2243, Page(s) 311–325

Abstract: Over the last decade, single cell RNA sequencing (scRNAseq) became an increasingly viable solution for analyzing cellular heterogeneity and cell-specific expression differences. While not as mature or fully realized as bulk sequencing, newly developed ... ...

Abstract	Over the last decade, single cell RNA sequencing (scRNAseq) became an increasingly viable solution for analyzing cellular heterogeneity and cell-specific expression differences. While not as mature or fully realized as bulk sequencing, newly developed computational methods offer a solution to the challenges of scRNAseq data analysis, providing previously inaccessible biological insight at unprecedented levels of detail. Here, we go over the inherent challenges of single-cell data analysis and the computational methods used to overcome them. We cover current and future applications of scRNAseq in research of cellular dynamics and as an integrative component of biological research.
MeSH term(s)	Computational Biology/methods ; Gene Expression Profiling/methods ; Humans ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods ; Software ; Transcriptome/genetics
Language	English
Publishing date	2021-02-19
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-1103-6_16
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Molecule generation using transformers and policy gradient reinforcement learning

Eyal Mazuz / Guy Shtar / Bracha Shapira / Lior Rokach

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 11

Abstract: Abstract Generating novel valid molecules is often a difficult task, because the vast chemical space relies on the intuition of experienced chemists. In recent years, deep learning models have helped accelerate this process. These advanced models can ... ...

Abstract	Abstract Generating novel valid molecules is often a difficult task, because the vast chemical space relies on the intuition of experienced chemists. In recent years, deep learning models have helped accelerate this process. These advanced models can also help identify suitable molecules for disease treatment. In this paper, we propose Taiga, a transformer-based architecture for the generation of molecules with desired properties. Using a two-stage approach, we first treat the problem as a language modeling task of predicting the next token, using SMILES strings. Then, we use reinforcement learning to optimize molecular properties such as QED. This approach allows our model to learn the underlying rules of chemistry and more easily optimize for molecules with desired properties. Our evaluation of Taiga, which was performed with multiple datasets and tasks, shows that Taiga is comparable to, or even outperforms, state-of-the-art baselines for molecule optimization, with improvements in the QED ranging from 2 to over 20 percent. The improvement was demonstrated both on datasets containing lead molecules and random molecules. We also show that with its two stages, Taiga is capable of generating molecules with higher biological property scores than the same model without reinforcement learning.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2023-05-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Elevated Expression of

Avnat, Eden / Shapira, Guy / Gurwitz, David / Shomron, Noam

Journal of personalized medicine

2022 Volume 12, Issue 9

Abstract: Anosmia is common in COVID-19 patients, lasting for weeks or months following recovery. The biological mechanism underlying olfactory deficiency in COVID-19 does not involve direct damage to nasal olfactory neurons, which do not express the proteins ... ...

Abstract	Anosmia is common in COVID-19 patients, lasting for weeks or months following recovery. The biological mechanism underlying olfactory deficiency in COVID-19 does not involve direct damage to nasal olfactory neurons, which do not express the proteins required for SARS-CoV-2 infection. A recent study suggested that anosmia results from downregulation of olfactory receptors. We hypothesized that anosmia in COVID-19 may also reflect SARS-CoV-2 infection-driven elevated expression of regulator of G protein signaling 2 (
Language	English
Publishing date	2022-08-28
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662248-8
ISSN	2075-4426
ISSN	2075-4426
DOI	10.3390/jpm12091396
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Article ; Online: Digital Signatures with Quantum Candies.

Mor, Tal / Shapira, Roman / Shemesh, Guy

Entropy (Basel, Switzerland)

2022 Volume 24, Issue 2

Abstract: Quantum candies (qandies) represent a type of pedagogical simple model that describes many concepts from quantum information processing (QIP) intuitively without the need to understand or make use of superpositions and without the need of using complex ... ...

Abstract	Quantum candies (qandies) represent a type of pedagogical simple model that describes many concepts from quantum information processing (QIP) intuitively without the need to understand or make use of superpositions and without the need of using complex algebra. One of the topics in quantum cryptography that has gained research attention in recent years is quantum digital signatures (QDS), which involve protocols to securely sign classical bits using quantum methods. In this paper, we show how the "qandy model" can be used to describe three QDS protocols in order to provide an important and potentially practical example of the power of "superpositionless" quantum information processing for individuals without background knowledge in the field.
Language	English
Publishing date	2022-01-28
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e24020207
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A simplified similarity-based approach for drug-drug interaction prediction.

Shtar, Guy / Solomon, Adir / Mazuz, Eyal / Rokach, Lior / Shapira, Bracha

PloS one

2023 Volume 18, Issue 11, Page(s) e0293629

Abstract: Drug-drug interactions (DDIs) are a critical component of drug safety surveillance. Laboratory studies aimed at detecting DDIs are typically difficult, expensive, and time-consuming; therefore, developing in-silico methods is critical. Machine learning- ... ...

Abstract	Drug-drug interactions (DDIs) are a critical component of drug safety surveillance. Laboratory studies aimed at detecting DDIs are typically difficult, expensive, and time-consuming; therefore, developing in-silico methods is critical. Machine learning-based approaches for DDI prediction have been developed; however, in many cases, their ability to achieve high accuracy relies on data only available towards the end of the molecule lifecycle. Here, we propose a simple yet effective similarity-based method for preclinical DDI prediction where only the chemical structure is available. We test the model on new, unseen drugs. To focus on the preclinical problem setting, we conducted a retrospective analysis and tested the models on drugs that were added to a later version of the DrugBank database. We extend an existing method, adjacency matrix factorization with propagation (AMFP), to support unseen molecules by applying a new lookup mechanism to the drugs' chemical structure, lookup adjacency matrix factorization with propagation (LAMFP). We show that using an ensemble of different similarity measures improves the results. We also demonstrate that Chemprop, a message-passing neural network, can be used for DDI prediction. In computational experiments, LAMFP results in high accuracy, with an area under the receiver operating characteristic curve of 0.82 for interactions involving a new drug and an existing drug and for interactions involving only existing drugs. Moreover, LAMFP outperforms state-of-the-art, complex graph neural network DDI prediction methods.
MeSH term(s)	Retrospective Studies ; Drug Interactions ; Neural Networks, Computer ; Databases, Factual ; Machine Learning
Language	English
Publishing date	2023-11-09
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0293629
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Pretrained transformer models for predicting the withdrawal of drugs from the market.

Mazuz, Eyal / Shtar, Guy / Kutsky, Nir / Rokach, Lior / Shapira, Bracha

Bioinformatics (Oxford, England)

2023 Volume 39, Issue 8

Abstract: Motivation: The process of drug discovery is notoriously complex, costing an average of 2.6 billion dollars and taking ∼13 years to bring a new drug to the market. The success rate for new drugs is alarmingly low (around 0.0001%), and severe adverse ... ...

Abstract	Motivation: The process of drug discovery is notoriously complex, costing an average of 2.6 billion dollars and taking ∼13 years to bring a new drug to the market. The success rate for new drugs is alarmingly low (around 0.0001%), and severe adverse drug reactions (ADRs) frequently occur, some of which may even result in death. Early identification of potential ADRs is critical to improve the efficiency and safety of the drug development process. Results: In this study, we employed pretrained large language models (LLMs) to predict the likelihood of a drug being withdrawn from the market due to safety concerns. Our method achieved an area under the curve (AUC) of over 0.75 through cross-database validation, outperforming classical machine learning models and graph-based models. Notably, our pretrained LLMs successfully identified over 50% drugs that were subsequently withdrawn, when predictions were made on a subset of drugs with inconsistent labeling between the training and test sets. Availability and implementation: The code and datasets are available at https://github.com/eyalmazuz/DrugWithdrawn.
MeSH term(s)	Humans ; Area Under Curve ; Databases, Factual ; Drug Discovery ; Drug-Related Side Effects and Adverse Reactions ; Language
Language	English
Publishing date	2023-08-23
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad519
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